CS276A Text Retrieval and Mining

Jan 15, 2013

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Recap of the last lectures. Introduction to Text Classification Machine Learning Algorithms for text classificationNa

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CS276A Text Retrieval and Mining

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CS276A Text Retrieval and Mining

CS276A Text Retrieval and Mining . Lecture 12 [Borrows slides from Viktor Lavrenko and Chengxiang Zhai]. Recap. Probabilistic models: Naïve Bayes Text Classification Introduction to Text Classification Probabilistic Language Models Naïve Bayes text categorization. Today.

651 views • 43 slides

CS276A Text Retrieval and Mining

CS276A Text Retrieval and Mining . Lecture 10. Recap of the last lecture. Improving search results Especially for high recall. E.g., searching for aircraft so it matches with plane; thermodynamic with heat Options for improving results… Global methods Query expansion Thesauri

666 views • 45 slides

Text Based Information Retrieval - Text Mining

Text Based Information Retrieval - Text Mining. PKB - Antonie. Background. Human dificults to process huge information Computer can do better with matemathics why don’t also use computer to process huge information? A Large text to find: Terrorist attack on 1995?

1.22k views • 37 slides

Information Retrieval and Text Mining

Today's lecture. Free text queriesRankingTf.idf weightingDocuments as vectors. What's wrong with Boolean?. Thus far, our queries have all been BooleanDocs either match or notGood for expert users with precise understanding of their needs and the corpusNot good for (the majority of) users with

763 views • 62 slides

CS276A Text Retrieval and Mining

CS276A Text Retrieval and Mining. Lecture 1. Query. Which plays of Shakespeare contain the words Brutus AND Caesar but NOT Calpurnia ? One could grep all of Shakespeare’s plays for Brutus and Caesar, then strip out lines containing Calpurnia ? Slow (for large corpora)

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CS276A Text Information Retrieval, Mining, and Exploitation

CS276A Text Information Retrieval, Mining, and Exploitation. Lecture 2 1 Oct 2002. Course structure & admin. CS276: two quarters this year: CS276A: IR, web (link alg.), (infovis, XML, P2P) Website: http://cs276a.stanford.edu/ CS276B: Clustering, categorization, IE, bio Course staff

546 views • 42 slides

CS276A Text Retrieval and Mining

CS276A Text Retrieval and Mining . Lecture 15. Thanks to Thomas Hoffman, Brown University for sharing many of these slides. Recap: Clustering 2. Hierarchical clustering Agglomerative clustering techniques Evaluation Term vs. document space clustering Multi-lingual docs Feature selection

542 views • 43 slides

CS276A Information Retrieval

CS276A Information Retrieval. Lecture 6. Plan. Last lecture Index construction This lecture Parametric and field searches Zones in documents Scoring documents: zone weighting Index support for scoring Term weighting. Parametric search.

530 views • 45 slides

CS276A Text Retrieval and Mining

CS276A Text Retrieval and Mining. Lecture 13 [Borrows slides from Ray Mooney and Soumen Chakrabarti]. Recap: The Language Model Approach to IR. Consider probability of generating the query using a language model derived from each document Usually mixed with a “ general model ”

620 views • 48 slides

CS276A Text Retrieval and Mining

CS276A Text Retrieval and Mining. Lecture 15. Thanks to Thomas Hoffman, Brown University for sharing many of these slides. Recap: Clustering 2. Hierarchical clustering Agglomerative clustering techniques Evaluation Term vs. document space clustering Multi-lingual docs Feature selection

582 views • 43 slides

CS276A Text Information Retrieval, Mining, and Exploitation

CS276A Text Information Retrieval, Mining, and Exploitation. Lecture 4 15 Oct 2002. Recap of last time. Index size Index construction techniques Dynamic indices Real world considerations. Back of the envelope index size calculation. Number of docs = n = 40M Number of terms = m = 1M

554 views • 39 slides

CS276A Text Retrieval and Mining

CS276A Text Retrieval and Mining. Lecture 10. Recap of the last lecture. Improving search results Especially for high recall. E.g., searching for aircraft so it matches with plane; thermodynamic with heat Options for improving results… Global methods Query expansion Thesauri

539 views • 45 slides

CS276A Information Retrieval

CS276A Information Retrieval. Lecture 3. Recap: lecture 2. Stemming, tokenization etc. Faster postings merges Phrase queries. This lecture. Index compression Space estimation. Corpus size for estimates. Consider n = 1M documents, each with about L =1K terms.

554 views • 47 slides

CS276A Text Information Retrieval, Mining, and Exploitation

CS276A Text Information Retrieval, Mining, and Exploitation. Project Information and Resources Project Ideas 3 Oct 2002. Project: The idea. An opportunity to do a reasonable sized project on a topic of interest related to the course Open ended, but... Have a clear, focused objective

569 views • 41 slides

CS276A Information Retrieval

CS276A Information Retrieval. Lecture 8. Recap of the last lecture. Vector space scoring Efficiency considerations Nearest neighbors and approximations. This lecture. Results summaries Evaluating a search engine Benchmarks Precision and recall. Results summaries. Summaries.

538 views • 44 slides

CS276A Information Retrieval

CS276A Information Retrieval. Lecture 4. Recap of last time. Index compression Space estimation. This lecture. “Tolerant” retrieval Wild-card queries Spelling correction Soundex. Wild-card queries. Wild-card queries: *. mon*: find all docs containing any word beginning “mon”.

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CS276A Text Information Retrieval, Mining, and Exploitation

CS276A Text Information Retrieval, Mining, and Exploitation. Lecture 13 19 November, 2002. Recap. Last week web search overview pagerank HITS Last lecture HITS algorithm using anchor text topic-specific pagerank. Today’s Topics. Behavior-based ranking Crawling and corpus construction

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Information Retrieval and Text Mining

Lecture 2. Information Retrieval and Text Mining. Recap of the previous lecture. Basic inverted indexes: Structure: Dictionary and Postings Key step in construction: Sorting Boolean query processing Simple optimization Linear time merging Overview of course topics. Plan for this lecture.

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CS276A Text Information Retrieval, Mining, and Exploitation

CS276A Text Information Retrieval, Mining, and Exploitation. Supplemental Min-wise Hashing Slides [Brod97,Brod98] (Adapted from Rajeev Motwani’s CS361A slides). Set Similarity. Set Similarity (Jaccard measure)

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CS276A Information Retrieval

CS276A Information Retrieval. Lecture 5. Plan. Last lecture: Tolerant retrieval Wildcards Spell correction Soundex This time: Index construction. Matching trigrams. Consider the query lord – we wish to identify words matching 2 of its 3 bigrams ( lo, or, rd ). lo. alone. lord.

580 views • 45 slides

CS276A Text Information Retrieval, Mining, and Exploitation

CS276A Text Information Retrieval, Mining, and Exploitation. Project Information and Resources / Project Ideas II 10 Oct 2002. Schedule. Last thursday – Project overview and some ideas Today – More detail on projects Size, write-up, and grading criteria More project ideas

553 views • 39 slides

CS276A Text Retrieval and Mining

CS276A Text Retrieval and Mining. Lecture 12 [Borrows slides from Viktor Lavrenko and Chengxiang Zhai]. CS276 Information Retrieval and Web Search. Lecture 12: Language Models [Borrows some slides from Viktor Lavrenko and Chengxiang Zhai]. Recap.

481 views • 44 slides

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