The document discusses various information retrieval models including Boolean, vector space, and probabilistic models. It provides details on how documents and queries are represented and compared in the vector space model. Specifically, it explains that in this model, documents and queries are represented as vectors of term weights in a multi-dimensional space. The similarity between a document and query vector is calculated using measures like the inner product or cosine similarity to retrieve and rank documents.
The Boolean model is a classical information retrieval model based on set theory and Boolean logic. Queries are specified as Boolean expressions to retrieve documents that either contain or do not contain the query terms. All term frequencies are binary and documents are retrieved based on an exact match to the query terms. However, this model has limitations as it does not rank documents and queries are difficult for users to translate into Boolean expressions, often returning too few or too many results.
This document compares web search and information retrieval (IR) across 10 differentiators:
1. Languages - Web search indexes documents in many languages using full text, while IR databases usually cover one language.
2. File types - Web search indexes several file types including some without text, while IR indexes consistent formats like PDF.
3. Document length - Web documents vary widely in length from short to long, while IR documents vary less.
4. Document structure - Web documents are semi-structured HTML, while IR allows searching structured document fields.
The document discusses information retrieval models. It describes the Boolean retrieval model, which represents documents and queries as sets of terms combined with Boolean operators. Documents are retrieved if they satisfy the Boolean query, but there is no ranking of results. The Boolean model has limitations including difficulty expressing complex queries, controlling result size, and ranking results. It works best for simple, precise queries when users know exactly what they are searching for.
Vector space model or term vector model is an algebraic model for representing text documents as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings. Its first use was in the SMART Information Retrieval System
The document discusses information retrieval, which involves obtaining information resources relevant to an information need from a collection. The information retrieval process begins when a user submits a query. The system matches queries to database information, ranks objects based on relevance, and returns top results to the user. The process involves document acquisition and representation, user problem representation as queries, and searching/retrieval through matching and result retrieval.
An inverted file indexes a text collection to speed up searching. It contains a vocabulary of distinct words and occurrences lists with information on where each word appears. For each term in the vocabulary, it stores a list of pointers to occurrences called an inverted list. Coarser granularity indexes use less storage but require more processing, while word-level indexes enable proximity searches but use more space. The document describes how inverted files are structured and constructed from text and discusses techniques like block addressing that reduce their space requirements.
The document discusses various information retrieval models, including:
1) Classic models like Boolean and vector space models that use index terms to represent documents and queries.
2) Probabilistic models that view IR as estimating the probability of relevance between documents and queries.
3) Structured models that incorporate document structure, including models based on non-overlapping text regions and hierarchical document structure.
4) Browsing models like flat, structure-guided, and hypertext models for navigating document collections.
The document discusses the vector space model for representing text documents and queries in information retrieval systems. It describes how documents and queries are represented as vectors of term weights, with each term being assigned a weight based on its frequency in the document or query. The vector space model allows documents and queries to be compared by calculating the similarity between their vector representations. Terms that are more frequent in a document and less frequent overall are given higher weights through techniques like TF-IDF weighting. This vector representation enables efficient retrieval of documents ranked by similarity to the query.
The Boolean model is a classical information retrieval model based on set theory and Boolean logic. Queries are specified as Boolean expressions to retrieve documents that either contain or do not contain the query terms. All term frequencies are binary and documents are retrieved based on an exact match to the query terms. However, this model has limitations as it does not rank documents and queries are difficult for users to translate into Boolean expressions, often returning too few or too many results.
This document compares web search and information retrieval (IR) across 10 differentiators:
1. Languages - Web search indexes documents in many languages using full text, while IR databases usually cover one language.
2. File types - Web search indexes several file types including some without text, while IR indexes consistent formats like PDF.
3. Document length - Web documents vary widely in length from short to long, while IR documents vary less.
4. Document structure - Web documents are semi-structured HTML, while IR allows searching structured document fields.
The document discusses information retrieval models. It describes the Boolean retrieval model, which represents documents and queries as sets of terms combined with Boolean operators. Documents are retrieved if they satisfy the Boolean query, but there is no ranking of results. The Boolean model has limitations including difficulty expressing complex queries, controlling result size, and ranking results. It works best for simple, precise queries when users know exactly what they are searching for.
Vector space model or term vector model is an algebraic model for representing text documents as vectors of identifiers, such as, for example, index terms. It is used in information filtering, information retrieval, indexing and relevancy rankings. Its first use was in the SMART Information Retrieval System
The document discusses information retrieval, which involves obtaining information resources relevant to an information need from a collection. The information retrieval process begins when a user submits a query. The system matches queries to database information, ranks objects based on relevance, and returns top results to the user. The process involves document acquisition and representation, user problem representation as queries, and searching/retrieval through matching and result retrieval.
