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.
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.
Functions of information retrival system(1)silambu111
The document discusses information retrieval systems. It defines information retrieval as the process of searching collections of documents to identify those dealing with a particular subject. Information retrieval systems aim to facilitate literature searching. They involve representing, storing, organizing, and providing access to information items so that users can easily find information of interest. Information retrieval draws from multiple disciplines and involves subsystems for documents, users, and searching/matching.
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.
This document provides an overview of an information retrieval system (IRS). It defines IRS as obtaining relevant information from a collection to meet a user's need. The IRS has three main components: a document subsystem for acquiring, representing and organizing data; a user subsystem for representing queries; and a search/retrieval subsystem for matching queries to documents. It describes the basic concepts like how a user enters a query that is scored and ranked to return relevant results, which can be iterated. The objectives are to highlight probabilistic models and establish relationships between popular techniques. The functions are to analyze information sources and queries to match and retrieve relevant items. [/SUMMARY]
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.
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.
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.
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.
Functions of information retrival system(1)silambu111
The document discusses information retrieval systems. It defines information retrieval as the process of searching collections of documents to identify those dealing with a particular subject. Information retrieval systems aim to facilitate literature searching. They involve representing, storing, organizing, and providing access to information items so that users can easily find information of interest. Information retrieval draws from multiple disciplines and involves subsystems for documents, users, and searching/matching.
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.
This document provides an overview of an information retrieval system (IRS). It defines IRS as obtaining relevant information from a collection to meet a user's need. The IRS has three main components: a document subsystem for acquiring, representing and organizing data; a user subsystem for representing queries; and a search/retrieval subsystem for matching queries to documents. It describes the basic concepts like how a user enters a query that is scored and ranked to return relevant results, which can be iterated. The objectives are to highlight probabilistic models and establish relationships between popular techniques. The functions are to analyze information sources and queries to match and retrieve relevant items. [/SUMMARY]
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.
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.
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.
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.
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 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.
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 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.
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 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.
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.
Indexing Techniques: Their Usage in Search Engines for Information RetrievalVikas Bhushan
1. The document discusses indexing techniques and their usage in modern search engines. It covers the transition from manual to automated indexing and different indexing methods.
2. Current trends in indexing and information retrieval are discussed such as XML indexing and its components. Future applications for indexers are also mentioned.
3. The conclusion emphasizes enhancements to indexing procedures like weighted indexing and linking of terms to improve retrieval of accurate information.
Information retrieval 13 alternative set theoretic modelsVaibhav Khanna
Alternative Set Theoretic Models
Fuzzy Set Model :a set theoretic model of document retrieval based on fuzzy theory.
Extended Boolean Model:a set theoretic model of document retrieval based on an extension of the classic Boolean model. The idea is to interpret partial matches as Euclidean distances represented in a vectorial space of index terms.
Broad introduction to information retrieval and web search, used to teaching at the Yahoo Bangalore Summer School 2013. Slides are a mash-up from my own and other people's presentations.
Information retrieval (IR) is the process of searching for and retrieving relevant documents from a large collection based on a user's query. Key aspects of IR include:
- Representing documents and queries in a way that allows measuring their similarity, such as the vector space model.
- Ranking retrieved documents by relevance to the query using factors like term frequency and inverse document frequency.
- Allowing for similarity-based retrieval where documents similar to a given document are retrieved.
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 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 talk features the basics behind the science of Information Retrieval with a story-mode on information and its various aspects. It then takes you through a quick journey into the process behind building of the search engine.
INTRODUCTION TO INFORMATION RETRIEVAL
This lecture will introduce the information retrieval problem, introduce the terminology related to IR, and provide a history of IR. In particular, the history of the web and its impact on IR will be discussed. Special attention and emphasis will be given to the concept of relevance in IR and the critical role it has played in the development of the subject. The lecture will end with a conceptual explanation of the IR process, and its relationships with other domains as well as current research developments.
INFORMATION RETRIEVAL MODELS
This lecture will present the models that have been used to rank documents according to their estimated relevance to user given queries, where the most relevant documents are shown ahead to those less relevant. Many of these models form the basis for many of the ranking algorithms used in many of past and today’s search applications. The lecture will describe models of IR such as Boolean retrieval, vector space, probabilistic retrieval, language models, and logical models. Relevance feedback, a technique that either implicitly or explicitly modifies user queries in light of their interaction with retrieval results, will also be discussed, as this is particularly relevant to web search and personalization.
