This document proposes a methodology to extract information from big data sources like course handouts and directories and represent it in a graphical, ontological tree format. Keywords are extracted from documents using natural language processing techniques and used to generate a hierarchical tree based on the DMOZ open directory project. The trees provide a comprehensive overview of document content and structure. The method is implemented using Python for natural language processing and Java for visualization. Evaluation on computer science course handouts shows the trees accurately represent topic coverage and depth. Future work aims to increase the number of keywords extracted.
This document discusses the use of fuzzy queries to retrieve information from databases. Fuzzy queries allow for imprecise or vague terms to be used in queries, similar to natural language. The document first provides background on limitations of traditional database queries. It then discusses how fuzzy set theory and membership functions can be applied to queries and data to handle uncertain terms. The proposed approach applies fuzzy queries to a relational database, defining linguistic variables and membership functions. This allows information to be retrieved based on fuzzy criteria and improves the ability to query databases using human-like terms. Benefits of fuzzy queries include more natural interaction and accounting for real-world data imperfections.
This document provides an overview of information retrieval models, including vector space models, TF-IDF, Doc2Vec, and latent semantic analysis. It begins with basic concepts in information retrieval like document indexing and relevance scoring. Then it discusses vector space models and how documents and queries are represented as vectors. TF-IDF weighting is explained as assigning higher weight to rare terms. Doc2Vec is introduced as an extension of word2vec to learn document embeddings. Latent semantic analysis uses singular value decomposition to project documents to a latent semantic space. Implementation details and examples are provided for several models.
Seeds Affinity Propagation Based on Text ClusteringIJRES Journal
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
IRJET - Deep Collaborrative Filtering with Aspect InformationIRJET Journal
This document discusses a proposed system for deep collaborative filtering with aspect information. The system aims to help web users efficiently locate relevant information on unfamiliar topics to increase their knowledge. It utilizes techniques like multi-keyword search, synonym matching, and ontology mapping to return relevant web links, images, and news articles to the user based on their search terms. The proposed system architecture includes an index structure to efficiently search and rank results based on similarity to the search query terms. The implementation and evaluation of the proposed system are also discussed.
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.
A rough set based hybrid method to text categorizationNinad Samel
This document summarizes a hybrid text categorization method that combines Latent Semantic Indexing (LSI) and Rough Sets theory to reduce the dimensionality of text data and generate classification rules. It introduces LSI to reduce the feature space of text documents represented as high-dimensional vectors. Then it applies Rough Sets theory to the reduced feature space to locate a minimal set of keywords that can distinguish document classes and generate multiple knowledge bases for classification instead of a single one. The method is tested on text categorization tasks and shown to improve accuracy over previous Rough Sets approaches.
Semantic Based Model for Text Document Clustering with IdiomsWaqas Tariq
Text document clustering has become an increasingly important problem in recent years because of the tremendous amount of unstructured data which is available in various forms in online forums such as the web, social networks, and other information networks. Clustering is a very powerful data mining technique to organize the large amount of information on the web. Traditionally, document clustering methods do not consider the semantic structure of the document. This paper addresses the task of developing an effective and efficient method to improve the semantic structure of the text documents. A method has been developed that performs the following: tag the documents for parsing, replacement of idioms with their original meaning, semantic weights calculation for document words and apply semantic grammar. The similarity measure is obtained between the documents and then the documents are clustered using Hierarchical clustering algorithm. The method adopted in this work is evaluated on different data sets with standard performance measures and the effectiveness of the method to develop in meaningful clusters has been proved.
This document discusses the use of fuzzy queries to retrieve information from databases. Fuzzy queries allow for imprecise or vague terms to be used in queries, similar to natural language. The document first provides background on limitations of traditional database queries. It then discusses how fuzzy set theory and membership functions can be applied to queries and data to handle uncertain terms. The proposed approach applies fuzzy queries to a relational database, defining linguistic variables and membership functions. This allows information to be retrieved based on fuzzy criteria and improves the ability to query databases using human-like terms. Benefits of fuzzy queries include more natural interaction and accounting for real-world data imperfections.
This document provides an overview of information retrieval models, including vector space models, TF-IDF, Doc2Vec, and latent semantic analysis. It begins with basic concepts in information retrieval like document indexing and relevance scoring. Then it discusses vector space models and how documents and queries are represented as vectors. TF-IDF weighting is explained as assigning higher weight to rare terms. Doc2Vec is introduced as an extension of word2vec to learn document embeddings. Latent semantic analysis uses singular value decomposition to project documents to a latent semantic space. Implementation details and examples are provided for several models.
Seeds Affinity Propagation Based on Text ClusteringIJRES Journal
The objective is to find among all partitions of the data set, best publishing according to some quality measure. Affinity propagation is a low error, high speed, flexible, and remarkably simple clustering algorithm that may be used in forming teams of participants for business simulations and experiential exercises, and in organizing participant’s preferences for the parameters of simulations. This paper proposes an efficient Affinity Propagation algorithm that guarantees the same clustering result as the original algorithm after convergence. The heart of our approach is (1) to prune unnecessary message exchanges in the iterations and (2) to compute the convergence values of pruned messages after the iterations to determine clusters.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
IRJET - Deep Collaborrative Filtering with Aspect InformationIRJET Journal
This document discusses a proposed system for deep collaborative filtering with aspect information. The system aims to help web users efficiently locate relevant information on unfamiliar topics to increase their knowledge. It utilizes techniques like multi-keyword search, synonym matching, and ontology mapping to return relevant web links, images, and news articles to the user based on their search terms. The proposed system architecture includes an index structure to efficiently search and rank results based on similarity to the search query terms. The implementation and evaluation of the proposed system are also discussed.
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.
A rough set based hybrid method to text categorizationNinad Samel
This document summarizes a hybrid text categorization method that combines Latent Semantic Indexing (LSI) and Rough Sets theory to reduce the dimensionality of text data and generate classification rules. It introduces LSI to reduce the feature space of text documents represented as high-dimensional vectors. Then it applies Rough Sets theory to the reduced feature space to locate a minimal set of keywords that can distinguish document classes and generate multiple knowledge bases for classification instead of a single one. The method is tested on text categorization tasks and shown to improve accuracy over previous Rough Sets approaches.
Semantic Based Model for Text Document Clustering with IdiomsWaqas Tariq
Text document clustering has become an increasingly important problem in recent years because of the tremendous amount of unstructured data which is available in various forms in online forums such as the web, social networks, and other information networks. Clustering is a very powerful data mining technique to organize the large amount of information on the web. Traditionally, document clustering methods do not consider the semantic structure of the document. This paper addresses the task of developing an effective and efficient method to improve the semantic structure of the text documents. A method has been developed that performs the following: tag the documents for parsing, replacement of idioms with their original meaning, semantic weights calculation for document words and apply semantic grammar. The similarity measure is obtained between the documents and then the documents are clustered using Hierarchical clustering algorithm. The method adopted in this work is evaluated on different data sets with standard performance measures and the effectiveness of the method to develop in meaningful clusters has been proved.
