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 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.
Boolean,vector space retrieval Models Primya Tamil
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 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.
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 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.
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 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.
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.
Boolean,vector space retrieval Models Primya Tamil
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 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.
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 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.
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 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.
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.
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.
This document discusses evaluating hypotheses and estimating hypothesis accuracy. It provides the following key points:
- The accuracy of a hypothesis estimated from a training set may be different from its true accuracy due to bias and variance. Testing the hypothesis on an independent test set provides an unbiased estimate.
- Given a hypothesis h that makes r errors on a test set of n examples, the sample error r/n provides an unbiased estimate of the true error. The variance of this estimate depends on r and n based on the binomial distribution.
- For large n, the binomial distribution can be approximated by the normal distribution. Confidence intervals for the true error can then be determined based on the sample error and standard deviation
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.
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.
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.
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.
Bayesian classification is a statistical classification method that uses Bayes' theorem to calculate the probability of class membership. It provides probabilistic predictions by calculating the probabilities of classes for new data based on training data. The naive Bayesian classifier is a simple Bayesian model that assumes conditional independence between attributes, allowing faster computation. Bayesian belief networks are graphical models that represent dependencies between variables using a directed acyclic graph and conditional probability tables.
Lazy learning is a machine learning method where generalization of training data is delayed until a query is made, unlike eager learning which generalizes before queries. K-nearest neighbors and case-based reasoning are examples of lazy learners, which store training data and classify new data based on similarity. Case-based reasoning specifically stores prior problem solutions to solve new problems by combining similar past case solutions.
This document provides an introduction to First Order Predicate Logic (FOPL). It defines FOPL as symbolized reasoning where sentences are broken down into subjects and predicates. FOPL is more expressive than propositional logic and allows representing almost any English sentence. It discusses features of FOPL such as generalization of propositional logic and more powerful representation. Applications of FOPL include presenting arguments, determining validity, and formulating theories. The document also defines key terms in FOPL such as constants, variables, functions, and predicates.
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 outlines techniques for representing uncertainty in expert systems, including Bayesian reasoning and certainty factors theory. It discusses sources of uncertain knowledge, probabilistic reasoning using Bayes' rule, and an example of computing posterior probabilities of hypotheses given observed evidence. Certainty factors theory is presented as an alternative to Bayesian reasoning that uses numerical factors between -1 and 1 to represent degrees of belief.
TF-IDF, short for Term Frequency - Inverse Document Frequency, is a text mining technique, that gives a numeric statistic as to how important a word is to a document in a collection or corpus. This is a technique used to categorize documents according to certain words and their importance to the document
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.
This document discusses decision trees and the ID3 algorithm for generating decision trees. It explains that a decision tree classifies examples based on their attributes through a series of questions or rules. The ID3 algorithm uses information gain to choose the most informative attributes to split on at each node, resulting in a tree that maximizes classification accuracy. Some drawbacks of decision trees are that they can only handle nominal attributes and may not be robust to noisy data.
This document discusses the key primitives, languages, and architectures for data mining systems. It describes five primitives for specifying a data mining task: task-relevant data, kind of knowledge to be mined, background knowledge, interestingness measures, and knowledge presentation. It also discusses data mining query languages like DMQL and system architectures ranging from no coupling to tight coupling with database/data warehouse systems.
The document provides an overview of compilers by discussing:
1. Compilers translate source code into executable target code by going through several phases including lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
2. An interpreter directly executes source code statement by statement while a compiler produces target code as translation. Compiled code generally runs faster than interpreted code.
3. The phases of a compiler include a front end that analyzes the source code and produces intermediate code, and a back end that optimizes and generates the target code.
Information Retrieval-4(inverted index_&_query handling)Jeet Das
The document describes the process of creating an inverted index to support keyword searching of documents. It discusses storing term postings lists that map terms to the documents that contain them. It also describes techniques like skip pointers, phrase queries, and proximity searches to improve query processing efficiency and support more complex search needs. Precision, recall, and f-score metrics for evaluating information retrieval systems are also summarized.
