The document discusses techniques for improving query formulations in information retrieval systems, including relevance feedback and query expansion. It describes how relevance feedback works by reformulating the query based on terms from documents users mark as relevant or irrelevant. This can be done automatically through pseudo relevance feedback by assuming the top retrieved documents are relevant. The document also discusses problems that can arise with relevance feedback and potential solutions, such as presenting modified queries to users for review.
This document summarizes key aspects of evaluating information retrieval systems, including:
- Precision and recall are common performance measures, where precision measures the percentage of retrieved documents that are relevant and recall measures the percentage of relevant documents retrieved.
- Other measures include mean average precision (MAP), which averages precision scores across queries, and R-precision, which measures precision after R relevant documents are retrieved, where R is the total number of relevant documents.
- Precision and recall can be plotted on a graph to show their tradeoff, with interpolation used to calculate precision at standard recall levels for better comparison of systems.
- Relevance judgments can be subjective, situational, and dynamic, making evaluation of IR systems challenging.
Enhancing Enterprise Search with Machine Learning - Simon Hughes, Dice.comSimon Hughes
In the talk I describe two approaches for improve the recall and precision of an enterprise search engine using machine learning techniques. The main focus is improving relevancy with ML while using your existing search stack, be that Luce, Solr, Elastic Search, Endeca or something else.
Modern information Retrieval-Relevance FeedbackHasanulFahmi2
The document discusses different types of relevance feedback in information retrieval systems, including explicit, implicit, and blind feedback. Explicit feedback involves users directly indicating relevant and irrelevant documents. Implicit feedback observes user behavior to infer relevance. Blind feedback automatically assumes the top results are relevant without user input. The document also examines local versus global query expansion and the use of controlled vocabularies and automatic versus manual thesauri.
Information retrieval systems aim to find documents relevant to a user's information need. Search engines are a common example, allowing users to enter queries and receiving a list of relevant web pages. Effective systems represent documents and queries statistically based on word frequencies and use scoring functions to rank documents by estimated relevance to the query. Evaluation involves measuring a system's precision, the proportion of returned documents that are relevant, and recall, the proportion of all relevant documents that are returned.
Information retrieval systems aim to find documents relevant to a user's information need. Search engines are a common example, allowing users to enter queries and receiving a list of relevant web pages. Effective systems represent documents and queries statistically based on word frequencies and use scoring functions to rank documents by estimated relevance to the query. Evaluation involves measuring a system's precision, the proportion of returned documents that are relevant, and recall, the proportion of all relevant documents that are returned.
This document discusses different types of query languages used for information retrieval systems. It describes keyword queries where documents are retrieved based on the presence of query words. Phrase queries search for an exact sequence of words. Boolean queries use logical operators like AND, OR and NOT to combine search terms. Natural language queries allow users to enter searches in a free-form manner but require translation to a formal query language. The document provides examples and explanations of each query language type over its 12 sections.
Here are the answers to the quiz questions:
1. The two main IR system evaluation strategies are:
- System-centered evaluation: Focuses on evaluating different variations of an IR system using document collections, queries, and relevance judgments.
- User-centered evaluation: Focuses on evaluating how well different IR systems satisfy users' actual information needs by testing users on tasks using different systems.
2. For the given information:
- Precision = Relevant docs returned / Total docs returned = 8/18 = 0.44
- Recall = Relevant docs returned / Total relevant docs in collection = 8/20 = 0.4
So the precision is 0.44 and the recall is 0.4
The document discusses Ellen Voorhees' work in information retrieval evaluation. It outlines some of the premises of evaluation, including the influential Cranfield paradigm, and how modern tests like TREC have adapted this paradigm. It also addresses challenges like pooling, incomplete judgments, assessor subjectivity, and how to build test collections for cross-language evaluation.
This document summarizes key aspects of evaluating information retrieval systems, including:
- Precision and recall are common performance measures, where precision measures the percentage of retrieved documents that are relevant and recall measures the percentage of relevant documents retrieved.
- Other measures include mean average precision (MAP), which averages precision scores across queries, and R-precision, which measures precision after R relevant documents are retrieved, where R is the total number of relevant documents.
- Precision and recall can be plotted on a graph to show their tradeoff, with interpolation used to calculate precision at standard recall levels for better comparison of systems.
- Relevance judgments can be subjective, situational, and dynamic, making evaluation of IR systems challenging.
Enhancing Enterprise Search with Machine Learning - Simon Hughes, Dice.comSimon Hughes
In the talk I describe two approaches for improve the recall and precision of an enterprise search engine using machine learning techniques. The main focus is improving relevancy with ML while using your existing search stack, be that Luce, Solr, Elastic Search, Endeca or something else.