Text Mining is the technique that helps users to find out useful information from a large amount of text documents on the web or database. Most popular text mining and classification methods have adopted term-based approaches. The term based approaches and the pattern-based method describing user preferences. This review paper analyse how the text mining work on the three level i.e sentence level, document level and feature level. In this paper we review the related work which is previously done. This paper also demonstrated that what are the problems arise while doing text mining done at the feature level. This paper presents the technique to text mining for the compound sentences.
This document describes a proposed concept-based mining model that aims to improve document clustering and information retrieval by extracting concepts and semantic relationships rather than just keywords. The model uses natural language processing techniques like part-of-speech tagging and parsing to extract concepts from text. It represents concepts and their relationships in a semantic network and clusters documents based on conceptual similarity rather than term frequency. The model is evaluated using singular value decomposition to increase the precision of key term and phrase extraction.
IRJET- Survey for Amazon Fine Food ReviewsIRJET Journal
This document discusses sentiment analysis and summarizes several papers on related topics. It begins with an abstract describing sentiment analysis and its importance. The introduction defines sentiment classification and analysis. The literature survey section summarizes 5 papers on natural language processing and machine learning algorithms for sentiment analysis, including K-means clustering, bag-of-words models, TF-IDF vectorization for document clustering, hierarchical clustering methods, and using naive bayes and SVM for sentiment analysis and text summarization. The conclusion discusses techniques for data processing, natural language processing, and machine learning algorithms covered.
This document provides an overview of text mining and web mining. It defines data mining and describes the common data mining tasks of classification, clustering, association rule mining and sequential pattern mining. It then discusses text mining, defining it as the process of analyzing unstructured text data to extract meaningful information and structure. The document outlines the seven practice areas of text mining as search/information retrieval, document clustering, document classification, web mining, information extraction, natural language processing, and concept extraction. It provides brief descriptions of the problems addressed within each practice area.
AN EFFICIENT APPROACH TO IMPROVE ARABIC DOCUMENTS CLUSTERING BASED ON A NEW K...csandit
Document Clustering algorithms goal is to create clusters that are coherent internally, but
clearly different from each other. The useful expressions in the documents is often accompanied
by a large amount of noise that is caused by the use of unnecessary words, so it is indispensable
to eliminate it and keeping just the useful information.
Keyphrases extraction systems in Arabic are new phenomena. A number of Text Mining
applications can use it to improve her results. The Keyphrases are defined as phrases that
capture the main topics discussed in document; they offer a brief and precise summary of
document content. Therefore, it can be a good solution to get rid of the existent noise from
documents.
In this paper, we propose a new method to solve the problem cited above especially for Arabic
language documents, which is one of the most complex languages, by using a new Keyphrases
extraction algorithm based on the Suffix Tree data structure (KpST). To evaluate our approach,
we conduct an experimental study on Arabic Documents Clustering using the most popular
approach of Hierarchical algorithms: Agglomerative Hierarchical algorithm with seven linkage
techniques and a variety of distance functions and similarity measures to perform Arabic
Document Clustering task. The obtained results show that our approach for extracting
Keyphrases improves the clustering results.
Conceptual similarity measurement algorithm for domain specific ontology[Zac Darcy
This paper presents the similarity measurement algorithm for domain specific terms collected in the
ontology based data integration system. This similarity measurement algorithm can be used in ontology
mapping and query service of
ontology based data integration sy
stem. In this paper, we focus
o
n the web
query service to apply
this proposed algorithm
. Concepts similarity is important for web query service
because the words in user input query are not
same wholly with the concepts in
ontology. So, we need to
extract the possible concepts that are match or related to the input words with the help of machine readable
dictionary WordNet. Sometimes, we use the generated mapping rules in query generation procedure for
some words that canno
t be
confirmed the similarity of these words
by WordNet. We prove the effect
of this
algorithm with two degree semantic result of web minin
g by generating
the concepts results obtained form
the input query
Topic detecton by clustering and text miningIRJET Journal
This document discusses topic detection from text documents using text mining and clustering techniques. It proposes extracting keywords from documents, representing topics as groups of keywords, and using k-means clustering on the keywords to group them into topics. The keywords are extracted based on frequency counts and preprocessed by removing stop words and stemming. The k-means clustering algorithm is used to assign keywords to topics represented by cluster centroids, and the centroids are iteratively updated until cluster assignments converge.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
Text mining is a new and exciting research area that tries to solve the information overload problem by using techniques from machine learning, natural language processing (NLP), data mining, information retrieval (IR), and knowledge management. Text mining involves the pre-processing of document collections such as information extraction, term extraction, text categorization, and storage of intermediate representations. The techniques that are used to analyse these intermediate representations such as clustering, distribution analysis, association rules and visualisation of the results.
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
Classification of News and Research Articles Using Text Pattern MiningIOSR Journals
This document summarizes a research paper that proposes a method for classifying news and research articles using text pattern mining. The method involves preprocessing text to remove stop words and perform stemming. Frequent and closed patterns are then discovered from the preprocessed text. These patterns are structured into a taxonomy and deployed to classify new documents. The method also involves evolving patterns by reshuffling term supports within patterns to reduce the effects of noise from negative documents. Over 80% of documents were successfully classified using this pattern-based approach.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document presents a novel approach for clustering textual information in emails using text data mining techniques. It discusses using k-means clustering and a vector space model to group similar emails based on word patterns and frequencies. The methodology involves preprocessing emails, applying a Porter stemmer, calculating term frequencies, and using k-means to form clusters. Clusters will contain emails with similar content, allowing users to more easily process emails based on priority. This clustering approach could reduce the time users spend filtering through emails one by one.
Hybrid approach for generating non overlapped substring using genetic algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Statistical Named Entity Recognition for Hungarian – analysis ...butest
This document describes statistical named entity recognition for Hungarian texts. The authors created a corpus of Hungarian news articles annotated with named entity tags. They used a rich set of 225 linguistic features to train support vector machines, neural networks, and decision trees. Their best model achieved an F-measure of 93.59% for term-level named entity recognition and 90.57% for phrase-level, outperforming prior rule-based systems for Hungarian. Feature selection helped reduce the feature set to 135 while maintaining high performance.