This document provides an overview of PAC (Probably Approximately Correct) learning theory. It discusses how PAC learning relates the probability of successful learning to the number of training examples, complexity of the hypothesis space, and accuracy of approximating the target function. Key concepts explained include training error vs true error, overfitting, the VC dimension as a measure of hypothesis space complexity, and how PAC learning bounds can be derived for finite and infinite hypothesis spaces based on factors like the training size and VC dimension.
Relationship Among Token, Lexeme & PatternBharat Rathore
Relationship among Token, Lexeme and Pattern
Outline
Token
Lexeme
Pattern
Relationship
Tokens : A token is sequence of characters that can be treated
as a unit/single logical entity.
Examples
Keywords
Examples : for, while, if etc.
Identifier
Examples : Variable name, function name, etc.
Operators
Examples : '+', '++', '-' etc.
Separators
Examples : ',' ';' etc.
Pattern
Pattern is a rule describing all those lexemes that can represent a particular token in a source language.
Lexeme
It is a sequence of characters in the source program that is matched by the pattern for a token.
Example : “float”, “=“, “223”, “;”
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.
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.
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.
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.
This document discusses evaluating hypotheses and estimating hypothesis accuracy. It provides the following key points:
- The accuracy of a hypothesis estimated from a training set may be different from its true accuracy due to bias and variance. Testing the hypothesis on an independent test set provides an unbiased estimate.
- Given a hypothesis h that makes r errors on a test set of n examples, the sample error r/n provides an unbiased estimate of the true error. The variance of this estimate depends on r and n based on the binomial distribution.
- For large n, the binomial distribution can be approximated by the normal distribution. Confidence intervals for the true error can then be determined based on the sample error and standard deviation
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.
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.
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.
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.
Bayesian classification is a statistical classification method that uses Bayes' theorem to calculate the probability of class membership. It provides probabilistic predictions by calculating the probabilities of classes for new data based on training data. The naive Bayesian classifier is a simple Bayesian model that assumes conditional independence between attributes, allowing faster computation. Bayesian belief networks are graphical models that represent dependencies between variables using a directed acyclic graph and conditional probability tables.
Lazy learning is a machine learning method where generalization of training data is delayed until a query is made, unlike eager learning which generalizes before queries. K-nearest neighbors and case-based reasoning are examples of lazy learners, which store training data and classify new data based on similarity. Case-based reasoning specifically stores prior problem solutions to solve new problems by combining similar past case solutions.
This document provides an introduction to First Order Predicate Logic (FOPL). It defines FOPL as symbolized reasoning where sentences are broken down into subjects and predicates. FOPL is more expressive than propositional logic and allows representing almost any English sentence. It discusses features of FOPL such as generalization of propositional logic and more powerful representation. Applications of FOPL include presenting arguments, determining validity, and formulating theories. The document also defines key terms in FOPL such as constants, variables, functions, and predicates.
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 outlines techniques for representing uncertainty in expert systems, including Bayesian reasoning and certainty factors theory. It discusses sources of uncertain knowledge, probabilistic reasoning using Bayes' rule, and an example of computing posterior probabilities of hypotheses given observed evidence. Certainty factors theory is presented as an alternative to Bayesian reasoning that uses numerical factors between -1 and 1 to represent degrees of belief.
TF-IDF, short for Term Frequency - Inverse Document Frequency, is a text mining technique, that gives a numeric statistic as to how important a word is to a document in a collection or corpus. This is a technique used to categorize documents according to certain words and their importance to the document
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.
This document discusses decision trees and the ID3 algorithm for generating decision trees. It explains that a decision tree classifies examples based on their attributes through a series of questions or rules. The ID3 algorithm uses information gain to choose the most informative attributes to split on at each node, resulting in a tree that maximizes classification accuracy. Some drawbacks of decision trees are that they can only handle nominal attributes and may not be robust to noisy data.
This document discusses the key primitives, languages, and architectures for data mining systems. It describes five primitives for specifying a data mining task: task-relevant data, kind of knowledge to be mined, background knowledge, interestingness measures, and knowledge presentation. It also discusses data mining query languages like DMQL and system architectures ranging from no coupling to tight coupling with database/data warehouse systems.
The document provides an overview of compilers by discussing:
1. Compilers translate source code into executable target code by going through several phases including lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
2. An interpreter directly executes source code statement by statement while a compiler produces target code as translation. Compiled code generally runs faster than interpreted code.