Modern information Retrieval-Relevance FeedbackHasanulFahmi2
The document discusses different types of relevance feedback in information retrieval systems, including explicit, implicit, and blind feedback. Explicit feedback involves users directly indicating relevant and irrelevant documents. Implicit feedback observes user behavior to infer relevance. Blind feedback automatically assumes the top results are relevant without user input. The document also examines local versus global query expansion and the use of controlled vocabularies and automatic versus manual thesauri.
Information retrieval systems aim to find documents relevant to a user's information need. Search engines are a common example, allowing users to enter queries and receiving a list of relevant web pages. Effective systems represent documents and queries statistically based on word frequencies and use scoring functions to rank documents by estimated relevance to the query. Evaluation involves measuring a system's precision, the proportion of returned documents that are relevant, and recall, the proportion of all relevant documents that are returned.
Information retrieval systems aim to find documents relevant to a user's information need. Search engines are a common example, allowing users to enter queries and receiving a list of relevant web pages. Effective systems represent documents and queries statistically based on word frequencies and use scoring functions to rank documents by estimated relevance to the query. Evaluation involves measuring a system's precision, the proportion of returned documents that are relevant, and recall, the proportion of all relevant documents that are returned.
This document discusses different types of query languages used for information retrieval systems. It describes keyword queries where documents are retrieved based on the presence of query words. Phrase queries search for an exact sequence of words. Boolean queries use logical operators like AND, OR and NOT to combine search terms. Natural language queries allow users to enter searches in a free-form manner but require translation to a formal query language. The document provides examples and explanations of each query language type over its 12 sections.
Here are the answers to the quiz questions:
1. The two main IR system evaluation strategies are:
- System-centered evaluation: Focuses on evaluating different variations of an IR system using document collections, queries, and relevance judgments.
- User-centered evaluation: Focuses on evaluating how well different IR systems satisfy users' actual information needs by testing users on tasks using different systems.
2. For the given information:
- Precision = Relevant docs returned / Total docs returned = 8/18 = 0.44
- Recall = Relevant docs returned / Total relevant docs in collection = 8/20 = 0.4
So the precision is 0.44 and the recall is 0.4
The document discusses Ellen Voorhees' work in information retrieval evaluation. It outlines some of the premises of evaluation, including the influential Cranfield paradigm, and how modern tests like TREC have adapted this paradigm. It also addresses challenges like pooling, incomplete judgments, assessor subjectivity, and how to build test collections for cross-language evaluation.
This document provides an overview of information retrieval systems. It defines key concepts such as data, information, and knowledge. It describes the components of an information retrieval system including the system, users, and documents. It discusses different models for information retrieval including vector space and probabilistic models. It also covers techniques for improving retrieval effectiveness such as relevance feedback and using term frequency-inverse document frequency to assign weights. The document outlines two main approaches to information retrieval - indexing and retrieval.
This document discusses content-based recommendation systems. It describes how items and user profiles are represented, and different methods for making recommendations including manual methods, decision trees/rule induction, and nearest neighbor algorithms. Content-based systems recommend items to users based on descriptions of the items and profiles of users' interests, but have limitations in recognizing subtleties and anticipating future interests.
Search term recommendation and non-textual ranking evaluatedGESIS
1. The document describes a study that evaluated search term recommendation and non-textual ranking services to improve search in digital libraries.
2. It found that each service produced unique document results with low overlap between them, and precision values were the same or better than standard text-based ranking.
3. The services provided alternative views into document spaces and improved relevance, though each had weaknesses, suggesting the services are best used interactively to support exploration.
This document provides an introduction to text mining and information retrieval. It discusses how text mining is used to extract knowledge and patterns from unstructured text sources. The key steps of text mining include preprocessing text, applying techniques like summarization and classification, and analyzing the results. Text databases and information retrieval systems are described. Various models and techniques for text retrieval are outlined, including Boolean, vector space, and probabilistic models. Evaluation measures like precision and recall are also introduced.
Enriching Solr with Deep Learning for a Question Answering System - Sanket Sh...Lucidworks
The document discusses research into using deep learning to improve question answering systems. It describes using Solr to retrieve documents and then using machine learning models to rerank the results. The research compared various supervised and unsupervised models for question similarity and answer selection tasks. For question similarity, ensemble models using TFIDF and sentence embeddings performed best. For answer selection, deep learning models outperformed traditional models when sufficient training data was available.
Personalized Search and Job Recommendations - Simon Hughes, Dice.comLucidworks
This document summarizes Simon Hughes' presentation on personalized search and job recommendations. Hughes is the Chief Data Scientist at Dice.com, where he works on recommender engines, skills pages, and other projects. The presentation discusses relevancy feedback algorithms like Rocchio that can be used to improve search results based on user interactions. It also describes how content-based and collaborative filtering recommendations can be provided in real-time using Solr plugins. Finally, it shows how personalized search can be achieved by boosting results matching a user's profile or search history.