An inverted file indexes a text collection to speed up searching. It contains a vocabulary of distinct words and occurrences lists with information on where each word appears. For each term in the vocabulary, it stores a list of pointers to occurrences called an inverted list. Coarser granularity indexes use less storage but require more processing, while word-level indexes enable proximity searches but use more space. The document describes how inverted files are structured and constructed from text and discusses techniques like block addressing that reduce their space requirements.
The document discusses various information retrieval models, including:
1) Classic models like Boolean and vector space models that use index terms to represent documents and queries.
2) Probabilistic models that view IR as estimating the probability of relevance between documents and queries.
3) Structured models that incorporate document structure, including models based on non-overlapping text regions and hierarchical document structure.
4) Browsing models like flat, structure-guided, and hypertext models for navigating document collections.
The document discusses the vector space model for representing text documents and queries in information retrieval systems. It describes how documents and queries are represented as vectors of term weights, with each term being assigned a weight based on its frequency in the document or query. The vector space model allows documents and queries to be compared by calculating the similarity between their vector representations. Terms that are more frequent in a document and less frequent overall are given higher weights through techniques like TF-IDF weighting. This vector representation enables efficient retrieval of documents ranked by similarity to the query.
Probabilistic information retrieval models & systemsSelman Bozkır
The document discusses probabilistic information retrieval and Bayesian approaches. It introduces concepts like conditional probability, Bayes' theorem, and the probability ranking principle. It explains how probabilistic models estimate the probability of relevance between a document and query by representing them as term sets and making probabilistic assumptions. The goal is to rank documents by the probability of relevance to present the most likely relevant documents first.
The document discusses various aspects of term weighting which is important for text retrieval systems, including term frequency, document frequency, inverse document frequency, and how they are used to calculate TF-IDF weights for terms. It also covers stoplists, stemming, and the bag-of-words model which represents text as vectors of word occurrences without considering word order. Term weighting schemes play a major role in the similarity measures used by information retrieval systems to determine document relevance.
This document discusses evaluation methods for information retrieval systems. It begins by outlining different types of evaluation, including retrieval effectiveness, efficiency, and user-based evaluation. It then focuses on retrieval effectiveness, describing commonly used measures like precision, recall, and discounted cumulative gain. It discusses how these measures are calculated and their limitations. The document also introduces other evaluation metrics like R-precision, average precision, and normalized discounted cumulative gain that provide single value assessments of system performance.
The document discusses probabilistic retrieval models in information retrieval. It introduces three influential probabilistic models: (1) Maron and Kuhns' 1960 model which calculates the probability of relevance based on historical user data; (2) Salton's model which estimates the probability of term occurrence in relevant documents; (3) A model that ranks documents by the probability of relevance and considers retrieval as a decision between costs of retrieving non-relevant vs. not retrieving relevant documents. The document provides background on the development of probabilistic IR models and challenges of estimating probabilities for evaluation.
Ppt evaluation of information retrieval systemsilambu111
The document discusses the evaluation of information retrieval systems. Evaluation is defined as systematically determining a subject's merit using a set of standards. The main purposes of evaluation are to compare the performance of different systems, assess how well systems meet their goals, and identify ways to improve effectiveness. Evaluation can consider managerial or user viewpoints. Common criteria include recall, precision, fallout, generality, effectiveness, efficiency, usability, satisfaction, and cost. Recall measures the proportion of relevant documents retrieved while precision measures the proportion of retrieved documents that are relevant. Evaluation helps identify ways to improve information retrieval system performance.
This document provides a full syllabus with questions and answers related to the course "Information Retrieval" including definitions of key concepts, the historical development of the field, comparisons between information retrieval and web search, applications of IR, components of an IR system, and issues in IR systems. It also lists examples of open source search frameworks and performance measures for search engines.
This document provides an overview of an information retrieval system. It defines an information retrieval system as a system capable of storing, retrieving, and maintaining information such as text, images, audio, and video. The objectives of an information retrieval system are to minimize the overhead for a user to locate needed information. The document discusses functions like search, browse, indexing, cataloging, and various capabilities to facilitate querying and retrieving relevant information from the system.
The document summarizes a technical seminar on web-based information retrieval systems. It discusses information retrieval architecture and approaches, including syntactical, statistical, and semantic methods. It also covers web search analysis techniques like web structure analysis, content analysis, and usage analysis. The document outlines the process of web crawling and types of crawlers. It discusses challenges of web structure, crawling and indexing, and searching. Finally, it concludes that as unstructured online information grows, information retrieval techniques must continue to improve to leverage this data.