3. The phases of a compiler include a front end that analyzes the source code and produces intermediate code, and a back end that optimizes and generates the target code.
Information Retrieval-4(inverted index_&_query handling)Jeet Das
The document describes the process of creating an inverted index to support keyword searching of documents. It discusses storing term postings lists that map terms to the documents that contain them. It also describes techniques like skip pointers, phrase queries, and proximity searches to improve query processing efficiency and support more complex search needs. Precision, recall, and f-score metrics for evaluating information retrieval systems are also summarized.
This document provides an overview of PAC (Probably Approximately Correct) learning theory. It discusses how PAC learning relates the probability of successful learning to the number of training examples, complexity of the hypothesis space, and accuracy of approximating the target function. Key concepts explained include training error vs true error, overfitting, the VC dimension as a measure of hypothesis space complexity, and how PAC learning bounds can be derived for finite and infinite hypothesis spaces based on factors like the training size and VC dimension.
Relationship Among Token, Lexeme & PatternBharat Rathore
Relationship among Token, Lexeme and Pattern
Outline
Token
Lexeme
Pattern
Relationship
Tokens : A token is sequence of characters that can be treated
as a unit/single logical entity.
Examples
Keywords
Examples : for, while, if etc.
Identifier
Examples : Variable name, function name, etc.
Operators
Examples : '+', '++', '-' etc.
Separators
Examples : ',' ';' etc.
Pattern
Pattern is a rule describing all those lexemes that can represent a particular token in a source language.
Lexeme
It is a sequence of characters in the source program that is matched by the pattern for a token.
Example : “float”, “=“, “223”, “;”
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.
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.
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.
The vector space model (VSM) represents documents as vectors of identifiers such as words, where each unique word corresponds to a dimension. Documents are broken down and represented as vectors based on word frequency. Queries are also represented as vectors, and similarity measures such as cosine similarity are used to compare document and query vectors and retrieve the most relevant documents. Variations of the basic VSM include removing common words, weighting terms based on frequency and document distribution, and using tf-idf to emphasize important words.
IRJET - Document Comparison based on TF-IDF MetricIRJET Journal
This document discusses comparing documents based on the TF-IDF metric and cosine similarity. It begins by representing documents as vectors of terms weighted by TF-IDF. Cosine similarity is then used to measure the similarity between document vectors, with values ranging from 0 (completely dissimilar) to 1 (identical). The document demonstrates this approach on 5 sample documents from different domains, showing their pairwise cosine similarities. Comparing documents based on TF-IDF and cosine similarity allows analyzing relationships between documents in large corpora.
T EXT M INING AND C LASSIFICATION OF P RODUCT R EVIEWS U SING S TRUCTURED S U...csandit
Text mining and Text classification are the two pro
minent and challenging tasks in the field of
Machine learning. Text mining refers to the process
of deriving high quality and relevant
information from text, while Text classification de
als with the categorization of text documents
into different classes. The real challenge in these
areas is to address the problems like handling
large text corpora, similarity of words in text doc
uments, and association of text documents with
a subset of class categories. The feature extractio
n and classification of such text documents
require an efficient machine learning algorithm whi
ch performs automatic text classification.
This paper describes the classification of product
review documents as a multi-label
classification scenario and addresses the problem u
sing Structured Support Vector Machine.
The work also explains the flexibility and performan
ce of the proposed approach for e
fficient text classification.
This document discusses text mining and summarizes some key differences between text mining and data mining. Text mining, also known as text data mining or knowledge discovery in textual databases, is the process of analyzing text to identify novel information from a collection of documents. Unlike data mining which directly analyzes structured numeric data, text mining applies natural language processing techniques to discover new information from unstructured text data. The document then provides an overview of common text retrieval methods like the Boolean model and document ranking, and discusses measures used to evaluate text retrieval systems like precision and recall.
Presentation of the main IR models
Presentation of our submission to TREC KBA 2014 (Entity oriented information retrieval), in partnership with Kware company (V. Bouvier, M. Benoit)
This document provides an introduction to information retrieval systems and their main components. It discusses how IR systems aim to find relevant documents from a large collection in response to a user's information need. The key processes involved are document indexing to represent contents, query formulation, retrieval of relevant documents, and system evaluation. Indexing involves selecting important keywords from documents and assigning them weights. Various retrieval models are described for comparing document and query representations, such as vector space and probabilistic models. The document also discusses challenges in document representation, query processing, and evaluating system effectiveness.