Dice.com Bay Area Search - Beyond Learning to Rank TalkSimon Hughes
This talk describes how to implement conceptual search (semantic search) within a modern search engine using the word2vec algorithm to learn concepts. We also cover how to auto-tune the search engine parameters using black box optimization techniques, and the problems of feedback loops encountered when building machine learning systems that modify the user behavior used to train the system.
This document discusses various techniques for filtering and recommender systems, including content-based filtering, collaborative filtering, and hybrid approaches. It provides an overview of key concepts such as using user profiles and feedback to provide personalized recommendations. It also covers common recommendation algorithms like nearest neighbor collaborative filtering and discusses challenges like cold start problems and sparsity issues.
The goal of information retrieval (IR) is to provide users with those documents that will satisfy their information need. The information need can be understood as forming a pyramid, where only its peak is made visible by users in the form of a conceptual query.
This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.
RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...S. Diana Hu
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
This document provides an overview of topic modeling. It defines topic modeling as discovering the thematic structure of a corpus by modeling relationships between words and documents through learned topics. The document introduces Latent Dirichlet Allocation (LDA) as a widely used topic modeling technique. It outlines LDA's generative process and inference methods like Gibbs sampling and variational inference. The document also discusses extensions to LDA, evaluation strategies, open questions, and applications like topic labeling and browsing.
Made to Measure: Ranking Evaluation using ElasticsearchDaniel Schneiter
The document describes ranking evaluation in Elasticsearch using its rank_eval API. Ranking evaluation allows measuring search quality through repeatable testing across different user needs. It defines typical search queries and rates documents to calculate metrics like precision, reciprocal rank, and discounted cumulative gain. This provides a way to quickly iterate on search and optimize for more user needs compared to methods like A/B testing. The demo uses Wikipedia data to illustrate the ranking evaluation process and API.
CUE Forum presented at JALT 2008 (Tokyo, Japan). Gives an overview of research design issues for Second Language Acquisition. For further details, visit jaltcue-sig.org
Learning by example: training users through high-quality query suggestionsClaudia Hauff
A presentation given at UvA in September 2015, discussing joint work with Morgan Harvey and David Elsweiler.
Full paper: http://dl.acm.org/citation.cfm?id=2767731
Relevance in the Wild - Daniel Gomez Vilanueva, FindwiseLucidworks
This document discusses relevance in information retrieval systems. It begins with definitions of relevance and how relevance is measured. It then covers similarity functions like TF-IDF and BM25 that are used to calculate relevance scores. Configuration options for similarity in Solr are presented, including setting similarity globally or per field. The edismax query parser is described along with parameters that impact relevance. Methods for evaluating relevance through testing and analysis are provided. Finally, examples of applying relevance techniques to real systems are briefly outlined.
Deductive vs Inductive ReasoningDeductive reasoning starts out w.docxsimonithomas47935
This document provides guidance for writing a research report, including its structure, formatting, and content. It outlines a five-chapter model for the report, with each chapter addressing a different component: Chapter 1 provides an introduction and background; Chapter 2 presents a literature review; Chapter 3 describes the methodology; Chapter 4 presents the findings and results; and Chapter 5 offers conclusions. Additional sections like the abstract, references, and appendices are also noted. Specific requirements are given for formatting the report, citing sources, and ensuring academic integrity. The document serves as a reference for students in developing an original research report that demonstrates their expertise on the chosen topic area.
Semantic Similarity and Selection of Resources Published According to Linked ...Riccardo Albertoni
The position paper aims at discussing the potential of exploiting linked data best practice to provide metadata documenting domain specific resources created through verbose acquisition-processing pipelines. It argues that resource selection, namely the process engaged to choose a set of resources suitable for a given analysis/design purpose, must be supported by a deep comparison of their metadata. The semantic similarity proposed in our previous works is discussed for this purpose and the main issues to make it scale up to the web of data are introduced. Discussed issues contribute beyond the re-engineering of our similarity since they largely apply to every tool which is going to exploit information made available as linked data. A research plan and an exploratory phase facing the presented issues are described remarking the lessons we have learnt so far.
An Example of Predictive Analytics: Building a Recommendation Engine Using Py...PyData
This document discusses building a hybrid recommendation engine using Python to recommend Pubmed documents. It begins with an introduction to predictive analytics and recommender systems. Different types of recommender systems are described, including knowledge-based, content-based, collaborative filtering, and hybrid models. The document then outlines a hybrid model that performs content-based filtering on Pubmed documents using vector space modeling and weights documents, before applying collaborative filtering using the Python-recsys library to filter and recommend documents. Finally, it demonstrates the hybrid model on a Pubmed dataset and compares its performance to using Python-recsys alone.
This document provides an outline for a course on Information Storage and Retrieval. It includes information on the course code, credits, target group, instructor contact details, course description and objectives. The course syllabus outlines 8 chapters covering topics like introduction to information retrieval systems, text operations and indexing, retrieval models and evaluation, query languages and operations, and current issues in IR. Student assessment will include assignments, tests, exams and a project. Reference books for the course are also listed.