The document discusses the World Wide Web and information retrieval on the web. It provides background on how the web was developed by Tim Berners-Lee in 1990 using HTML, HTTP, and URLs. It then discusses some key differences in information retrieval on the web compared to traditional library systems, including the presence of hyperlinks, heterogeneous content, duplication of content, exponential growth in the number of documents, and lack of stability. It also summarizes some challenges in web search including the expanding nature of the web, dynamically generated content, influence of monetary contributions on search results, and search engine spamming.
Data mining involves multiple steps in the knowledge discovery process including data cleaning, integration, selection, transformation, mining, and pattern evaluation. It has various functionalities including descriptive mining to characterize data, predictive mining for inference, and different mining techniques like classification, association analysis, clustering, and outlier analysis.
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.
This document provides an overview of information retrieval models. It begins with definitions of information retrieval and how it differs from data retrieval. It then discusses the retrieval process and logical representations of documents. A taxonomy of IR models is presented including classic, structured, and browsing models. Boolean, vector, and probabilistic models are explained as examples of classic models. The document concludes with descriptions of ad-hoc retrieval and filtering tasks and formal characteristics of IR models.
Parallel and Distributed Information Retrieval Systemvimalsura
This document discusses parallel and distributed information retrieval. It describes how parallel architectures like MIMD can be used to accelerate search over very large document collections by distributing the work across multiple processors. Two main approaches to parallelism are covered: building new parallel algorithms or adapting existing techniques. Common ways to partition data for parallel indexing and search are discussed, including document partitioning and term partitioning. Specific data structures like inverted files, suffix arrays, and signature files are examined in terms of how they can be adapted for parallel and distributed retrieval architectures.
This document discusses evaluation in information retrieval. It describes standard test collections which consist of a document collection, queries on the collection, and relevance judgments. It also discusses various evaluation measures used in information retrieval like precision, recall, F-measure, mean average precision, and kappa statistic which measure reliability of relevance judgments. R-precision and normalized discounted cumulative gain are also summarized as important single number evaluation measures.
The document discusses signature files, which are used for document retrieval. A signature file creates a compressed representation or "signature" for each document in a database. These signatures are stored in hash tables to allow easy retrieval of matching documents for user queries. Signatures can represent words using triplets of characters and a hash function, or entire documents through concatenation of word signatures or superimposed coding. Signature files provide a quick link between queries and documents but have lower accuracy than inverted files, which are generally better for information retrieval applications.
Term weighting assigns a weight to terms in documents to quantify their importance in describing the document's contents. Weights are higher for terms that occur frequently in a document but rarely in other documents. Term frequency in a document and inverse document frequency are used to calculate TF-IDF weights. Term occurrences may be correlated, so term weights should reflect their correlation. For example, terms like "computer" and "network" often appear together in documents about computer networks.
This document provides an introduction to text mining and information retrieval. It discusses how text mining is used to extract knowledge and patterns from unstructured text sources. The key steps of text mining include preprocessing text, applying techniques like summarization and classification, and analyzing the results. Text databases and information retrieval systems are described. Various models and techniques for text retrieval are outlined, including Boolean, vector space, and probabilistic models. Evaluation measures like precision and recall are also introduced.
The document discusses different theories used in information retrieval systems. It describes cognitive or user-centered theories that model human information behavior and structural or system-centered theories like the vector space model. The vector space model represents documents and queries as vectors of term weights and compares similarities between queries and documents. It was first used in the SMART information retrieval system and involves assigning term vectors and weights to documents based on relevance.
Automatic indexing is the process of analyzing documents to extract information to be included in an index. This can be done through statistical, natural language, concept-based, or hypertext linkage techniques. Statistical techniques are the most common, identifying words and phrases to index documents. Natural language techniques perform additional parsing of text. Concept indexing correlates words to concepts, while hypertext linkages create connections between documents. The goal of automatic indexing is to preprocess documents to allow for relevant search results by representing concepts in the index.
The document discusses probabilistic retrieval models in information retrieval. It provides an overview of older models like Boolean retrieval and vector space models. The main focus is on probabilistic models like BM25 and language models. It explains key concepts in probabilistic IR like the probability ranking principle, using Bayes' rule to estimate the probability that a document is relevant given features of the document, and estimating probabilities based on the frequencies of terms in relevant documents. The goal is to rank documents based on the probability of relevance to the query.
The document discusses the vector space model used in information retrieval. It explains that documents and queries are represented as weighted vectors in a multidimensional space. Similar vectors are close to each other. The weights used are usually tf-idf, which considers both the frequency of a term within a document and its rarity across documents. Documents are ranked based on the similarity between their vector representation and the query vector.