This document describes a method for enriching search results using ontology. It begins with an abstract discussing how keyword searches often return irrelevant documents due to the large amount of information available online. It then introduces the concept of using ontology to allow for more sophisticated semantic searches. The paper presents an architecture that augments keyword search results with additional documents that are semantically relevant based on ontology mappings. Documents in the search results are then ranked based on both keyword frequency and semantic relevance to improve search accuracy.
8 efficient multi-document summary generation using neural networkINFOGAIN PUBLICATION
This paper proposes a multi-document summarization system that uses bisect k-means clustering, an optimal merge function, and a neural network. The system first preprocesses input documents through stemming and removing stop words. It then applies bisect k-means clustering to group similar sentences. The clusters are merged using an optimal merge function to find important keywords. The NEWSUM algorithm is used to generate a primary summary for each keyword. A neural network trained on sentence classifications is then used to classify sentences in the primary summary as positive or negative. Only positively classified sentences are included in the final summary to improve accuracy. The system aims to generate a concise and accurate summary in a short period of time from multiple documents on a given topic.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
IRJET- A Pragmatic Supervised Learning Methodology of Hate Speech Detection i...IRJET Journal
This document summarizes a paper that proposes a supervised machine learning approach for detecting hate speech in social media. It begins with an introduction to the problem of hate speech online and anonymity enabling harmful communication. It then describes common text preprocessing techniques like tokenization and filtering used to clean text data. Feature extraction methods are discussed, including n-grams, bag-of-words, and word embeddings. Popular machine learning algorithms for classification are also summarized, such as support vector machines, logistic regression, and neural networks. The document concludes by reviewing related work on hate speech detection and challenges around dataset annotation.
Knowledge Graph and Similarity Based Retrieval Method for Query Answering SystemIRJET Journal
This document proposes a knowledge graph and question answering system to extract and analyze information from large volumes of unstructured data like annual reports. It discusses using natural language processing techniques like named entity recognition with spaCy and dependency parsing to extract entity-relation pairs from text and construct a knowledge graph. For question answering, it analyzes user queries with similar NLP approaches and then matches query triplets to the knowledge graph to retrieve answers, combining information retrieval and trained classifiers. The proposed system aims to provide faster understanding and analysis of complex, unstructured data for professionals.
A template based algorithm for automatic summarization and dialogue managemen...eSAT Journals
Abstract This paper describes an automated approach for extracting significant and useful events from unstructured text. The goal of research is to come out with a methodology which helps in extracting important events such as dates, places, and subjects of interest. It would be also convenient if the methodology helps in presenting the users with a shorter version of the text which contain all non-trivial information. We also discuss implementation of algorithms which exactly does this task, developed by us. Key Words: Cosine Similarity, Information, Natural Language, Summarization, Text Mining
CANDIDATE SET KEY DOCUMENT RETRIEVAL SYSTEMIRJET Journal
This document describes a proposed candidate set key document retrieval system. The system would process user queries in English and return relevant documents from a collection. It would use natural language processing techniques like tokenization, stop word removal, stemming, and lemmatization to index the documents and match them with user queries. The proposed system architecture includes components for indexing, processing user queries, and retrieving relevant documents from the collection. The indexing process involves organizing the documents, extracting tokens, removing stop words, and applying stemming/lemmatization to create an inverted index for efficient searching.
Text Mining is the technique that helps users to find out useful information from a large amount of text documents on the web or database. Most popular text mining and classification methods have adopted term-based approaches. The term based approaches and the pattern-based method describing user preferences. This review paper analyse how the text mining work on the three level i.e sentence level, document level and feature level. In this paper we review the related work which is previously done. This paper also demonstrated that what are the problems arise while doing text mining done at the feature level. This paper presents the technique to text mining for the compound sentences.
This document describes a proposed concept-based mining model that aims to improve document clustering and information retrieval by extracting concepts and semantic relationships rather than just keywords. The model uses natural language processing techniques like part-of-speech tagging and parsing to extract concepts from text. It represents concepts and their relationships in a semantic network and clusters documents based on conceptual similarity rather than term frequency. The model is evaluated using singular value decomposition to increase the precision of key term and phrase extraction.
IRJET- Survey for Amazon Fine Food ReviewsIRJET Journal
This document discusses sentiment analysis and summarizes several papers on related topics. It begins with an abstract describing sentiment analysis and its importance. The introduction defines sentiment classification and analysis. The literature survey section summarizes 5 papers on natural language processing and machine learning algorithms for sentiment analysis, including K-means clustering, bag-of-words models, TF-IDF vectorization for document clustering, hierarchical clustering methods, and using naive bayes and SVM for sentiment analysis and text summarization. The conclusion discusses techniques for data processing, natural language processing, and machine learning algorithms covered.
This document provides an overview of text mining and web mining. It defines data mining and describes the common data mining tasks of classification, clustering, association rule mining and sequential pattern mining. It then discusses text mining, defining it as the process of analyzing unstructured text data to extract meaningful information and structure. The document outlines the seven practice areas of text mining as search/information retrieval, document clustering, document classification, web mining, information extraction, natural language processing, and concept extraction. It provides brief descriptions of the problems addressed within each practice area.
AN EFFICIENT APPROACH TO IMPROVE ARABIC DOCUMENTS CLUSTERING BASED ON A NEW K...csandit
Document Clustering algorithms goal is to create clusters that are coherent internally, but
clearly different from each other. The useful expressions in the documents is often accompanied
by a large amount of noise that is caused by the use of unnecessary words, so it is indispensable
to eliminate it and keeping just the useful information.
Keyphrases extraction systems in Arabic are new phenomena. A number of Text Mining
applications can use it to improve her results. The Keyphrases are defined as phrases that
capture the main topics discussed in document; they offer a brief and precise summary of
document content. Therefore, it can be a good solution to get rid of the existent noise from
documents.
In this paper, we propose a new method to solve the problem cited above especially for Arabic
language documents, which is one of the most complex languages, by using a new Keyphrases
extraction algorithm based on the Suffix Tree data structure (KpST). To evaluate our approach,
we conduct an experimental study on Arabic Documents Clustering using the most popular
approach of Hierarchical algorithms: Agglomerative Hierarchical algorithm with seven linkage
techniques and a variety of distance functions and similarity measures to perform Arabic
Document Clustering task. The obtained results show that our approach for extracting
Keyphrases improves the clustering results.