How to Measure Document Similarity and Build Text Classifiers: A First Look at Term Frequency-Inverse Document Frequency (TF-IDF) Representations
Text data is potentially valuable for many data science projects but working with text is different from working with structured data. One representation of text that has worked well for many text mining and machine learning applications is the term frequency - inverse document frequency (TF-IDF) vector. In spite of the long winded name, this method is easy to understand, performs well in many applications, and has been implemented in commonly used data science tools. This presentation will introduce TF-IDF and show examples of how to use TF-IDF for document classification and measuring the similarity between documents.
This presentation does not assume any background in text mining or natural language processing. Examples will use Python.
DOMAIN KEYWORD EXTRACTION TECHNIQUE: A NEW WEIGHTING METHOD BASED ON FREQUENC...cscpconf
On-line text documents rapidly increase in size with the growth of World Wide Web. To manage such a huge amount of texts,several text miningapplications came into existence. Those
applications such as search engine, text categorization,summarization, and topic detection arebased on feature extraction.It is extremely time consuming and difficult task to extract keyword or feature manually.So an automated process that extracts keywords or features needs to be
established.This paper proposes a new domain keyword extraction technique that includes a new weighting method on the base of the conventional TF•IDF. Term frequency-Inverse
document frequency is widely used to express the documentsfeature weight, which can’t reflect the division of terms in the document, and then can’t reflect the significance degree and the difference between categories. This paper proposes a new weighting method to which a new weight is added to express the differences between domains on the base of original TF•IDF.The extracted feature can represent the content of the text better and has a better distinguished
This document provides lecture notes on information retrieval systems. It covers key concepts like precision and recall, different retrieval strategies including vector space model and probabilistic models, and retrieval utilities. The vector space model represents documents and queries as vectors in a shared space and calculates similarity using cosine similarity. Probabilistic models assign probabilities to terms and documents and estimate relevance probabilities. The notes discuss term weighting schemes, inverted indexes to improve efficiency, and integrating structured data with text retrieval. The overall objective is for students to learn fundamental models and techniques for information storage and retrieval.
Vector space model12345678910111213.pptxsomeyamohsen2
The vector space model is an algebraic model for representing text documents and search queries as vectors. It represents documents and queries as vectors in a multidimensional space, where each unique term is a dimension. It allows documents and queries to be compared by determining the similarity between their vector representations. The vector space model involves representing documents as vectors of the words they contain and transforming these into numerical term-document matrices. This allows techniques like information retrieval and extraction to be applied.
Testing Different Log Bases for Vector Model Weighting Techniquekevig
Information retrieval systems retrieves relevant documents based on a query submitted by the user. The documents are initially indexed and the words in the documents are assigned weights using a weighting technique called TFIDF which is the product of Term Frequency (TF) and Inverse Document Frequency (IDF). TF represents the number of occurrences of a term in a document. IDF measures whether the term is common or rare across all documents. It is computed by dividing the total number of documents in the system by the number of documents containing the term and then computing the logarithm of the quotient. By default, we use base 10 to calculate the logarithm. In this paper, we are going to test this weighting technique by using a range of log bases from 0.1 to 100.0 to calculate the IDF. Testing different log bases for vector model weighting technique is to highlight the importance of understanding the performance of the system at different weighting values. We use the documents of MED, CRAN, NPL, LISA, and CISI test collections that scientists assembled explicitly for experiments in data information retrieval systems.
Testing Different Log Bases for Vector Model Weighting Techniquekevig
Information retrieval systems retrieves relevant documents based on a query submitted by the user. The documents are initially indexed and the words in the documents are assigned weights using a weighting technique called TFIDF which is the product of Term Frequency (TF) and Inverse Document Frequency (IDF). TF represents the number of occurrences of a term in a document. IDF measures whether the term is common or rare across all documents. It is computed by dividing the total number of documents in the system by the number of documents containing the term and then computing the logarithm of the quotient. By default, we use base 10 to calculate the logarithm. In this paper, we are going to test this weighting technique by using a range of log bases from 0.1 to 100.0 to calculate the IDF. Testing different log bases for vector model weighting technique is to highlight the importance of understanding the performance of the system at different weighting values. We use the documents of MED, CRAN, NPL, LISA, and CISI test collections that scientists assembled explicitly for experiments in data information retrieval systems.