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.
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 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.
This document discusses information storage and retrieval. It covers basic concepts of information storage including common storage media like hard drives, floppy disks, CDs, DVDs, and USB flash drives. It also discusses basic concepts of information retrieval and the major components of IR systems including databases, search mechanisms, languages, and interfaces. Finally, it discusses retrieval techniques, IR systems, evaluating IR systems, and future trends in IR.
lecture presented by Fe Angela M. Verzosa at PAARL's National Summer on the theme: Planning, Developing and Managing Digitization & Research Projects for Libraries and Information Centers, held on April 18-20, 2012, at the Function Hall of Tourism Center, Coron, Palawan.
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.
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 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.
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 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.
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 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.
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.
Indexing Techniques: Their Usage in Search Engines for Information RetrievalVikas Bhushan
1. The document discusses indexing techniques and their usage in modern search engines. It covers the transition from manual to automated indexing and different indexing methods.
2. Current trends in indexing and information retrieval are discussed such as XML indexing and its components. Future applications for indexers are also mentioned.
3. The conclusion emphasizes enhancements to indexing procedures like weighted indexing and linking of terms to improve retrieval of accurate information.
Information retrieval 13 alternative set theoretic modelsVaibhav Khanna
Alternative Set Theoretic Models
Fuzzy Set Model :a set theoretic model of document retrieval based on fuzzy theory.
Extended Boolean Model:a set theoretic model of document retrieval based on an extension of the classic Boolean model. The idea is to interpret partial matches as Euclidean distances represented in a vectorial space of index terms.
Broad introduction to information retrieval and web search, used to teaching at the Yahoo Bangalore Summer School 2013. Slides are a mash-up from my own and other people's presentations.
Information retrieval (IR) is the process of searching for and retrieving relevant documents from a large collection based on a user's query. Key aspects of IR include:
- Representing documents and queries in a way that allows measuring their similarity, such as the vector space model.
- Ranking retrieved documents by relevance to the query using factors like term frequency and inverse document frequency.
- Allowing for similarity-based retrieval where documents similar to a given document are retrieved.
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 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 talk features the basics behind the science of Information Retrieval with a story-mode on information and its various aspects. It then takes you through a quick journey into the process behind building of the search engine.
INTRODUCTION TO INFORMATION RETRIEVAL
This lecture will introduce the information retrieval problem, introduce the terminology related to IR, and provide a history of IR. In particular, the history of the web and its impact on IR will be discussed. Special attention and emphasis will be given to the concept of relevance in IR and the critical role it has played in the development of the subject. The lecture will end with a conceptual explanation of the IR process, and its relationships with other domains as well as current research developments.
INFORMATION RETRIEVAL MODELS
This lecture will present the models that have been used to rank documents according to their estimated relevance to user given queries, where the most relevant documents are shown ahead to those less relevant. Many of these models form the basis for many of the ranking algorithms used in many of past and today’s search applications. The lecture will describe models of IR such as Boolean retrieval, vector space, probabilistic retrieval, language models, and logical models. Relevance feedback, a technique that either implicitly or explicitly modifies user queries in light of their interaction with retrieval results, will also be discussed, as this is particularly relevant to web search and personalization.
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.
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 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.
This document discusses information storage and retrieval. It covers basic concepts of information storage including common storage media like hard drives, floppy disks, CDs, DVDs, and USB flash drives. It also discusses basic concepts of information retrieval and the major components of IR systems including databases, search mechanisms, languages, and interfaces. Finally, it discusses retrieval techniques, IR systems, evaluating IR systems, and future trends in IR.
lecture presented by Fe Angela M. Verzosa at PAARL's National Summer on the theme: Planning, Developing and Managing Digitization & Research Projects for Libraries and Information Centers, held on April 18-20, 2012, at the Function Hall of Tourism Center, Coron, Palawan.
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)
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 2-hour lecture was held at Amsterdam University of Applied Sciences (HvA) on October 16th, 2013. It represents a basic overview over core technologies used by ICT companies such as Google, Twitter or Facebook. The lecture does not require a strong technical background and stays at conceptual level.