The document discusses information retrieval (IR) models, including the Boolean, vector space, and probabilistic models. The Boolean model represents documents and queries as sets of index terms and determines relevance through binary term presence, while the vector space model represents documents and queries as weighted vectors in a multidimensional space and ranks documents by calculating similarity between document and query vectors. The probabilistic model determines relevance probabilities based on the likelihood of terms appearing in relevant vs. non-relevant documents.
This document provides an overview of information retrieval systems. It defines key concepts such as data, information, and knowledge. It describes the components of an information retrieval system including the system, users, and documents. It discusses different models for information retrieval including vector space and probabilistic models. It also covers techniques for improving retrieval effectiveness such as relevance feedback and using term frequency-inverse document frequency to assign weights. The document outlines two main approaches to information retrieval - indexing and retrieval.
This document discusses content-based recommendation systems. It describes how items and user profiles are represented, and different methods for making recommendations including manual methods, decision trees/rule induction, and nearest neighbor algorithms. Content-based systems recommend items to users based on descriptions of the items and profiles of users' interests, but have limitations in recognizing subtleties and anticipating future interests.
Search term recommendation and non-textual ranking evaluatedGESIS
1. The document describes a study that evaluated search term recommendation and non-textual ranking services to improve search in digital libraries.
2. It found that each service produced unique document results with low overlap between them, and precision values were the same or better than standard text-based ranking.
3. The services provided alternative views into document spaces and improved relevance, though each had weaknesses, suggesting the services are best used interactively to support exploration.
This document provides an introduction to text mining and information retrieval. It discusses how text mining is used to extract knowledge and patterns from unstructured text sources. The key steps of text mining include preprocessing text, applying techniques like summarization and classification, and analyzing the results. Text databases and information retrieval systems are described. Various models and techniques for text retrieval are outlined, including Boolean, vector space, and probabilistic models. Evaluation measures like precision and recall are also introduced.
Enriching Solr with Deep Learning for a Question Answering System - Sanket Sh...Lucidworks
The document discusses research into using deep learning to improve question answering systems. It describes using Solr to retrieve documents and then using machine learning models to rerank the results. The research compared various supervised and unsupervised models for question similarity and answer selection tasks. For question similarity, ensemble models using TFIDF and sentence embeddings performed best. For answer selection, deep learning models outperformed traditional models when sufficient training data was available.
Personalized Search and Job Recommendations - Simon Hughes, Dice.comLucidworks
This document summarizes Simon Hughes' presentation on personalized search and job recommendations. Hughes is the Chief Data Scientist at Dice.com, where he works on recommender engines, skills pages, and other projects. The presentation discusses relevancy feedback algorithms like Rocchio that can be used to improve search results based on user interactions. It also describes how content-based and collaborative filtering recommendations can be provided in real-time using Solr plugins. Finally, it shows how personalized search can be achieved by boosting results matching a user's profile or search history.
Dice.com Bay Area Search - Beyond Learning to Rank TalkSimon Hughes
This talk describes how to implement conceptual search (semantic search) within a modern search engine using the word2vec algorithm to learn concepts. We also cover how to auto-tune the search engine parameters using black box optimization techniques, and the problems of feedback loops encountered when building machine learning systems that modify the user behavior used to train the system.
This document discusses various techniques for filtering and recommender systems, including content-based filtering, collaborative filtering, and hybrid approaches. It provides an overview of key concepts such as using user profiles and feedback to provide personalized recommendations. It also covers common recommendation algorithms like nearest neighbor collaborative filtering and discusses challenges like cold start problems and sparsity issues.
The goal of information retrieval (IR) is to provide users with those documents that will satisfy their information need. The information need can be understood as forming a pyramid, where only its peak is made visible by users in the form of a conceptual query.
This tutorial gives an overview of how search engines and machine learning techniques can be tightly coupled to address the need for building scalable recommender or other prediction based systems. Typically, most of them architect retrieval and prediction in two phases. In Phase I, a search engine returns the top-k results based on constraints expressed as a query. In Phase II, the top-k results are re-ranked in another system according to an optimization function that uses a supervised trained model. However this approach presents several issues, such as the possibility of returning sub-optimal results due to the top-k limits during query, as well as the prescence of some inefficiencies in the system due to the decoupling of retrieval and ranking.
To address this issue the authors created ML-Scoring, an open source framework that tightly integrates machine learning models into Elasticsearch, a popular search engine. ML-Scoring replaces the default information retrieval ranking function with a custom supervised model that is trained through Spark, Weka, or R that is loaded as a plugin in Elasticsearch. This tutorial will not only review basic methods in information retrieval and machine learning, but it will also walk through practical examples from loading a dataset into Elasticsearch to training a model in Spark, Weka, or R, to creating the ML-Scoring plugin for Elasticsearch. No prior experience is required in any system listed (Elasticsearch, Spark, Weka, R), though some programming experience is recommended.