Probabilistic information retrieval models & systemsSelman Bozkır
The document discusses probabilistic information retrieval and Bayesian approaches. It introduces concepts like conditional probability, Bayes' theorem, and the probability ranking principle. It explains how probabilistic models estimate the probability of relevance between a document and query by representing them as term sets and making probabilistic assumptions. The goal is to rank documents by the probability of relevance to present the most likely relevant documents first.
The document discusses various aspects of term weighting which is important for text retrieval systems, including term frequency, document frequency, inverse document frequency, and how they are used to calculate TF-IDF weights for terms. It also covers stoplists, stemming, and the bag-of-words model which represents text as vectors of word occurrences without considering word order. Term weighting schemes play a major role in the similarity measures used by information retrieval systems to determine document relevance.
This document discusses evaluation methods for information retrieval systems. It begins by outlining different types of evaluation, including retrieval effectiveness, efficiency, and user-based evaluation. It then focuses on retrieval effectiveness, describing commonly used measures like precision, recall, and discounted cumulative gain. It discusses how these measures are calculated and their limitations. The document also introduces other evaluation metrics like R-precision, average precision, and normalized discounted cumulative gain that provide single value assessments of system performance.
The document discusses probabilistic retrieval models in information retrieval. It introduces three influential probabilistic models: (1) Maron and Kuhns' 1960 model which calculates the probability of relevance based on historical user data; (2) Salton's model which estimates the probability of term occurrence in relevant documents; (3) A model that ranks documents by the probability of relevance and considers retrieval as a decision between costs of retrieving non-relevant vs. not retrieving relevant documents. The document provides background on the development of probabilistic IR models and challenges of estimating probabilities for evaluation.
Ppt evaluation of information retrieval systemsilambu111
The document discusses the evaluation of information retrieval systems. Evaluation is defined as systematically determining a subject's merit using a set of standards. The main purposes of evaluation are to compare the performance of different systems, assess how well systems meet their goals, and identify ways to improve effectiveness. Evaluation can consider managerial or user viewpoints. Common criteria include recall, precision, fallout, generality, effectiveness, efficiency, usability, satisfaction, and cost. Recall measures the proportion of relevant documents retrieved while precision measures the proportion of retrieved documents that are relevant. Evaluation helps identify ways to improve information retrieval system performance.
This document provides a full syllabus with questions and answers related to the course "Information Retrieval" including definitions of key concepts, the historical development of the field, comparisons between information retrieval and web search, applications of IR, components of an IR system, and issues in IR systems. It also lists examples of open source search frameworks and performance measures for search engines.
This document provides an overview of an information retrieval system. It defines an information retrieval system as a system capable of storing, retrieving, and maintaining information such as text, images, audio, and video. The objectives of an information retrieval system are to minimize the overhead for a user to locate needed information. The document discusses functions like search, browse, indexing, cataloging, and various capabilities to facilitate querying and retrieving relevant information from the system.
The document summarizes a technical seminar on web-based information retrieval systems. It discusses information retrieval architecture and approaches, including syntactical, statistical, and semantic methods. It also covers web search analysis techniques like web structure analysis, content analysis, and usage analysis. The document outlines the process of web crawling and types of crawlers. It discusses challenges of web structure, crawling and indexing, and searching. Finally, it concludes that as unstructured online information grows, information retrieval techniques must continue to improve to leverage this data.
The document discusses the World Wide Web and information retrieval on the web. It provides background on how the web was developed by Tim Berners-Lee in 1990 using HTML, HTTP, and URLs. It then discusses some key differences in information retrieval on the web compared to traditional library systems, including the presence of hyperlinks, heterogeneous content, duplication of content, exponential growth in the number of documents, and lack of stability. It also summarizes some challenges in web search including the expanding nature of the web, dynamically generated content, influence of monetary contributions on search results, and search engine spamming.
Data mining involves multiple steps in the knowledge discovery process including data cleaning, integration, selection, transformation, mining, and pattern evaluation. It has various functionalities including descriptive mining to characterize data, predictive mining for inference, and different mining techniques like classification, association analysis, clustering, and outlier analysis.
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.
This document provides an overview of information retrieval models. It begins with definitions of information retrieval and how it differs from data retrieval. It then discusses the retrieval process and logical representations of documents. A taxonomy of IR models is presented including classic, structured, and browsing models. Boolean, vector, and probabilistic models are explained as examples of classic models. The document concludes with descriptions of ad-hoc retrieval and filtering tasks and formal characteristics of IR models.