Models for Information Retrieval and RecommendationArjen de Vries
Online information services personalize the user experience by applying recommendation systems to identify the information that is most relevant to the user. The question how to estimate relevance has been the core concept in the field of information retrieval for many years. Not so surprisingly then, it turns out that the methods used in online recommendation systems are closely related to the models developed in the information retrieval area. In this lecture, I present a unified approach to information retrieval and collaborative filtering, and demonstrate how this let’s us turn a standard information retrieval system into a state-of-the-art recommendation system.
Planning and Implementing a Digital Library ProjectJenn Riley
This document provides an overview of planning and implementing a digital library project. It discusses establishing goals and objectives, planning activities such as selecting content and writing proposals, implementing digitization, and evaluating projects. The document was presented as part of a workshop on digital library projects, and provides guidance on various aspects of the planning and implementation process.
This document discusses various types of e-resources including e-books, e-journals, online databases, and social media platforms. It outlines the advantages of e-resources such as easy retrieval of information, ability to search across many resources simultaneously, and convenience of access from any computer. The document also discusses specific e-resources like educational blogs, podcasts, virtual classrooms, and e-learning, outlining how they can be used for teaching and learning.
This document provides tips for effective writing of professional and inter-office documents and emails. It discusses focusing writing on clarity and conciseness while avoiding jargon and considering the audience. Specific tips are provided for elements like margins, dates, salutations and font sizes in letters of recommendation, and maintaining consistency, alignment and readability in curricula vitae. Effective email writing strategies include using meaningful subject lines, focusing the message, avoiding attachments, identifying yourself clearly, proofreading, maintaining privacy and professionalism, considering the formality of the situation, responding promptly and showing respect and restraint.
Tutorial on foundations of Information Retrieval Models by Thomas Roelleke and Ingo Frommholz presented at the Information Retrieval and Foraging Autumn School at Schloß Dagstuhl, Germany, September 2014.
The document discusses the query formulation process in information retrieval systems. It defines a query and explains that query formulation involves refining the original query entered by the user, such as through tokenization, normalization, and stemming of terms. This refinement stage is followed by a structural alteration stage where the query is segmented and expanded with related concepts. Effective query formulation improves search quality by better representing the user's intent.
The document discusses industrial relations (IR) models for the IT and ITES industries in India. It provides context on what IR is and the key relationships and processes involved. The main IR models followed in India are collective bargaining. For the IT and ITES sectors specifically, determinants of IR include a tertiary sector structure, flat organizations, performance-based pay, and less union involvement. The document argues Dunlop's model is relevant, with interactions between labor, management, and government producing workplace rules.
Information consolidation is defined as the process of evaluating and compressing relevant documents to provide users with reliable and concise information. It involves defining responsibility for analyzing documents and packaging information appropriately for users' needs, levels, and time constraints. The benefits of information consolidation include increasing the effectiveness and use of information for various activities, as well as expanding the circle of potential users by providing evaluated and synthesized information. The basic processes involve studying user needs, selecting relevant sources, evaluating and analyzing information, restructuring it into a new whole, and packaging and disseminating it to encourage use.
The document discusses the basics of information retrieval systems. It covers two main stages - indexing and retrieval. In the indexing stage, documents are preprocessed and stored in an index. In retrieval, queries are issued and the index is accessed to find relevant documents. The document then discusses several models for defining relevance between documents and queries, including the Boolean model and vector space model. It also covers techniques for representing documents and queries as vectors and calculating similarity between them.
The document provides guidance on revising, editing, and proofreading written work. It emphasizes the importance of proofreading to catch errors and ensures the reader understands the main idea without distraction. Strategies for effective proofreading include reading aloud, using spell check, reading backwards, and examining each section piece by piece to check elements like structure, flow, and consistency. Editing focuses on errors typical to an individual writer by knowing what types of mistakes are commonly made.
The document provides instructions for editing an essay before printing the final draft. It recommends checking that the title is centered at the top with the author's first and last name centered underneath. Paragraphs should be indented and the essay should be double spaced. Acceptable font styles, sizes between 12-16 point, correct use of capitalization, punctuation, and spelling should also be reviewed.
Real-time operating systems (RTOS) are specialized operating systems designed to run applications with precise timing and high reliability. An RTOS is single-tasked compared to general purpose OSs which run multiple tasks simultaneously. There are three main types of RTOS: hard, firm, and soft. An RTOS provides functions like task management, scheduling, resource allocation, and interrupt handling. Common applications of RTOS include web servers, aircraft control systems, medical devices, and industrial automation.