RecSys 2015 Tutorial – Scalable Recommender Systems: Where Machine Learning...S. Diana Hu
Search engines have focused on solving the document retrieval problem, so their scoring functions do not handle naturally non-traditional IR data types, such as numerical or categorical. Therefore, on domains beyond traditional search, scores representing strengths of associations or matches may vary widely. As such, the original model doesn’t suffice, so relevance ranking is performed as a two-phase approach with 1) regular search 2) external model to re-rank the filtered items. Metrics such as click-through and conversion rates are associated with the users’ response to items served. The predicted selection rates that arise in real-time can be critical for optimal matching. For example, in recommender systems, predicted performance of a recommended item in a given context, also called response prediction, is often used in determining a set of recommendations to serve in relation to a given serving opportunity. Similar techniques are used in the advertising domain. To address this issue the authors have created ML-Scoring, an open source framework that tightly integrates machine learning models into a popular search engine (SOLR/Elasticsearch), replacing the default IR-based ranking function. A custom model is trained through either Weka or Spark and it is loaded as a plugin used at query time to compute custom scores.
This document provides an overview of topic modeling. It defines topic modeling as discovering the thematic structure of a corpus by modeling relationships between words and documents through learned topics. The document introduces Latent Dirichlet Allocation (LDA) as a widely used topic modeling technique. It outlines LDA's generative process and inference methods like Gibbs sampling and variational inference. The document also discusses extensions to LDA, evaluation strategies, open questions, and applications like topic labeling and browsing.
Made to Measure: Ranking Evaluation using ElasticsearchDaniel Schneiter
The document describes ranking evaluation in Elasticsearch using its rank_eval API. Ranking evaluation allows measuring search quality through repeatable testing across different user needs. It defines typical search queries and rates documents to calculate metrics like precision, reciprocal rank, and discounted cumulative gain. This provides a way to quickly iterate on search and optimize for more user needs compared to methods like A/B testing. The demo uses Wikipedia data to illustrate the ranking evaluation process and API.
CUE Forum presented at JALT 2008 (Tokyo, Japan). Gives an overview of research design issues for Second Language Acquisition. For further details, visit jaltcue-sig.org
Learning by example: training users through high-quality query suggestionsClaudia Hauff
A presentation given at UvA in September 2015, discussing joint work with Morgan Harvey and David Elsweiler.
Full paper: http://dl.acm.org/citation.cfm?id=2767731
Relevance in the Wild - Daniel Gomez Vilanueva, FindwiseLucidworks
This document discusses relevance in information retrieval systems. It begins with definitions of relevance and how relevance is measured. It then covers similarity functions like TF-IDF and BM25 that are used to calculate relevance scores. Configuration options for similarity in Solr are presented, including setting similarity globally or per field. The edismax query parser is described along with parameters that impact relevance. Methods for evaluating relevance through testing and analysis are provided. Finally, examples of applying relevance techniques to real systems are briefly outlined.
Deductive vs Inductive ReasoningDeductive reasoning starts out w.docxsimonithomas47935
This document provides guidance for writing a research report, including its structure, formatting, and content. It outlines a five-chapter model for the report, with each chapter addressing a different component: Chapter 1 provides an introduction and background; Chapter 2 presents a literature review; Chapter 3 describes the methodology; Chapter 4 presents the findings and results; and Chapter 5 offers conclusions. Additional sections like the abstract, references, and appendices are also noted. Specific requirements are given for formatting the report, citing sources, and ensuring academic integrity. The document serves as a reference for students in developing an original research report that demonstrates their expertise on the chosen topic area.
Semantic Similarity and Selection of Resources Published According to Linked ...Riccardo Albertoni
The position paper aims at discussing the potential of exploiting linked data best practice to provide metadata documenting domain specific resources created through verbose acquisition-processing pipelines. It argues that resource selection, namely the process engaged to choose a set of resources suitable for a given analysis/design purpose, must be supported by a deep comparison of their metadata. The semantic similarity proposed in our previous works is discussed for this purpose and the main issues to make it scale up to the web of data are introduced. Discussed issues contribute beyond the re-engineering of our similarity since they largely apply to every tool which is going to exploit information made available as linked data. A research plan and an exploratory phase facing the presented issues are described remarking the lessons we have learnt so far.
An Example of Predictive Analytics: Building a Recommendation Engine Using Py...PyData
This document discusses building a hybrid recommendation engine using Python to recommend Pubmed documents. It begins with an introduction to predictive analytics and recommender systems. Different types of recommender systems are described, including knowledge-based, content-based, collaborative filtering, and hybrid models. The document then outlines a hybrid model that performs content-based filtering on Pubmed documents using vector space modeling and weights documents, before applying collaborative filtering using the Python-recsys library to filter and recommend documents. Finally, it demonstrates the hybrid model on a Pubmed dataset and compares its performance to using Python-recsys alone.