Parallel and Distributed Information Retrieval Systemvimalsura
This document discusses parallel and distributed information retrieval. It describes how parallel architectures like MIMD can be used to accelerate search over very large document collections by distributing the work across multiple processors. Two main approaches to parallelism are covered: building new parallel algorithms or adapting existing techniques. Common ways to partition data for parallel indexing and search are discussed, including document partitioning and term partitioning. Specific data structures like inverted files, suffix arrays, and signature files are examined in terms of how they can be adapted for parallel and distributed retrieval architectures.
This document discusses evaluation in information retrieval. It describes standard test collections which consist of a document collection, queries on the collection, and relevance judgments. It also discusses various evaluation measures used in information retrieval like precision, recall, F-measure, mean average precision, and kappa statistic which measure reliability of relevance judgments. R-precision and normalized discounted cumulative gain are also summarized as important single number evaluation measures.
The document discusses signature files, which are used for document retrieval. A signature file creates a compressed representation or "signature" for each document in a database. These signatures are stored in hash tables to allow easy retrieval of matching documents for user queries. Signatures can represent words using triplets of characters and a hash function, or entire documents through concatenation of word signatures or superimposed coding. Signature files provide a quick link between queries and documents but have lower accuracy than inverted files, which are generally better for information retrieval applications.
Term weighting assigns a weight to terms in documents to quantify their importance in describing the document's contents. Weights are higher for terms that occur frequently in a document but rarely in other documents. Term frequency in a document and inverse document frequency are used to calculate TF-IDF weights. Term occurrences may be correlated, so term weights should reflect their correlation. For example, terms like "computer" and "network" often appear together in documents about computer networks.
This document provides an introduction to text mining and information retrieval. It discusses how text mining is used to extract knowledge and patterns from unstructured text sources. The key steps of text mining include preprocessing text, applying techniques like summarization and classification, and analyzing the results. Text databases and information retrieval systems are described. Various models and techniques for text retrieval are outlined, including Boolean, vector space, and probabilistic models. Evaluation measures like precision and recall are also introduced.
The document discusses different theories used in information retrieval systems. It describes cognitive or user-centered theories that model human information behavior and structural or system-centered theories like the vector space model. The vector space model represents documents and queries as vectors of term weights and compares similarities between queries and documents. It was first used in the SMART information retrieval system and involves assigning term vectors and weights to documents based on relevance.
Automatic indexing is the process of analyzing documents to extract information to be included in an index. This can be done through statistical, natural language, concept-based, or hypertext linkage techniques. Statistical techniques are the most common, identifying words and phrases to index documents. Natural language techniques perform additional parsing of text. Concept indexing correlates words to concepts, while hypertext linkages create connections between documents. The goal of automatic indexing is to preprocess documents to allow for relevant search results by representing concepts in the index.
The document discusses probabilistic retrieval models in information retrieval. It provides an overview of older models like Boolean retrieval and vector space models. The main focus is on probabilistic models like BM25 and language models. It explains key concepts in probabilistic IR like the probability ranking principle, using Bayes' rule to estimate the probability that a document is relevant given features of the document, and estimating probabilities based on the frequencies of terms in relevant documents. The goal is to rank documents based on the probability of relevance to the query.
The document discusses the vector space model used in information retrieval. It explains that documents and queries are represented as weighted vectors in a multidimensional space. Similar vectors are close to each other. The weights used are usually tf-idf, which considers both the frequency of a term within a document and its rarity across documents. Documents are ranked based on the similarity between their vector representation and the query vector.
The document discusses the vector space model used in information retrieval. It explains that documents and queries are represented as weighted vectors in a high dimensional vector space. Similarities between queries and documents are calculated to rank documents by relevance. Weights are often calculated using TF-IDF, which considers the frequency of terms within documents and across collections. Documents with vector representations closer to the query vector are considered more relevant.
The document discusses two main types of retrieval models: Boolean models which use set theory and vector space models which use statistical and algebraic approaches. Vector space models represent documents and queries as vectors of keywords weighted by factors like term frequency and inverse document frequency. Similarity between document and query vectors is calculated using measures like the inner product or cosine similarity to retrieve and rank documents.
The document summarizes a student project analyzing restaurant data using Python. It includes an introduction to the project goals, dataset, data mining techniques, and machine learning algorithms used. Specifically, the project aims to collect a restaurant dataset from Kaggle, apply knowledge in data mining and machine learning using Python, and perform classification, regression, and prediction tasks. Key algorithms discussed include linear regression, covariance, standard deviation, and prediction using support vector machines.
This document provides an overview of various information retrieval models and techniques used in search engines, including:
- Boolean, vector space, probabilistic models like BM25, and language models are described as older retrieval models.