Conceptual similarity measurement algorithm for domain specific ontology[Zac Darcy
This paper presents the similarity measurement algorithm for domain specific terms collected in the
ontology based data integration system. This similarity measurement algorithm can be used in ontology
mapping and query service of
ontology based data integration sy
stem. In this paper, we focus
o
n the web
query service to apply
this proposed algorithm
. Concepts similarity is important for web query service
because the words in user input query are not
same wholly with the concepts in
ontology. So, we need to
extract the possible concepts that are match or related to the input words with the help of machine readable
dictionary WordNet. Sometimes, we use the generated mapping rules in query generation procedure for
some words that canno
t be
confirmed the similarity of these words
by WordNet. We prove the effect
of this
algorithm with two degree semantic result of web minin
g by generating
the concepts results obtained form
the input query
Topic detecton by clustering and text miningIRJET Journal
This document discusses topic detection from text documents using text mining and clustering techniques. It proposes extracting keywords from documents, representing topics as groups of keywords, and using k-means clustering on the keywords to group them into topics. The keywords are extracted based on frequency counts and preprocessed by removing stop words and stemming. The k-means clustering algorithm is used to assign keywords to topics represented by cluster centroids, and the centroids are iteratively updated until cluster assignments converge.
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
Text mining is a new and exciting research area that tries to solve the information overload problem by using techniques from machine learning, natural language processing (NLP), data mining, information retrieval (IR), and knowledge management. Text mining involves the pre-processing of document collections such as information extraction, term extraction, text categorization, and storage of intermediate representations. The techniques that are used to analyse these intermediate representations such as clustering, distribution analysis, association rules and visualisation of the results.
FAST FUZZY FEATURE CLUSTERING FOR TEXT CLASSIFICATION cscpconf
Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text
classification. In this paper, Fast Fuzzy Feature clustering for text classification is proposed. It
is based on the framework proposed by Jung-Yi Jiang, Ren-Jia Liou and Shie-Jue Lee in 2011.
The word in the feature vector of the document is grouped into the cluster in less iteration. The
numbers of iterations required to obtain cluster centers are reduced by transforming clusters
center dimension from n-dimension to 2-dimension. Principle Component Analysis with slit
change is used for dimension reduction. Experimental results show that, this method improve
the performance by significantly reducing the number of iterations required to obtain the cluster
center. The same is being verified with three benchmark datasets
Classification of News and Research Articles Using Text Pattern MiningIOSR Journals
This document summarizes a research paper that proposes a method for classifying news and research articles using text pattern mining. The method involves preprocessing text to remove stop words and perform stemming. Frequent and closed patterns are then discovered from the preprocessed text. These patterns are structured into a taxonomy and deployed to classify new documents. The method also involves evolving patterns by reshuffling term supports within patterns to reduce the effects of noise from negative documents. Over 80% of documents were successfully classified using this pattern-based approach.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document presents a novel approach for clustering textual information in emails using text data mining techniques. It discusses using k-means clustering and a vector space model to group similar emails based on word patterns and frequencies. The methodology involves preprocessing emails, applying a Porter stemmer, calculating term frequencies, and using k-means to form clusters. Clusters will contain emails with similar content, allowing users to more easily process emails based on priority. This clustering approach could reduce the time users spend filtering through emails one by one.
Hybrid approach for generating non overlapped substring using genetic algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Statistical Named Entity Recognition for Hungarian – analysis ...butest
This document describes statistical named entity recognition for Hungarian texts. The authors created a corpus of Hungarian news articles annotated with named entity tags. They used a rich set of 225 linguistic features to train support vector machines, neural networks, and decision trees. Their best model achieved an F-measure of 93.59% for term-level named entity recognition and 90.57% for phrase-level, outperforming prior rule-based systems for Hungarian. Feature selection helped reduce the feature set to 135 while maintaining high performance.
This document describes a method for enriching search results using ontology. It begins with an abstract discussing how keyword searches often return irrelevant documents due to the large amount of information available online. It then introduces the concept of using ontology to allow for more sophisticated semantic searches. The paper presents an architecture that augments keyword search results with additional documents that are semantically relevant based on ontology mappings. Documents in the search results are then ranked based on both keyword frequency and semantic relevance to improve search accuracy.
8 efficient multi-document summary generation using neural networkINFOGAIN PUBLICATION
This paper proposes a multi-document summarization system that uses bisect k-means clustering, an optimal merge function, and a neural network. The system first preprocesses input documents through stemming and removing stop words. It then applies bisect k-means clustering to group similar sentences. The clusters are merged using an optimal merge function to find important keywords. The NEWSUM algorithm is used to generate a primary summary for each keyword. A neural network trained on sentence classifications is then used to classify sentences in the primary summary as positive or negative. Only positively classified sentences are included in the final summary to improve accuracy. The system aims to generate a concise and accurate summary in a short period of time from multiple documents on a given topic.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Modeling Text Independent Speaker Identification with Vector QuantizationTELKOMNIKA JOURNAL
Speaker identification is one of the most important technologies nowadays. Many fields such as
bioinformatics and security are using speaker identification. Also, almost all electronic devices are using
this technology too. Based on number of text, speaker identification divided into text dependent and text
independent. On many fields, text independent is mostly used because number of text is unlimited. So, text
independent is generally more challenging than text dependent. In this research, speaker identification text
independent with Indonesian speaker data was modelled with Vector Quantization (VQ). In this research
VQ with K-Means initialization was used. K-Means clustering also was used to initialize mean and
Hierarchical Agglomerative Clustering was used to identify K value for VQ. The best VQ accuracy was
59.67% when k was 5. According to the result, Indonesian language could be modelled by VQ. This
research can be developed using optimization method for VQ parameters such as Genetic Algorithm or
Particle Swarm Optimization.
IRJET- A Pragmatic Supervised Learning Methodology of Hate Speech Detection i...IRJET Journal
This document summarizes a paper that proposes a supervised machine learning approach for detecting hate speech in social media. It begins with an introduction to the problem of hate speech online and anonymity enabling harmful communication. It then describes common text preprocessing techniques like tokenization and filtering used to clean text data. Feature extraction methods are discussed, including n-grams, bag-of-words, and word embeddings. Popular machine learning algorithms for classification are also summarized, such as support vector machines, logistic regression, and neural networks. The document concludes by reviewing related work on hate speech detection and challenges around dataset annotation.
Knowledge Graph and Similarity Based Retrieval Method for Query Answering SystemIRJET Journal
This document proposes a knowledge graph and question answering system to extract and analyze information from large volumes of unstructured data like annual reports. It discusses using natural language processing techniques like named entity recognition with spaCy and dependency parsing to extract entity-relation pairs from text and construct a knowledge graph. For question answering, it analyzes user queries with similar NLP approaches and then matches query triplets to the knowledge graph to retrieve answers, combining information retrieval and trained classifiers. The proposed system aims to provide faster understanding and analysis of complex, unstructured data for professionals.