This document provides an outline for a course on Information Storage and Retrieval. It includes information on the course code, credits, target group, instructor contact details, course description and objectives. The course syllabus outlines 8 chapters covering topics like introduction to information retrieval systems, text operations and indexing, retrieval models and evaluation, query languages and operations, and current issues in IR. Student assessment will include assignments, tests, exams and a project. Reference books for the course are also listed.
The document discusses information retrieval (IR) models, including the Boolean, vector space, and probabilistic models. The Boolean model represents documents and queries as sets of index terms and determines relevance through binary term presence, while the vector space model represents documents and queries as weighted vectors in a multidimensional space and ranks documents by calculating similarity between document and query vectors. The probabilistic model determines relevance probabilities based on the likelihood of terms appearing in relevant vs. non-relevant documents.
Chapter 1 Introduction to Information Storage and Retrieval.pdfHabtamu100
This course outline provides information about an Information Storage and Retrieval course for third year Information Technology students. The course will cover introductory concepts of information storage and retrieval over 5 ECTS credits across one semester. Topics will include automatic text operations, indexing structures, retrieval models, evaluation, query languages, and current issues. Assessment will include assignments, tests, a project, midterm, and final exam.
This document discusses various text operations and techniques for automatic indexing in information retrieval systems. It covers topics like tokenization, stop word removal, stemming, term weighting, Zipf's law, Luhn's model of word frequency, and Heap's law on vocabulary growth. The goal of these text operations is to select meaningful index terms from documents to represent their contents and reduce noise for more effective retrieval.
The document discusses various indexing structures used to improve efficiency of information retrieval from document collections. It describes sequential files which arrange records sequentially but sorted on a key. Inverted files list terms alphabetically with pointers to documents containing the term. Suffix trees and arrays store suffixes to enable fast lookups. The construction of inverted files involves extracting terms, building vocabulary and postings lists. Searching inverted files uses binary search on vocabulary and accesses postings lists. Suffix trees compactly store all suffixes to enable prefix matching.
The document discusses techniques for improving information retrieval through query reformulation, including relevance feedback and query expansion. It describes:
1) Relevance feedback involves revising the original query based on a user's judgements about retrieved documents, such as increasing weights of terms in relevant documents and decreasing weights in irrelevant documents.
2) Pseudo relevance feedback automatically assumes the top retrieved documents are relevant and uses them to expand the query, without explicit user feedback.
3) Query expansion techniques aim to retrieve additional relevant documents by adding new related terms to the original query, drawing terms either from the local set of initially retrieved documents, or globally from the entire document collection. Local analysis tends to give better results by avoiding ambiguity.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This presentation was provided by Rebecca Benner, Ph.D., of the American Society of Anesthesiologists, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
B. Ed Syllabus for babasaheb ambedkar education university.pdf
qury.pdf
1. 1
Chapter Seven
Query Operations
Relevance feedback
Query expansion
Problems with Keywords
• May not retrieve relevant documents that
include synonymous terms.
◦ “restaurant” vs. “café”
◦ “PRC” vs. “China”
• May retrieve irrelevant documents that include
ambiguous terms.
◦ “bat” (baseball vs. mammal)
◦ “Apple” (company vs. fruit)
◦ “bit” (unit of data vs. act of eating)
Techniques for Intelligent IR
• Take into account the meaning of the words used
• Take into account the order of words in the query
• Adapt to the user based on automatic or semi-
automatic feedback
• Extend search with related terms
• Perform automatic spell checking / diacritics
restoration (diacritics-A mark added to a letter to
indicate a special pronunciation)
• Take into account the authority of the source.
Query operations
l No detailed knowledge of collection and retrieval
environment
difficult to formulate queries well designed for
retrieval
Need many formulations of queries for effective
retrieval
uFirst formulation: often naïve attempt to retrieve
relevant information
uDocuments initially retrieved:
Can be examined for relevance information by
user, automatically by the system
Improve query formulations for retrieving
additional relevant documents
2. 2
Query reformulation
•Two basic techniques to revise query to account for
feedback:
–Query expansion: Expanding original query with
new terms from relevant documents.
• This is done by adding new terms to query from
relevant documents
–Term reweighting in expanded query: Modify term
weights based on user relevance judgements.
• Increase weight of terms in relevant documents
• decrease weight of terms in irrelevant documents
6
Approaches for Relevance Feedback
Approaches based on Users relevance feedback
Relevance feedback with user input
Clustering hypothesis: known relevant documents contain terms which
can be used to describe a larger cluster of relevant documents
Description of cluster built interactively with user
assistance
Approaches based on pseudo relevance feedback
Use relevance feedback methods without explicit user
involvement.