The document discusses various techniques for information retrieval and language modeling approaches to IR, including:
- Clustering documents into similar groups to aid in retrieval
- Using term frequency-inverse document frequency (TF-IDF) to measure word importance in documents
- Language models that represent documents and queries as probability distributions over words
- Smoothing language models to address data sparsity issues
- Cluster-based scoring methods that incorporate information from query-relevant document clusters
Le développement du Web et des réseaux sociaux ou les numérisations massives de documents contribuent à un renouvellement des Sciences Humaines et Sociales, des études des patrimoines littéraires ou culturels, ou encore de la façon dont est exploitée la littérature scientifique en général.
Les humanités numériques, qui croisent diverses disciplines avec l’informatique, posent comme centrales les questions du volume des données, de leur diversité, de leur origine, de leur véracité ou de leur représentativité. Les informations sont véhiculées au sein de « documents » textuels (livres, pages Web, tweets...), audio, vidéo ou multimédia. Ils peuvent comporter des illustrations ou des graphiques.
Appréhender de telles ressources nécessite le développement d'approches informatiques robustes, capables de passer à l’échelle et adaptées à la nature fondamentalement ambiguë et variée des informations manipulées (langage naturel ou images à interpréter, points de vue multiples…).
Si les approches d’apprentissage statistique sont monnaie courante pour des tâches de classification ou d’extraction d’information, elles doivent faire face à des espaces vectoriels creux et de dimension très élevées (plusieurs millions), être en mesure d’exploiter des ressources (par exemple des lexiques ou des thesaurus) et tenir compte ou produire des annotations sémantiques qui devront pouvoir être réutilisées.
Pour faire face à ces enjeux, des infrastructures ont été créées telle HumaNum à l’échelle nationale, DARIAH ou CLARIN à l’échelle européenne et des recommandations établies à l’échelle mondiale telle que la TEI (Text Encoding Initiative). Des plateformes au service de l’information scientifique comme l’équipement d’excellence OpenEdition.org sont une autre brique essentielle pour la préservation et l’accès aux « Big Digital Humanities » mais aussi pour favoriser la reproductibilité et la compréhension des expérimentations et des résultats obtenus.
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.
This document provides an overview of the Introduction to Algorithms course, including the course modules and motivating problems. It introduces the Document Distance problem, which aims to define metrics to measure the similarity between documents based on word frequencies. It discusses an initial Python program ("docdist1.py") to calculate document distance that runs inefficiently due to quadratic time list concatenation. Profiling identifies this as the bottleneck. The solution is to use list extension, resulting in "docdist3.py". Further optimizations include using a dictionary to count word frequencies in constant time, creating "docdist4.py". The document outlines remaining opportunities like improving the word extraction and sorting algorithms.
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.
Post init hook in the odoo 17 ERP ModuleCeline George
In Odoo, hooks are functions that are presented as a string in the __init__ file of a module. They are the functions that can execute before and after the existing code.
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.
Decolonizing Universal Design for LearningFrederic Fovet
UDL has gained in popularity over the last decade both in the K-12 and the post-secondary sectors. The usefulness of UDL to create inclusive learning experiences for the full array of diverse learners has been well documented in the literature, and there is now increasing scholarship examining the process of integrating UDL strategically across organisations. One concern, however, remains under-reported and under-researched. Much of the scholarship on UDL ironically remains while and Eurocentric. Even if UDL, as a discourse, considers the decolonization of the curriculum, it is abundantly clear that the research and advocacy related to UDL originates almost exclusively from the Global North and from a Euro-Caucasian authorship. It is argued that it is high time for the way UDL has been monopolized by Global North scholars and practitioners to be challenged. Voices discussing and framing UDL, from the Global South and Indigenous communities, must be amplified and showcased in order to rectify this glaring imbalance and contradiction.