Deep Learning for Information Retrieval: Models, Progress, & OpportunitiesMatthew Lease
Talk given at the 8th Forum for Information Retrieval Evaluation (FIRE, http://fire.irsi.res.in/fire/2016/), December 10, 2016, and at the Qatar Computing Research Institute (QCRI), December 15, 2016.
The document discusses the reference interview process and searching databases. It describes the reference interview as involving understanding a user's information needs through communication and questioning techniques. Effective communication involves both content and relationship aspects. When searching databases, the document outlines strategies like controlled vocabulary, boolean logic, truncation and proximity operators to effectively search and retrieve relevant information.
14. Michael Oakes (UoW) Natural Language Processing for TranslationRIILP
This document discusses information retrieval and describes its three main phases: 1) asking a question to define an information need, 2) constructing an answer by matching queries to documents, and 3) assessing the relevance of the retrieved answers. It also covers several important information retrieval concepts like keywords, indexing documents, stemming words, calculating TF-IDF weights, and evaluating system performance using recall and precision.
An empirical performance evaluation of relational keyword search systemsBrowse Jobs
The document presents an empirical performance evaluation of relational keyword search systems. It evaluates 7 relational keyword search techniques and finds that many existing techniques do not provide acceptable performance for realistic retrieval tasks. In particular, memory consumption prevents many search techniques from scaling to datasets with more than tens of thousands of vertices. The evaluation also explores how factors varied in previous studies have relatively little impact on performance. The work confirms that existing systems have unacceptable performance and underscores the need for standardization in evaluating these retrieval systems.
This document provides guidance on planning and preparing effective searches of library databases. It recommends brainstorming concepts and keywords related to the topic, and organizing these into themes. Boolean operators and symbols can be used to construct search statements combining keywords. Complex statements using parentheses, truncation, phrase searching and proximity operators are best for databases. Sample search statements are provided combining keywords related to data visualization in health. Further steps outlined are assessing information needs, selecting search tools, testing searches, and getting help from librarians.
The document describes an evaluation of existing relational keyword search systems. It notes discrepancies in how prior studies evaluated systems using different datasets, query workloads, and experimental designs. The evaluation aims to conduct an independent assessment that uses larger, more representative datasets and queries to better understand systems' real-world performance and tradeoffs between effectiveness and efficiency. It outlines schema-based and graph-based search approaches included in the new evaluation.
Polyrepresentation in a Quantum-inspired Information Retrieval FrameworkIngo Frommholz
The document discusses the principle of polyrepresentation in information retrieval. It describes polyrepresentation as using multiple evidence or interpretations from different cognitive perspectives to determine relevance. This helps mitigate the "cognitive freefall" problem. There are different types of polyrepresentation, including document, information need, and algorithm polyrepresentation. Document polyrepresentation uses different features of a document, information need polyrepresentation uses different interpretations of a user's need, and algorithm polyrepresentation uses different retrieval algorithms or weighting schemes. The document argues that combining rankings from different polyrepresentative approaches through restricted fusion, where only the overlapping results are considered, can improve retrieval performance over single models.
Resource Description Framework Approach to Data Publication and FederationPistoia Alliance
Bob Stanley, CEO, IO Informatics, explains the utility to RDF as a standard way of defining and redefining data that could have utility in managing life science information.
Chapter 1 Introduction to Information Storage and Retrieval.pdfHabtamu100
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Hinari basic course_module_2_workbook_2014_07Aslam Mehdi
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There are 8 discussions needed in 3 days (72 hours). I added the lis.docxsusannr
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2. INFORMATION RETRIEVAL
Information retrieval is the activity of obtaining
information resources relevant to an information need
from a collection of information resources.
Searches can be based on metadata or on full-text (or
other content-based) indexing.
Goal: Find the documents most relevant to a certain Query
Dealing with notions of:
Collection of documents
Query (User’s information need)
Notion of Relevancy
3. MODEL
A model is a construct designed help us understand a
complex system
A particular way of “looking at things”
Models inevitably make simplifying assumptions
What are the limitations of the model?
Different types of models:
Conceptual models
Physical analog models
Mathematical models
4. Retrieval Models
A retrieval model specifies the details
of:
Document representation
Query representation
Retrieval function
Determines a notion of relevance.