A template based algorithm for automatic summarization and dialogue managemen...eSAT Journals
Abstract This paper describes an automated approach for extracting significant and useful events from unstructured text. The goal of research is to come out with a methodology which helps in extracting important events such as dates, places, and subjects of interest. It would be also convenient if the methodology helps in presenting the users with a shorter version of the text which contain all non-trivial information. We also discuss implementation of algorithms which exactly does this task, developed by us. Key Words: Cosine Similarity, Information, Natural Language, Summarization, Text Mining
CANDIDATE SET KEY DOCUMENT RETRIEVAL SYSTEMIRJET Journal
This document describes a proposed candidate set key document retrieval system. The system would process user queries in English and return relevant documents from a collection. It would use natural language processing techniques like tokenization, stop word removal, stemming, and lemmatization to index the documents and match them with user queries. The proposed system architecture includes components for indexing, processing user queries, and retrieving relevant documents from the collection. The indexing process involves organizing the documents, extracting tokens, removing stop words, and applying stemming/lemmatization to create an inverted index for efficient searching.
CONTEXT-AWARE CLUSTERING USING GLOVE AND K-MEANSijseajournal
ABSTRACT
In this paper we propose a novel method to cluster categorical data while retaining their context. Typically, clustering is performed on numerical data. However it is often useful to cluster categorical data as well, especially when dealing with data in real-world contexts. Several methods exist which can cluster categorical data, but our approach is unique in that we use recent text-processing and machine learning advancements like GloVe and t- SNE to develop a a context-aware clustering approach (using pre-trained
word embeddings). We encode words or categorical data into numerical, context-aware, vectors that we use to cluster the data points using common clustering algorithms like K-means.
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
Feature selection, optimization and clustering strategies of text documentsIJECEIAES
Clustering is one of the most researched areas of data mining applications in the contemporary literature. The need for efficient clustering is observed across wide sectors including consumer segmentation, categorization, shared filtering, document management, and indexing. The research of clustering task is to be performed prior to its adaptation in the text environment. Conventional approaches typically emphasized on the quantitative information where the selected features are numbers. Efforts also have been put forward for achieving efficient clustering in the context of categorical information where the selected features can assume nominal values. This manuscript presents an in-depth analysis of challenges of clustering in the text environment. Further, this paper also details prominent models proposed for clustering along with the pros and cons of each model. In addition, it also focuses on various latest developments in the clustering task in the social network and associated environments.
SEMANTIC INTEGRATION FOR AUTOMATIC ONTOLOGY MAPPING cscpconf
In the last decade, ontologies have played a key technology role for information sharing and agents interoperability in different application domains. In semantic web domain, ontologies are efficiently used toface the great challenge of representing the semantics of data, in order to bring the actual web to its full
power and hence, achieve its objective. However, using ontologies as common and shared vocabularies requires a certain degree of interoperability between them. To confront this requirement, mapping ontologies is a solution that is not to be avoided. In deed, ontology mapping build a meta layer that allows different applications and information systems to access and share their informations, of course, after resolving the different forms of syntactic, semantic and lexical mismatches. In the contribution presented in this paper, we have integrated the semantic aspect based on an external lexical resource, wordNet, to design a new algorithm for fully automatic ontology mapping. This fully automatic character features the
main difference of our contribution with regards to the most of the existing semi-automatic algorithms of ontology mapping, such as Chimaera, Prompt, Onion, Glue, etc. To better enhance the performances of our algorithm, the mapping discovery stage is based on the combination of two sub-modules. The former
analysis the concept’s names and the later analysis their properties. Each one of these two sub-modules is
it self based on the combination of lexical and semantic similarity measures.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share their interests without being at the same geographical location. With the great and rapid growth of Social Media sites such as Facebook, LinkedIn, Twitter…etc. causes huge amount of user-generated content. Thus, the improvement in the information quality and integrity becomes a great challenge to all social media sites, which allows users to get the desired content or be linked to the best link relation using improved search / link technique. So introducing semantics to social networks will widen up the representation of the social networks. In this paper, a new model of social networks based on semantic tag ranking is introduced. This model is based on the concept of multi-agent systems. In this proposed model the representation of social links will be extended by the semantic relationships found in the vocabularies which are known as (tags) in most of social networks.The proposed model for the social media engine is based on enhanced Latent Dirichlet Allocation(E-LDA) as a semantic indexing algorithm, combined with Tag Rank as social network ranking algorithm. The improvements on (E-LDA) phase is done by optimizing (LDA) algorithm using the optimal parameters. Then a filter is introduced to enhance the final indexing output. In ranking phase, using Tag Rank based on the indexing phase has improved the output of the ranking. Simulation results of the proposed model have shown improvements in indexing and ranking output.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGIJwest
The document presents a new model for intelligent social networks based on semantic tag ranking. It uses a multi-agent system approach with agents performing indexing and ranking. For indexing, it uses an enhanced Latent Dirichlet Allocation (E-LDA) model that optimizes LDA parameters. Tags above a threshold from E-LDA output are ranked using Tag Rank. Simulation results showed improvements in indexing and ranking over conventional methods. The model introduces semantics to social networks to improve search and link recommendation.
INTELLIGENT SOCIAL NETWORKS MODEL BASED ON SEMANTIC TAG RANKINGdannyijwest
Social Networks has become one of the most popular platforms to allow users to communicate, and share
their interests without being at the same geographical location. With the great and rapid growth of Social
Media sites such as Facebook, LinkedIn, Twitter...etc. causes huge amount of user-generated content.
Thus, the improvement in the information quality and integrity becomes a great challenge to all social
media sites, which allows users to get the desired content or be linked to the best link relation using
improved search / link technique. So introducing semantics to social networks will widen up the
representation of the social networks.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Improvement of Text Summarization using Fuzzy Logic Based MethodIOSR Journals
The document describes a method for improving text summarization using fuzzy logic. It proposes using fuzzy logic to determine the importance of sentences based on calculated feature scores. Eight features are used to score sentences, including title words, length, term frequency, position, and similarity. Sentences are then ranked based on their fuzzy logic-determined scores. The highest scoring sentences are extracted to create a summary. An evaluation of summaries generated using this fuzzy logic method found it performed better than other summarizers in accurately reflecting the content and order of human-generated reference summaries. The method could be expanded to multi-document summarization and automatic selection of fuzzy rules based on input type.
This document proposes a BOT virtual guide that will extract educational web content based on topics recently taught using web crawling techniques. It will use a domain ontology, DOM parsing, and concept-focused crawling to find relevant documents from the web. The documents will be ranked based on their concept similarity to the topic. The filtered and crawled data will then be provided to students as speech output through a text-to-speech system to serve as an automated virtual guide for supplemental learning materials.