This session represents an opportunity for the author to reflect on a volume he has just finished editing entitled Decolonizing UDL and to highlight and share insights into the key innovations, promising practices, and calls for change, originating from the Global South and Indigenous Communities, that have woven the canvas of this book. The session seeks to create a space for critical dialogue, for the challenging of existing power dynamics within the UDL scholarship, and for the emergence of transformative voices from underrepresented communities. The workshop will use the UDL principles scrupulously to engage participants in diverse ways (challenging single story approaches to the narrative that surrounds UDL implementation) , as well as offer multiple means of action and expression for them to gain ownership over the key themes and concerns of the session (by encouraging a broad range of interventions, contributions, and stances).
bryophytes.pptx bsc botany honours second semester
Term weighting
1. Ms. T. Primya
Assistant Professor
Department of Computer Science and Engineering
Dr. N. G. P. Institute of Technology
Coimbatore
2. Term weighting is an important aspect of modern text
retrieval systems.
Terms are words, phrases, or any other indexing units
used to identify the contents of a text
Since different terms have different importance in a
text, an important indicator-the term weight is
associated with every term
3. The retrieval performance of the information retrieval
systems is largely dependent on similarity measures.
The term weighting scheme plays an major role for
the similarity measure
aij=gi*tij*dj
Where
gi is the global weight of the ith term
tij is the local weight of the ith term in the jth document
dj is the normalization factor for the jth document
4. Term Weight is used to rank the relevance
Parameters in calculating a weight for a document term or query term
Term Frequency (tf):
Term Frequency is the number of times a term i appears in
document j (tfij )
tf is dividing by the frequency of the most common term in the
document
tfij = fij / maxi{fij}
fij = frequency of term i in document j
5. Document Frequency (df): Number of documents a
term i appears in, (dfi).
Inverse Document Frequency (idf): A discriminating
measure for a term i in collection, i.e., how
discriminating term i is
(idfi) = log10(n / dfi),
Where, n is the number of document in the collection
dfi = number of documents in which term i appears
TF-IDF => wij = tfij*idfi = tfij* log10(n / dfi)
6. Given a document containing terms with given
frequencies A(3), B(2), C(1)
Assume document contains 10,000 documents and
document frequencies of these terms are A(50), B(1300),
C(250)
A: tf=3/3, idf=log(10000/50)=7.6, tf-idf=1*7.6=7.6
B: tf=2/3, idf=log(10000/1300)=2.9, tf-idf=2.0
C: tf=1/3, idf=log(10000/250)=5.3, tf-idf=1.8
7. Stoplists:
List of words to be ignored when processing documents,
since they give no useful information about content.
Stemming:(conflation)
Process of treating a set of words as all instances of the
same term
Ex: fights, fighting, fighter,..
Stem is fight
8. A bag-of-words model (BoW) is a way of extracting
features from text for use in modeling, such as with
machine learning algorithms.
A bag-of-words is a representation of text that describes the
occurrence of words within a document. It involves two
things:
A vocabulary of known words.
A measure of the presence of known words.
It is called a “bag” of words, because any information about
the order or structure of words in the document is discarded.
The model is only concerned with whether known words
occur in the document, not where in the document.
9. Step 1: Collect Data
It was the best of times,
it was the worst of times,
it was the age of wisdom,
it was the age of foolishness,
10. Step 2: Design the Vocabulary
The unique words here (ignoring case and punctuation) are:
“it”
“was”
“the”
“best”
“of”
“times”
“worst”
“age”
“wisdom”
“foolishness”
That is a vocabulary of 10 words from a corpus containing 24
words.
11. Step 3: Create Document Vectors
The next step is to score the words in each document.
The scoring of the document would look as follows
“it” = 1
“was” = 1
“the” = 1
“best” = 1
“of” = 1
“times” = 1
“worst” = 0
“age” = 0
“wisdom” = 0
“foolishness” = 0
12. Binary Vectors are:
“it was the best of times”=[1, 1, 1, 1, 1, 1, 0, 0, 0, 0]
"it was the worst of times" = [1, 1, 1, 0, 1, 1, 1, 0, 0, 0]
"it was the age of wisdom" = [1, 1, 1, 0, 1, 0, 0, 1, 1, 0]
"it was the age of foolishness" = [1, 1, 1, 0, 1, 0, 0, 1, 0, 1]
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