'Inverse document frequency' presentation slideshows

Introducing ranked retrieval

1.13k views • 90 slides

Introducing ranked retrieval

1.1k views • 90 slides

Introduction to Information Retrieval

Introduction to Information Retrieval. Definition. Information retrieval (IR) is the task of finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need (usually expressed as a query)

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Introducing ranked retrieval

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Introduction to Information Retrieval

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Text Retrieval Algorithms

Text Retrieval Algorithms. Data-Intensive Information Processing Applications ? Session #4. Jimmy Lin University of Maryland Tuesday, February 23, 2010.

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TF-IDF

TF-IDF. David Kauchak cs160 Fall 2009 adapted from: http://www.stanford.edu/class/cs276/handouts/ lecture6-tfidf. ppt. Administrative. Homework 3 available soon Assignment 2 available soon Popular media article. Ranked retrieval. Thus far, our queries have all been Boolean

943 views • 51 slides

Discourse Structure and Discourse Coherence

Discourse Structure and Discourse Coherence. Julia Hirschberg CS 4705. Thanks to Dan Jurafsky, Diane Litman, Andy Kehler, Jim Martin . What makes a text or dialogue coherent? .

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Models for IR

Models for IR. Adapted from Lectures by Berthier Ribeiro-Neto (Brazil), Prabhakar Raghavan (Yahoo and Stanford) and Christopher Manning (Stanford). Introduction. Docs DB. Index Terms. Doc. abstract. match. Ranked List of Docs. Information Need. Query. Introduction.

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Computing Relevance, Similarity: The Vector Space Model

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Introduction to Lucene & Solr and Use-cases October Solr/Lucene Meetup

Introduction to Lucene & Solr and Use-cases October Solr/Lucene Meetup. Rahul Jain @rahuldausa. Who am I?. Software Engineer 7 years of programming experience Areas of expertise/interest High traffic web applications JAVA/J2EE Big data, NoSQL Information-Retrieval, Machine learning.

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Boolean and Vector Space Retrieval Models

Boolean and Vector Space Retrieval Models. Many slides in this section are adapted from Prof. Joydeep Ghosh (UT ECE) who in turn adapted them from Prof. Dik Lee (Univ. of Science and Tech, Hong Kong). Retrieval Models. A retrieval model specifies the details of: Document representation

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Comparing Twitter Summarization Algorithms for Multiple Post Summaries

Comparing Twitter Summarization Algorithms for Multiple Post Summaries. David Inouye and Jugal K. Kalita SocialCom 2011 2013 May 10 Hyewon Lim. Outline . Introduction Related Work Problem Definition Selected Approaches for Twitter Summaries Experimental Setup Results and Analysis

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Automated Gene Summary: Let the Computer Summarize the Knowledge

Automated Gene Summary: Let the Computer Summarize the Knowledge. Xu Ling Department of Computer Science University of Illinois at Urbana-Champaign. The Reality of Scientific Literature. Hard to keep up manual curation!. Automated Gene Summarization. Gene summary. . . . . . . . . .

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Ranking in Information Retrieval Systems

Ranking in Information Retrieval Systems. Prepared by: Mariam John CSE 6392 03/23/2006. Introduction. Basic Assumption: A document is a bag of words A keyword query is a small set of words

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Web Intelligence Text Mining, and web-related Applications

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Term weighting and vector representation of text

Term weighting and vector representation of text. Lecture 3. Last lecture. Tokenization Token, type, term distinctions Case normalization Stemming and lemmatization Stopwords 30 most common words account for 30% of the tokens in written text . Last lecture: empirical laws.

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