Taxonomy extraction from automotive natural language requirements using unsup...ijnlc
In this paper we present a novel approach to semi-automatically learn concept hierarchies from natural
language requirements of the automotive industry. The approach is based on the distributional hypothesis
and the special characteristics of domain-specific German compounds. We extract taxonomies by using
clustering techniques in combination with general thesauri. Such a taxonomy can be used to support
requirements engineering in early stages by providing a common system understanding and an agreedupon
terminology. This work is part of an ontology-driven requirements engineering process, which builds
on top of the taxonomy. Evaluation shows that this taxonomy extraction approach outperforms common
hierarchical clustering techniques.
1) The document discusses different clustering algorithms for text summarization including hierarchical clustering, query-based summarization, graph theoretic clustering, fuzzy c-means clustering, and DBSCAN clustering.
2) These algorithms are evaluated based on performance parameters like precision, recall, time complexity, space complexity, and summary quality.
3) The algorithm found to perform best based on these evaluations will be suggested as the better algorithm for query-dependent text document summarization.
This document presents a framework for automatically generating entity-relationship (ER) diagrams from natural language text input. It involves five main modules: 1) text preprocessing and summary generation, 2) translating the summary to a Semantic Business Vocabulary and Rules (SBVR) format, 3) part-of-speech tagging, 4) extracting ER diagram requirements by identifying entities, relationships, and attributes, and 5) generating an XMI file that can be imported into a UML modeling tool to visualize the generated ER diagram. Keywords are extracted from the input text using term frequency, and sentences are scored and selected for the summary based on important keywords and nouns. The framework aims to reduce the complexity of manually creating ER diagrams by
Mining Opinion Features in Customer ReviewsIJCERT JOURNAL
Now days, E-commerce systems have become extremely important. Large numbers of customers are choosing online shopping because of its convenience, reliability, and cost. Client generated information and especially item reviews are significant sources of data for consumers to make informed buy choices and for makers to keep track of customer’s opinions. It is difficult for customers to make purchasing decisions based on only pictures and short product descriptions. On the other hand, mining product reviews has become a hot research topic and prior researches are mostly based on pre-specified product features to analyse the opinions. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to raw customer reviews and keywords can be extracted. This paper presents a survey on the techniques used for designing software to mine opinion features in reviews. Elven IEEE papers are selected and a comparison is made between them. These papers are representative of the significant improvements in opinion mining in the past decade.
Great model a model for the automatic generation of semantic relations betwee...ijcsity
The
large
a
v
ailable
am
ou
n
t
of
non
-
structured
texts
that
b
e
-
long
to
differe
n
t
domains
su
c
h
as
healthcare
(e.g.
medical
records),
justice
(e.g.
l
a
ws,
declarations),
insurance
(e.g.
declarations),
etc. increases
the
effort
required
for
the
analysis
of
information
in
a
decision making
pro
-
cess.
Differe
n
t
pr
o
jects
and t
o
ols
h
av
e
pro
p
osed
strategies
to
reduce
this
complexi
t
y
b
y
classifying,
summarizing
or
annotating
the
texts.
P
artic
-
ularl
y
,
text
summary
strategies
h
av
e
pr
ov
en
to
b
e
v
ery
useful
to
pr
o
vide
a
compact
view
of
an
original
text.
H
ow
e
v
er,
the
a
v
ailable
strategies
to
generate
these
summaries
do
not
fit
v
ery
w
ell
within
the
domains
that
require
ta
k
e
i
n
to
consideration
the
tem
p
oral
dimension
of
the
text
(e.g.
a
rece
n
t
piece
of
text
in
a
medical
record
is
more
im
p
orta
n
t
than
a
pre
-
vious
one)
and
the
profile
of
the
p
erson
who
requires
the
summary
(e.g
the
medical
s
p
ecialization).
T
o
co
p
e with
these
limitations
this
pa
p
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prese
n
ts
”GRe
A
T”
a
m
o
del
for
automatic
summary
generation
that
re
-
lies
on
natural
language
pr
o
cessing
and
text
mining
te
c
hniques
to
extract
the
most
rele
v
a
n
t
information
from
narrati
v
e
texts
and
disc
o
v
er
new
in
-
formation
from
the
detection
of
related
information. GRe
A
T
M
o
del
w
as impleme
n
ted
on
sof
tw
are
to
b
e
v
alidated
in
a
health
institution
where
it
has
sh
o
wn
to
b
e
v
ery
useful
to displ
a
y
a
preview
of
the
information
a
b
ou
t
medical
health
records
and
disc
o
v
er
new
facts
and
h
y
p
otheses
within
the
information.
Se
v
eral
tests
w
ere
executed
su
c
h
as
F
unctional
-
i
t
y
,
Usabili
t
y
and
P
erformance
regarding
to
the
impleme
n
ted
sof
t
w
are.
In
addition,
precision
and
recall
measures
w
ere
applied
on
the
results
ob
-
tained
through
the
impleme
n
ted
t
o
ol,
as
w
ell
as
on
the
loss
of
information
obtained
b
y
pr
o
viding
a
text
more
shorter than
the
original
Twitter Sentiment Analysis: An Unsupervised ApproachIRJET Journal
The document describes a study that performs sentiment analysis on Twitter data using an unsupervised machine learning technique. It discusses how Twitter data was collected and preprocessed, including removing stopwords and lemmatizing words. It then used the FastText word embedding model to represent words as vectors, which is suitable for unlabeled data. The K-Means clustering algorithm was implemented to group the Twitter data into clusters in an unsupervised manner and classify the tweets as positive, negative, or neutral sentiment.
Similar to ONTOLOGICAL TREE GENERATION FOR ENHANCED INFORMATION RETRIEVAL (20)
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ONTOLOGICAL TREE GENERATION FOR ENHANCED INFORMATION RETRIEVAL
1. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 4, July 2013
DOI : 10.5121/ijaia.2013.4405 41
ONTOLOGICAL TREE GENERATION FOR
ENHANCED INFORMATION RETRIEVAL
Anwaya Aras1
,Sakshi Paratap2
and Dr.Mangesh Bedekar3
1,2,3
Department of Computer Science,BITS-Pilani University,Goa,India
1
anwayaaras@gmail.com
2
sakshi.2392@gmail.com
3
bedekar@goa.bits-pilani.ac.in
ABSTRACT:
Information visualization seeks to leverage human visual processing to make sense of abstract information.
One particularly rich class of information structures ripe for visualization are those representable as
graphs (i.e. nodes and edges), including organization charts, website linkage, and computer networks. In
this paper we propose a methodology to extract information from big data and convert it into a human
comprehensible format of graphs to give the reader an objective overall idea of the document content. We
put forth the design and implementation details to mapping our data into the Open Directory Project or the
DMOZ tree and build a hierarchical ontological tree based on the extracted metadata.