- Learning to rank uses machine learning techniques to optimize ranking functions using features and training data.
- Relevance feedback, query likelihood models, and pseudo-relevance feedback are discussed as techniques for improving retrieval effectiveness by incorporating user feedback.
This document discusses vector space retrieval models. It describes how documents and queries are represented as vectors in a common vector space based on terms. Terms are weighted using metrics like term frequency (TF) and inverse document frequency (IDF) to determine importance. The cosine similarity measure is used to calculate similarity between document and query vectors and rank results by relevance. While simple and effective in practice, vector space models have limitations like missing semantic and syntactic information.
This document discusses different information retrieval models including the Boolean model, vector space model, and probabilistic model. It focuses on describing the Boolean model and its drawbacks. Term frequency-inverse document frequency (TF-IDF) weighting is explained as a way to assign weights to terms based on frequency and document distribution. Cosine similarity is presented as a common way to measure similarity between a document vector and query vector in the vector space model.
Document ranking using qprp with concept of multi dimensional subspacePrakash Dubey
This presentation discusses a project titled "Document Ranking Using QPRP with Concept of Multi-Dimensional Subspace". It was presented by Prakash Kumar Dubey and guided by Mr. Sourish Dhar and Mr. Bhagaban Swain of the Department of IT. The presentation provides an overview of the project, including an introduction to information retrieval, classical IR models such as Boolean, vector space, and probabilistic models. It then discusses quantum probability and how it can be applied to document ranking. The presentation outlines the proposed solution, data collection and implementation, and concludes with future work.
The document discusses information retrieval (IR) models, including the Boolean, vector space, and probabilistic models. The Boolean model represents documents and queries as sets of index terms and determines relevance through binary term presence, while the vector space model represents documents and queries as weighted vectors in a multidimensional space and ranks documents by calculating similarity between document and query vectors. The probabilistic model determines relevance probabilities based on the likelihood of terms appearing in relevant vs. non-relevant documents.
Multimodal Searching and Semantic Spaces: ...or how to find images of Dalmati...Jonathon Hare
Tutorial at the "Reality of the Semantic Gap in Image Retrieval" tutorial at the first international conference on Semantics And digital Media Technology (SAMT 2006). 6th December 2006.
The document summarizes the vector space model for scoring and ranking documents in response to a query in an information retrieval system. It explains that in this model, documents and queries are represented as vectors in a common vector space. The similarity between a document and query vector is measured by calculating the cosine similarity of the two vectors, which scores and ranks documents based on the terms they share with the query. It also describes how the vector space model allows retrieving the top K documents by relevance rather than using a Boolean retrieval model.
This document discusses classification and clustering techniques used in search engines. It covers classification tasks like spam detection, sentiment analysis, and ad classification. Naive Bayes and support vector machines are described as common classification approaches. Features, feature selection, and evaluation metrics for classifiers are also summarized.
Speech recognition using vector quantization through modified k means lbg alg...Alexander Decker
This document discusses speech recognition using vector quantization and proposes a modified K-meansLBG algorithm. It begins by introducing vector quantization techniques which are commonly used in speech recognition. It then describes the Linde-Buzo-Gray (LBG) algorithm and classical K-means algorithm for vector quantization before proposing a modified K-meansLBG algorithm. The paper claims this modified algorithm provides superior performance compared to the classical approaches. It evaluates the performance of the proposed algorithm through experimental work.
This document discusses various techniques for document clustering and retrieval, including cosine similarity, k-means clustering, hierarchical clustering, and the EM algorithm. Cosine similarity measures the similarity between document vectors based on the angle between them. K-means clustering partitions documents into k clusters to minimize intra-cluster similarity, while hierarchical clustering merges clusters in a dendogram based on similarity. The EM algorithm computes maximum likelihood estimates of document distributions. Evaluation of clustering assesses the quality based on intra-class and inter-class similarity.
This document discusses various techniques for document clustering and retrieval, including cosine similarity, k-means clustering, hierarchical clustering, and the EM algorithm. Cosine similarity measures the similarity between document vectors and is often used to compare documents, with higher values indicating more similar documents. K-means clustering partitions documents into k groups to minimize intra-cluster similarity, while hierarchical clustering creates a dendrogram of document clusters by progressively merging the most similar pairs. The EM algorithm computes maximum likelihood estimates for document clustering when data is incomplete. Evaluation of document clusters considers internal metrics like intra-cluster similarity and inter-cluster dissimilarity.