Notion of relevance can be binary or
continuous (i.e. ranked retrieval).
5. CLASSES OF RM
Boolean models (set theoretic)
Extended Boolean
Vector space models
(statistical/algebraic)
Generalized VS
Latent Semantic Indexing
Probabilistic models
6. MODELS OF IR
Boolean model
Based on the notion of sets
Documents are retrieved only if they satisfy Boolean
conditions specified in the query
Does not impose a ranking on retrieved documents
Exact match
Vector space model
Based on geometry, the notion of vectors in high dimensional
space
Documents are ranked based on their similarity to the query
(ranked retrieval)
Best/partial match
7. Language models
Based on the notion of probabilities and processes for
generating text
Documents are ranked based on the probability that
they generated the query
Best/partial match
8. BOOLEAN MODEL
Invented by George Boole (1815-1864)
He devised a system of symbolic logic in which he used
three operators (+, , - ) to combine statements in
symbolic form.
John Venn named to this operators of Boolean logic
are the logical sum(+), logical product(), and logical
difference(-).
IR systems allow the users to express their queries by
using this operators.
9. BOOLEAN MODEL
Each index term is either present or absent
Documents are either Relevant or Not Relevant(no
ranking)
A document is represented as a set of keywords.
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.
10. BOOLEAN RETRIEVAL MODEL
Popular retrieval model because:
Easy to understand for simple queries.
Clean formalism.
Boolean models can be extended to include ranking.
Reasonably efficient implementations possible for
normal queries.
11. BOOLEAN MODEL
Weights assigned to terms are either “0” or “1”
“0” represents “absence”: term isn’t in the document
“1” represents “presence”: term is in the document
Build queries by combining terms with Boolean
operators
AND, OR, NOT
The system returns all documents that satisfy the
query
13. Why Boolean Retrieval Works
Boolean operators approximate natural language
Find documents about a good party that is not over
AND can discover relationships between concepts
good party
OR can discover alternate terminology
excellent party, wild party, etc.
NOT can discover alternate meanings
Democratic party
14. The Perfect Query Paradox
Every information need has a perfect set of documents
If not, there would be no sense doing retrieval
Every document set has a perfect query
AND every word in a document to get a query for it
Repeat for each document in the set
OR every document query to get the set query
But can users realistically be expected to formulate this
perfect query?
Boolean query formulation is hard!
15. Why Boolean Retrieval Fails
• Natural language is way more complex
• AND “discovers” nonexistent relationships
– Terms in different sentences, paragraphs, …
• Guessing terminology for OR is hard
– good, nice, excellent, outstanding, awesome, …
• Guessing terms to exclude is even harder!
– Democratic party, party to a lawsuit, …
16. BOOLEAN MODEL
Strengths
Precise, if you know the right strategies
Precise, if you have an idea of what you’re looking for
Efficient for the computer
Simple
Weaknesses
Users must learn Boolean logic
Boolean logic insufficient to capture the richness of language
No control over size of result set: either too many documents or none
When do you stop reading? All documents in the result set are
considered “equally good”
What about partial matches? Documents that “don’t quite match” the
query may be useful also
No notion of ranking (exact matching only)
All index terms have equal weight
17. PROBLEMS
Very rigid: AND means all; OR means any.
Difficult to express complex user requests.
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?
18. ADVANTAGES & DISADVANTAGES
Advantages
Results are predictable, relatively easy to explain
Many different features can be incorporated
Efficient processing since many documents can be
eliminated from search
Disadvantages
Effectiveness depends entirely on user
Simple queries usually don’t work well
Complex queries are difficult.
19. LIMITATIONS
The first relates to the formulation of search statements.
It has been noted that users are not able to formulate an exact search
statement by the combination of AND, OR and NOT operators,
especially when several query terms are involved.
In such cases either the search statement becomes too narrow or too
broad.
The second limitation relates to the number of retrieval items.
It has been noted that users cannot predict a priori exactly how many
items are to be retrieved to satisfy a given query.
If the search statement is broad, the number of retrieved items may
sometimes be several hundreds and thus it may be quite difficult to
find out the exact information required.
The third limitation is that it identifies an item as relevant by finding
out whether a given query term is present or not in a given record in the
database.
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