KEYWORDS:
Ontology Information; visualization; Natural Language Processing; Information Retrieval; Key word
extraction; DMOZ; Computer Science Curriculum; Graph based knowledge representation
1. INTRODUCTION
Large related data, like directories, encyclopedias, books have massive amount of data which is
both extensive and important. While it is important to understand and grasp their content, the
process of reading such documents might get time consuming and cumbersome. Text in most
documents is highly redundant and sometimes irrelevant. Entire documents would need to be read
to understand what its content is. Much of such big data, specifically the one which is related can
easily visualized using a graphical hierarchical structure and every token representing a node and
edge linking related categories .To address the problems effectively, we are trying to use
techniques to pull out phrases that seem to characterize a document and then map them to achieve
an ontological tree based graph to represent the information.
For the purpose of experimentation, we have used the academic handouts of universities. For
initial analysis purpose we are focusing on undergraduate courses as they have a well structured
curriculum and range over a wider variety of subjects. Our system effectively generates their
respective DMOZ trees [2] give us a fair idea of the depth of the courses taught which are
represented by the handout. We then compare handouts of various courses and make notes of the
inferences and then correlate them to human drawn conclusions to verify the accurate working of
our system.
It is essential to organize the results into ontology, in particular a hierarchical ontology. Ontology
is an explicit formal specification of the terms and relations among terms in a domain. It can be
2. International Journal of Artificial Intelligence & Applications (IJAIA), Vol. 4, No. 4, July 2013
42
achieved by a systematic grouping of domain concepts (e.g., user interests) based on their
definitions, in machine-interpretable form [7].
In this paper we are proposing an approach for efficient classification of data into the large topic
ontology DMoz. The Open Directory Project, also known as DMOZ, is the largest human edited
directory of Internet sites. The DMOZ directory incorporated 590,000 categories and 6 million
quality website content organized into 15 levels. ODP uses a hierarchical ontology scheme for
organizing site listings. Listings on a similar topic are grouped into categories which then include
smaller categories. One effective technique for the display of such data is a focus+context
approach that uses lightweight modeling of user interest to inform the display of information.User
interest is modeled using a Degree-of-Interest (DOI) function, which assigns a single number
representing the estimated relative interest of the user to each node in the structure.These
numbers are used to appropriately layout and render the structure, for example by controlling
which nodes are visible and which are elided [9].
2. BRIEF METHODOLOGY
Our proposed plan consists of two parts namely document key word retrival and building of
ontological trees.[5] The first part uses techniques of NLP to extract only the important
keywords-unigrams,bigrams and trigrams from the large chunk of data. In the second stage, these
words are fed into the DMOZ directory and when a particular n gram phrase hits the tree, the
entire tree is generated.
2.1. Keyword Extraction
Algorithm:
Efficient extraction of related keywords in the form of n grams models was extremely imporatnt
as it directly affects the accuracy of the DMOZ tree. Although,work on summarization of large
amounts of data has been effectively done and substantial results have been achieved[5],
information extraction from related documents with a characteristic concept flow, requires a
different approach as shown by Rahman [11].
Initially, the data is fragmented and divided into multiple tokens. Then a Part Of Speech tagger is
then run on each sentence and the words are tagged accordingly. However, this tagging is not
accurate enough and hence we have used a novel technique for tagging based on a naturally
growing resource which has used concepts demonstrated by Taskaret. al(2012)[3].Once this
tagging is complete, all the important derivatives of speech are put into the output file.We then
have hard coded a probability value, to generate unigrams, bigrams and trigrams out of the
annotated data.
The following are all the steps that encompass the entire algorithm that runs behind our keyword
extraction module. Each of the techniques described below comprise of a separate module
running at the back end of our code.
* Stemming
We use stemming to narrow our overall word matrix so as to help with the lack of similar words
per text. We have used the standard Porter's stemming algorithm for the purpose. Overall, we see
mixed results for stemming.While stemming shows improvement it also negatively affects the
overall accuracy when combined with other aspects of descriptive texts.
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* Stop Word Removal
As in much text, that there were many common, short function words, often prepositional terms
and other grammatical syntax fillers that were found specially in descriptive texts. We have not
yet attempted to build a domain-specific we used a standard set of Porter stem words.
Ex: the, is, at, who, which, on
Stop words removal turned out to drastically reduce the length of passage and descriptive texts
thereby giving huge accuracy boost.
* Named entity recognition
Entity identification is a very important part of summarization especially because it tells the
system extremely relevant data about the text data. Though NER systems are known to be
brittle,our system has been specifically designed for the big data domain and thus it rightly
extracts the necessary words depending upon the type and setting of the question.
* Punctuation Removal
Removing punctuation was another result of looking at our data and noticing that written texts
have a very large variance in phrasing and word choice. This is especially true for words that may
have multiple accepted forms, or words with punctuation in them. Also, because we parse the
item descriptors on spaces, any punctuations that are in the phrase are left in, including ellipses,
periods, exclamation points, and others. In addition, words that are concatenated are often used
differently. Punctuation removal was the also an effective feature normalization method used for
summarization.
* Lowercasing
While writing in English, texts tend to have the first word capitalized. In addition, different
writing styles will capitalize different words intentionally or otherwise, depending on their intent
interpretation of the word, of choice of capitalizing acronyms. This is generally not a useful
normalization for the system to understand as it deteriorates the performance of the POS tagger.
Ex. President, president, CD, cd, Windows, windows
2.2 Keyword synthesis
After application of the above techniques, the system generates a relevant set of words from the
document. However, tp precisely understand what the document is trying to convey, the
frequency of each word, specific occurrence with other words and the context of its usage is
equally important. Hence, once the raw word data set is available, we further analyze the words
and the information they convey. Three modules are used for the same and the words are further
divided as :
a.Unigramlist: These are the words which occur frequently in the document i.e occur more times
than some threshold value. This threshold value is decided on the basis of the total number of
words, the count of the word which has highest occurrence and the count of the word which has
lowest occurrence
b. Bigram list: These are the words which always occur together in the document. For example
“operating systems” or “computer networks”. Their pairing and occurring together is much
meaningful than the word occurring isolated and hence they carry more weight in word extraction
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analysis. Two words get included in the bigram list if their count of occurring together or at an
offset of at most 3 words is more than a predetermined threshold value.
c. Trigram list: If three words are seen to occur together then they are included in the trigram
list. Occurrences of such words are rare, but if they occur they covey very subtle information and
help greatly in the DMOZ tree building
.2.3. Ontological tree generation
The problem of retrieving the complete category link for an interest and ranking the categories
based on their importance is resolved through the use of DMoz category hierarchy. Every
category in the DMoz dump consists of a listing and description of external pages associated with
that category. Our DMoz based approach works as follows: each interest is searched in the DMoz
RDF dump. The categories under which one or more of the external page descriptions contain the
concerned interest are selected.