This document discusses various techniques for document clustering and retrieval, including cosine similarity, k-means clustering, hierarchical clustering, and the EM algorithm. Cosine similarity measures the similarity between document vectors and is often used to compare documents, with higher values indicating more similar documents. K-means clustering partitions documents into k groups to minimize intra-cluster similarity, while hierarchical clustering creates a dendrogram of document clusters by progressively merging the most similar groups. The EM algorithm computes maximum likelihood estimates for document clustering when data is incomplete. Evaluation of document clusters considers internal metrics like intra-cluster similarity and inter-cluster dissimilarity.
A Visual Exploration of Distance, Documents, and DistributionsRebecca Bilbro
The document discusses various distance metrics that can be used to quantify similarity between text documents for machine learning applications. It explains challenges in modeling text data due to its high dimensionality and sparse distributions. It then summarizes distance metrics available in Scikit-Learn and SciPy that can be used, including Euclidean, Manhattan, Chebyshev, Minkowski, Mahalanobis, Cosine, Canberra, Jaccard, and Hamming distances. It provides examples applying t-SNE visualization to embed documents from three text corpora using different distance metrics to understand how the choice of distance metric impacts the resulting visualizations.
Machine learning often requires us to think spatially and make choices about what it means for two instances to be close or far apart. So which is best - Euclidean? Manhattan? Cosine? It all depends! In this talk, we'll explore open source tools and visual diagnostic strategies for picking good distance metrics when doing machine learning on text.
The document discusses several information retrieval models including the Boolean, vector space, and probabilistic models. It provides details on how each model represents documents and queries, defines relevance, and ranks documents in response to queries. Specifically, it describes:
1) The Boolean model uses exact matching to retrieve only documents that satisfy a Boolean query, but does not rank results.
2) The vector space model represents documents and queries as vectors of term weights and ranks documents based on their similarity to the query vector using measures like cosine similarity.
3) Term frequency-inverse document frequency (TF-IDF) is discussed as a method to weight terms based on their importance.
Similar to Boolean,vector space retrieval Models (20)
Creativity for Innovation and SpeechmakingMattVassar1
Tapping into the creative side of your brain to come up with truly innovative approaches. These strategies are based on original research from Stanford University lecturer Matt Vassar, where he discusses how you can use them to come up with truly innovative solutions, regardless of whether you're using to come up with a creative and memorable angle for a business pitch--or if you're coming up with business or technical innovations.
Get Success with the Latest UiPath UIPATH-ADPV1 Exam Dumps (V11.02) 2024yarusun
Are you worried about your preparation for the UiPath Power Platform Functional Consultant Certification Exam? You can come to DumpsBase to download the latest UiPath UIPATH-ADPV1 exam dumps (V11.02) to evaluate your preparation for the UIPATH-ADPV1 exam with the PDF format and testing engine software. The latest UiPath UIPATH-ADPV1 exam questions and answers go over every subject on the exam so you can easily understand them. You won't need to worry about passing the UIPATH-ADPV1 exam if you master all of these UiPath UIPATH-ADPV1 dumps (V11.02) of DumpsBase. #UIPATH-ADPV1 Dumps #UIPATH-ADPV1 #UIPATH-ADPV1 Exam Dumps
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
Artificial Intelligence (AI) has revolutionized the creation of images and videos, enabling the generation of highly realistic and imaginative visual content. Utilizing advanced techniques like Generative Adversarial Networks (GANs) and neural style transfer, AI can transform simple sketches into detailed artwork or blend various styles into unique visual masterpieces. GANs, in particular, function by pitting two neural networks against each other, resulting in the production of remarkably lifelike images. AI's ability to analyze and learn from vast datasets allows it to create visuals that not only mimic human creativity but also push the boundaries of artistic expression, making it a powerful tool in digital media and entertainment industries.
1. Ms. T. Primya
Assistant Professor
Department of Computer Science and Engineering
Dr. N. G. P. Institute of Technology
Coimbatore
2. A retrieval model can be a description of either the
computational process or the human process of
retrieval
the process of choosing documents for retrieval
the process by which information needs are first
articulated and then refined.
3. Boolean Models
Vector Space Models
Probabilistic Models
Models based on Belief nets
Models based on Language Models
4. A document is represented as a set of keywords.
Index terms are considered to be either present or absent in a
document and to provide equal evidence with respect to information
needs.
Queries are Boolean expressions of keywords, connected by AND,
OR, and NOT, including the use of brackets to indicate scope.
[[Rio & Brazil] | [Hilo & Hawaii]] & hotel & !Hilton]
Output: Document is relevant or not. No partial matches or ranking.
5. User need: I’m interested in learning about vitamins
other than vitamin e that are anti-oxidants.