Obtain cluster description automatically
Identify terms related to query terms
e.g. synonyms, stemming variations, terms close to query terms
in text
User Relevance Feedback
• Most popular query reformulation strategy
• Cycle:
–User presented with list of retrieved documents
• After initial retrieval results are presented, allow the user to provide
feedback on the relevance of one or more of the retrieved documents.
–User marks those which are relevant
• In practice: top 10-20 ranked documents are examined
–Use this feedback information to reformulate the query.
• Select important terms from documents assessed relevant by users
–Enhance importance of these terms in a new query
• Produce new results based on reformulated query.
–Allows more interactive, multi-pass process.
• Expected:
–New query moves towards relevant documents and away from non-relevant
documents
User Relevance Feedback Architecture
Rankings
IR
System
Document
corpus
Ranked
Documents
1. Doc1
2. Doc2
3. Doc3
.
.
1. Doc1
2. Doc2
3. Doc3
.
.
Feedback
Query
String
Revised
Query
ReRanked
Documents
1. Doc2
2. Doc4
3. Doc5
.
.
Query
Reformulation
3. 3
9
Refinement by relevance feedback (cont.)
• In the vector model, a query is a vector of term weights; hence
reformulation involves reassigning term weights.
• If a document is known to be relevant, the query can be improved
by increasing its similarity to that document.
• If a document is known to be non-relevant, the query can be
improved by decreasing its similarity to that document.
• Problems:
• What if the query has to increase its similarity to two very non-similar
documents (each “pulls” the query in an entirely different direction)?
• What if the query has to be decrease its similarity to two very non-
similar documents (each “pushes” the query in an entirely different
direction)?
• Critical assumptions that must be made:
• Relevant documents resemble each other (are clustered).
• Non-relevant documents resemble each other (are clustered).
• Non-relevant documents differ from the relevant documents. 10
Refinement by relevance feedback (cont.)
• For a query q, denote
• DR : The set of relevant documents in the answer (as identified by the user)
• DN : The set of non-relevant documents in the answer.
• CR : The set of relevant documents in the collection (the ideal answer).
• Assume (unrealistic!) that CR is known in advance.
• It can then be shown that the best query vector for distinguishing
the relevant documents from the non-relevant documents is:
• The left expression is the centroid of the relevant documents, the
right expression is the centroid of the non-relevant documents.
• Note: This expression is vector arithmetic! dj are vectors, whereas
|CR| and |n – CR| are scalars.
R
j
R
j C
d
j
R
C
d
j
R
d
C
n
d
C
1
1
11
Refinement by relevance feedback (cont.)
• Since we don’t know CR, we shall substitute it by DR in
each of the two expressions, and then use them to modify
the initial query q:
• a, b, and g are tuning constants; for example, 1.0, 0.5,
0.25.
• Note: This expression is vector arithmetic! q and dj are
vectors, whereas |DR|, |DN|, a, b, and g are scalars.
N
j
R
j D
d
j
N
D
d
j
R
new
d
D
d
D
q
q
g
b
a.
12
Refinement by relevance feedback (cont.)
• Positive feedback factor. Uses the user's judgments
on relevant documents to increase the values of terms. Moves
the query to retrieve documents similar to relevant documents
retrieved (in the direction of more relevant documents).
• Negative feedback factor. Uses the user's judgments
on non-relevant documents to decrease the values of terms.
Moves the query away from non-relevant documents.
• Positive feedback often weighted significantly more than
negative feedback; Sometimes, only positive feedback is used.
R
j
D
d
j
R
d
D
b
N
j D
d
j
N
d
D
g
4. 4
13
Refinement by relevance feedback (cont.)
• Example:
• Assume query q = (3,0,0,2,0) retrieved three documents: d1, d2, d3.
• Assume d1 and d2 are judged relevant and d3 is judged non-relevant.
• Assume the tuning constants used are 1.0, 0.5, 0.25.
k1 k2 k3 k4 k5
q 3 0 0 2 0
d1 2 4 0 0 2
d2 1 3 0 0 0
d3 0 0 4 3 3
qnew 3.75 1.75 0 1.25 0
The revised query is:
qnew = (3, 0, 0, 2, 0)
+ 0.5 * ((2+1)/2, (4+3)/2, (0+0)/2, (0+0)/2, (2+0)/2)
– 0.25 * (0, 0, 4, 3, 3)
= (3.75, 1.75, –1, 1.25, 0)
= (3.75, 1.75, 0, 1.25, 0)
14
Refinement by relevance feedback (cont.)