Thus, when searching for an interest such as “programming” in the dump, we see that
“Computers” occurs frequently in the external page description of the category link. All such
category links under which the interest is found are extracted. To engineer the ontology, we use
only the top level categories. This avoids large scale duplication of the interest
instances.Furthermore, with DMoz, it becomes possible to retrieve the complete category link
associated with the interest. Thus, for example, the interest “programming” in DMoz belongs not
only to the category “Computers” but to its complete category link, which is Computers-
>Internet->Cloud Computing->Programming We parse the complete category link and every
term in the link becomes a node in the ontology. The interest is made a child of the lowest node in
the hierarchy..
As seen in the figure, all interests are accurately grouped under the respective categories to which
they belong.Simultaneously the depth of the node from the start point is attached at every level to
gauge the importance especially when the levels are more than 10.
Fig 1-A view of DMOZ tree output on the word programming without using any filter
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The nodes can be then arranged in the required way to allow every user to personalize their space.
This can help users to relate certain data, ignore unnecessary information and get more
information in a shorter period.[10]
The various features like zooming, panning on mouse and keyboard actions enable only certain
data to be visible at a given time and increase readability. Simultaneously the application supports
4 different orientations of the data, top to bottom, bottom to up, right to left, left to right, making
it easier for the user to decide depending on the requirement.
The double-click function redirects to open another pop-up window (See figure)which has the
options either to delve deeper into the tree of the clicked topic or choose among the links
corresponding to its topic. Expanding the tree both hides the other irrelevant nodes not in the path
of the clicked data as well as displays its branches .(subtopics arising from the topic) Clicking on
any other button opens the corresponding link on the default web-browser. On the left hand side
are the topics and on the right hand side is a brief description about each topic.
Figure2: Popup window on clicking Oracle.
Fig3: Result on expanding the tree
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Fig4: Result on clicking on the website link “Oracle FAQ”
3. IMPLEMENTATION
We have primarily coded in Python and Java to avail the benefits of both the languages. While
Python offers an extensive and a fully fledged library for language processing purposes, JavaJava
is fast, secure, reliable and with its underlying object-oriented principles provides an excellent
platform to work on.
The summarization and key word extraction part has been implemented in Python programming
language using NLTK library[1]. Since python contain an extensive library for language
processing, it stood out as the best language for development of robust modules for this
project.We have developed our own POS tagger,tokeniser and hand codded features for the
named entity recognition algorithm based on the dominant aspects of big data analysis.These
modules when run together successfully extract the relevant words of our dataand further go on to
generating the n gram words for our project
The requirements of our design of DMOZ trees are best supported by Java using
Prefuse[8].Prefuse is a set of software tools for creating rich interactive data visualizations. The
original prefuse toolkit provides a visualization framework for the Java programming language.
The prefuse flare toolkit also provides visualization and animation tools for ActionScript and the
Adobe Flash Player. The label renderer and JPanel components introduce interactivity in the
visualization.
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4. INTEGRATED SYSTEM AND RESULTS
4.1. Handout Data extraction
The course handouts of Massachusetts institute of technology (MIT) and Birla Institute of
Technology and Science (BITS) have been used for analysis. Using this method all the
information of the course can be easily represented in graphical hierarchical form, thus making it
possible to get the sense of the topics, sub-topics and the complete course structure. By The
handouts used in consideration here are of the course Computer Networks , offered by the
Computer Science department of the colleges. Techniques have been used to perform
interestword sense disambiguation . For instance here, “Security” should refer to something in the
world of Computers only. Thus, this approach constructs a simple yet effective grouping of user
interests.
Fig5 -A view of DMOZ tree output on the handout of BITS.
Fig6 -A view of DMOZ tree output on the handout of MIT.
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4.2 Results:
The tree gives complete information of the course structure, the linkage between categories,
classification, the volume and depth of each topic that will be covered.
It can be gauged if only the overview or details of particular topics would be covered in the
duration of the course. For instance in BITS, the node ethernet has no children, thus it is less
likely that the topic will be taught in much detail. Similarly we can even compare two topics and
safely say that protocols will be focused more than ethernet in BITS.
On formation of the tree we can easily extract useful comparison from the structure of courses:
• Here both MIT and BITS have topics till the level 6, thus providing equal information on
considering depth and detail level of the course taught.
• We can easily find out the broad topics being studied, which topics overlap and
differences. Here the topic Data communications is covered in BITS, including the
subtopic ethernet while MIT does not have it.
• If we go into the depth of the tree, we see that protocols is taught in much more depth in
MIT focusing on RFCs, NAT while in BITS the focus is on studying various types of
protocols without going into intricate details.
We similarly created a tree of all the subjects of interest to get a complete picture of the subjects
ordered by only important key topics, thus providing details on the whole course in one
graph.Zooming and panning provides easy navigation to any topic and its linked branches. For
details regarding a particular topic, we can double click the interest node and go into further depth
or web links relevant to the category.We aim to use highlighting and navigation techniques to
check overlapping information between subjects, check particular courses itself and thus provide
a clear picture.
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Fig 7.Complete tree
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Fig 8.Zooming in a particular area
5. FUTURE WORK AND CONCLUSION
The results provided by our application were satisfactory and gave precise information as well as
provided clarity in regard to the actual topics being covered and depth and those as resulting from
our application. The technique and methodology used provided upto 90% accuracy in most cases.
One of the future scope that stands for this project is increasing the number of relevant keywords
extracted by the system and thus increasing the recall value for key word extraction module.
Specifically, the system tends to reject acronyms as it identifies them as stray words and
overlooks their instances. Robust feature extractors for training the system using NER are needed
to be built to overcome this problem and get an even more efficient system.
The interface is being made more appealing by adding additional graphics and features to
increase interactivity. The project is being deployed as a web app to allow instant result display
on any file that is provided.
In future, similar procedure can be employed to other databases and directories like Wikipedia to
improve visualization and to provide exhaustive results as well as compare how the results are
mapped in each of the given dumps. More number of colleges as well as schools’ curriculum is
being compared .Analysis on how the information is related at every stage and usefulness and
relevance of subjects when compared to specialization in a particular field in that branch is being
done. The same Information Visualization Techniques and concepts can be used to create precise
memory maps and business analytics techniques can be formulated.
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