User’s Boolean query: antioxidant AND vitamin
AND NOT vitamin e
6. For each retrieval model, there explicit three
components:
Document representation d
Query q
Ranking function R(d, q)
7. An IR strategy is a technique by which a relevance
measure is obtained between a query and a document.
Retrieve documents that make the query true.
8. Boolean-Documents either match or don’t.
Good for expert users with precise understanding of
their needs and of the collection.
Also good for applications: Applications can easily
consume 1000s of results.
Not good for the majority of users
This is particularly true of web search.
9. Boolean queries often have either too few or too many results.
Query 1
standard AND user AND dlink AND 650
→ 200,000 hits Feast!
Query 2
standard AND user AND dlink AND 650 AND no AND card AND found
→ 0 hits Famine!
In Boolean retrieval, it takes a lot of skill to come up with a query that
produces a manageable number of hits.
In ranked retrieval, “feast or famine” is less of a problem.
Condition: Results that are more relevant are ranked higher than results that
are less relevant. (i.e., the ranking algorithm works.)
10. A commonly used measure of overlap of two sets
Let A and B be two sets
Jaccard coefficient:
jaccard(A,B) = |A∩B| |A∪B|
jaccard(A,A) = 1
jaccard(A,B) = 0 if A∩B = 0
A and B don’t have to be the same size. Always
assigns a number between 0 and 1.
11. What is the query-document match score that the Jaccard
coefficient computes for:
Query
“ides of March”
Document
“Caesar died in March”
jaccard(q,d) = 1/6
12. It doesn’t consider term frequency (how many
occurrences a term has).
Rare terms are more informative than frequent terms.
Jaccard does not consider this information.
13. Advantages
Can use very restrictive search
Makes experienced users happy
Clear formalism
Simplicity
It is still used in small scale searches like searching e-
mails, files from local hard drives
14. Disadvantages
Simple queries do not work well.
Complex query language, confusing to end users
Difficult to control the number of documents
retrieved.
◦ All matched documents will be returned.
Difficult to rank output.
◦ All matched documents logically satisfy the query.
Difficult to perform relevance feedback.
◦ If a document is identified by the user as relevant or
irrelevant, how should the query be modified?
15. Vector space model or term vector model is an
algebraic model for representing text documents (and
any objects, in general) as vectors of identifiers, such
as, for example, index terms.
It is used in information filtering, information
retrieval, indexing and relevancy rankings.
16. The basis vectors correspond to the dimensions or
directions of the vector space
17. A vector is a point in a vector space and has length
(from the origin to the point) and direction
18. A 2-dimensional vector can be written as [x, y]
A 3-dimensional vector can be written as [x, y, z]
19. Let V denote the size of the indexed vocabulary
Any arbitrary span of text (i.e., a document, or a
query) can be represented as a vector in V-
dimensional space
let’s assume three index terms: dog, bite, man (i.e.,
V=3)
20. 1 = the term appears at least once
0 = the term does not appear
21. A query is a vector in V-dimensional space, where
V is the number of terms in the vocabulary
22. The vector space model ranks documents based on
the vector-space similarity between the query vector
and the document vector
There are many ways to compute the similarity
between two vectors
One way is to compute the inner product
24. Pros and Cons
The inner-product doesn’t account for the fact that
documents have widely varying lengths
All things being equal, longer documents are more
likely to have the query-terms
So, the inner-product favours long documents
25. Document represented as a vector:
d =< d1; d2; …. dn >
Query represented as a vector: q =< q1; q2;…. qn >
Ranking function (retrieval status value):
26. The cosine similarity between two vectors (or two
documents on the Vector Space) is a measure that
calculates the cosine of the angle between them.
the cosine similarity equation is to solve the equation
of the dot product for the :
The numerator is the inner product
The denominator is the product of the two vector-
lengths
Ranges from 0 to 1 (equals 1 if the vectors are
identical)
27. a =[1, 2, 3]
b =[4,-5,6]
a with b is dpab = 1*4 + 2*-5 + 3*6 = 12
a with itself is dpaa = 1*1 + 2*2 + 3*3 = 14
b with itself is dpbb = 4*4 + -5*-5 + 6*6 = 77
la = (dpaa) ½ = (14) ½ = 3.74; i.e., the length of a.
lb = (dpbb) ½ = (77)½ = 8.77; i.e., the length of b.
la*lb = (dpaa) ½ * (dpbb) ½ = 32.83;
i.e., the length product (lpab) of a and b.
29. The vector space model procedure can be divided
into three stages.
The first stage is the document indexing where
content bearing terms are extracted from the
document text.
The second stage is the weighting of the indexed
terms to enhance retrieval of document relevant to the
user.
The last stage ranks the document with respect to the
query according to a similarity measure.
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