• Using a simplified similarity formula (the nominator only of the cosine):
we can compare the similarity of q and qnew to the three documents:
d1 d2 d3
q 6 3 6
qnew 14.5 9 3.75
• Compared to the original query, the new query is indeed more similar to
d1, d2. (which were judged relevant), and less similar to d3 (which was
judged non-relevant).
similarity d j ,q
i 1
t
W i , j Wi ,q
15
• Problem: Relevance feedback may not operate satisfactorily, if the
identified relevant documents do not form a tight cluster.
• Possible solution: Cluster the identified relevant documents, then split the
original query into several, by constructing a new query for each cluster.
• Problem: Some of the query terms might not be found in any of the
retrieved documents. This will lead to reduction of their
relative weight in the modified query (or even elimination).
Undesirable, because these terms might still be found in future
iterations.
• Possible solutions: Ensure that the original terms are kept; or present all
modified queries to the user for review.
• Problem: New query terms might be introduced that conflict with the
intention of the user
•Possible solutions: Present all modified queries to the user for review.
Refinement by relevance feedback (cont.)
16
Refinement by relevance feedback (cont.)
• Conclusion: Experimentation showed that user relevance
feedback in the vector model gives good results.
• However:
• Users are sometimes reluctant to provide explicit feedback
• Results in long queries that needs more computation to
retrieve, which is a lot of time for search engines.
• Makes it harder to understand why a particular document was
retrieved.
• “Fully automatic” relevance feedback: The rank values for
the documents in the first answer are used as relevance
feedback to automatically generate the second query (no human
judgment).
• The highest ranking documents are assumed to be relevant
(positive feedback only).
5. 5
Pseudo Relevance Feedback
• Just assume the top m retrieved documents are relevant,
and use them to reformulate the query.
• Allows for query expansion that includes terms that are
correlated with the query terms.
Two strategies:
– Local strategies: Approaches based on information derived
from set of initially retrieved documents (local set of
documents)
– Global strategies: Approaches based on global information
derived from document collection
Pseudo Feedback Architecture
Rankings
IR
System
Document
corpus
Ranked
Documents
1. Doc1
2. Doc2
3. Doc3
.
.
Query
String
Revised
Query
ReRanked
Documents
1. Doc2
2. Doc4
3. Doc5
.
.
Query
Reformulation
1. Doc1
2. Doc2
3. Doc3
.
.
Pseudo
Feedback
Local analysis
Examine documents retrieved for query to determine
query expansion
No user assistance
• Synonymy association: terms that frequently co-occur
inside local set of documents
At query time, dynamically determine similar terms
based on analysis of top-ranked retrieved documents.
Base correlation analysis on only the “local” set of
retrieved documents for a specific query.
Avoids ambiguity by determining similar (correlated)
terms only within relevant documents.
“Apple computer” “Apple computer Powerbook
laptop”
Association Matrix
w1 w2 w3 …………………..wn
w1
w2
w3
.
.
wn
c11 c12 c13…………………c1n
c21
c31
.
.
cn1
cij: Correlation factor between term i and term j
6. 6
21
Clusters
• Synonymy association: terms that frequently co-occur
inside local set of documents
• Clustering techniques
– Query-index term association matrix (normalised)
– Where, tf(ti, d) is frequency of term i in document d
; Ci,j is association factor between term i and term j
;
– Term-term (e.g., stem-stem) association matrix is
normalised using mi,j
– Normalized score mi,j is 1 if two terms have the
same frequency in all documents
Dl
d
j
i
j
i d
t
tf
d
t
tf
c )
,
(
)
,
(
,
j
i
j
j
i
i
j
i
j
i
c
c
c
c
m
,
,
,
,
,
Example
• Given:
Doc 1 = D D A B C A B C
Doc 2 = E C E A A D
Doc 3 = D C B B D A B C A
Doc 4 = A
• Query: A E E
What is the New Reformulated Query using
synonymous association matrix?
Global analysis
• Expand query using information from whole set of
documents in collection
• Approach to select terms for query expansion
– Determine term similarity through a pre-computed
statistical analysis of the complete corpus.
• Compute association matrices which quantify term
correlations in terms of how frequently they co-occur.
• Expand queries with statistically most similar terms.
Problems with Global Analysis
• Term ambiguity may introduce irrelevant
statistically correlated terms.
– “Apple computer” “Apple red fruit computer”
• Since terms are highly correlated anyway,
expansion may not retrieve many additional
documents.
7. 7
Global vs. Local Analysis
• Global analysis requires intensive term correlation
computation only once at system development time.
• Global – Thesaurus used to help select terms for
expansion.
• Local analysis requires intensive term correlation
computation for every query at run time (although
number of terms and documents is less than in global
analysis).
• Local – Documents retrieved are examined to
automatically determine query expansion. No relevance
feedback needed.
• Generally local analysis gives better results.
Query Expansion Conclusions
• Expansion of queries with related terms can improve
performance, particularly recall.
• However, must select similar terms very carefully to avoid
problems, such as loss of precision.
26
27
Thank you