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Using Latent Semantic Analysis to Find Different Names for the Same Entity in Free Text Tim Oates, Vinay Bhat, Vishal Shanbhag, Charles Nicholas University of Maryland - Baltimore County ACM Web Information Data Management, 2002:31-35 Conley Read cread@cs.ucr.edu Computer Science & Engineering University of California - Riverside

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Overview The problem – research motivation The solution, LSA? LSA doesn’t work so well Let’s do it (LSA) again Two-stage LSA works! Create your own Corpus

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The Problem Mumbai Bombay

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Motivation al Qaeda al Qaida

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Motivation Nutrasweet aspartame

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Motivation al Qaeda cellsal Qaida networksuspects Iraqbin Laden allegedcellwarned terrorist

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An Old IR Problem … drove their car to … … minor car accident … … new 2002 car models … … car gets good mileage … … drove their automobile to … … minor automobile accident … … new 2002 automobile models … … automobile gets good mileage …

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Keyword Query: CAR … drove their car to … … minor car accident … … new 2002 car models … … car gets good mileage … … drove their automobile to … … minor automobile accident … … new 2002 automobile models … … automobile gets good mileage …

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Keyword Query: AUTOMOBILE … drove their car to … … minor car accident … … new 2002 car models … … car gets good mileage … … drove their automobile to … … minor automobile accident … … new 2002 automobile models … … automobile gets good mileage …

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Latent Semantic Analysis … drove their car to … … minor car accident … … new 2002 car models … … car gets good mileage … … drove their automobile to … … minor automobile accident … … new 2002 automobile models … … automobile gets good mileage …

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Term-Document Matrix A = m terms n documents A(I, J) = number of times term I occurs in document J

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Latent Semantic Analysis Compute singular value decomposition (SVD) of A Retain k < n largest singular values Set remainder to zero Projects terms/docs into k-dimensional space Compute similarity in that space A = U V T

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Singular Value Decomposition U V U – row corresponds to a word Σ – singular values of A V – column corresponds to a document [Berry & Fierro 1996] Numerical Linear Algebra with Applications 3(4):301-327

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Using SVD U V U – Look only at k columns (words) – Set all but k largest to zero V – Look only at k rows (documents) [Berry & Fierro 1996] Numerical Linear Algebra with Applications 3(4):301-327

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Using LSA to Find Aliases Given name N and document collection D Compute SVD of term-document matrix Retain k largest singular values Compute similarity of all terms to N Report rank-ordered list of terms True aliases for N must be high in list

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Experiment: Creating Aliases name N and document collection D Set P, a percentage S1 and S2 are two strings not in D Replace N with S1 in P% of the documents Replace N with S2 in the other documents Search for aliases for S1 Observe rank of S2 in ordered list

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Our Dataset 77 documents from www.cnn.comwww.cnn.com Shortest has 131 words, longest has 1923 “al Qaeda” occurs in 49 documents Others on politics, sports, entertainment N = “al Qaeda” S1 = “alqaeda1” S2 = “alqaeda2” P = 50

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Algorithm Parameters k – dimensionality of compressed space Small values result in spurious similarities Large values closely approximate A T – threshold on TF/IDF value More aggressive filtering with larger values Want to avoid filtering aliases Want to filter irrelevant words Term Frequency / Inverse Document Frequency We want High Retrieval (precision) and Low Miss (infrequent in collection) rates.

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Results 1: LSA Stage 1 Figure 1: Plot of Rank as a function of t for values of k.

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Results: Ontologically Dissimilar arrested government ressam lindh zubaydah raids attacks brahim passengers virginia k = 5 zubaydah raids ressam pakistani hamdi soldier trial alqaeda2 pakistan walker k = 10 zubaydah ressam raids hamdi alqaeda2 pakistani trial soldier pakistan lindh k = 20 Problem: LSA shows Organizations and Individuals as similar.

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Local Context to Ontology … list of al Qaeda leaders … … most senior al Qaeda member captured … … alleged al Qaeda representative … … photograph showing Lindh blindfolded … … with Lindh, the 21-year-old American … … Lindh pleaded guilty … Ontology: Hierarchical structuring of knowledge according to relevant or cognitive qualities. An Organization An Individual

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A Second Run of LSA For each term T in the top 250 candidates Create a document D T D T contains the words just before and just after each occurrence of T in the original corpus Run LSA on all of the D T (the new corpus) … most senior al Qaeda member captured … … photograph showing Lindh blindfolded and …

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Results 2: LSA Stage 2 Figure 2: Plot of Rank as a function of t for values of k.

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Results 2: Scaled to Figure 1 Figure 3: Plot of Rank as a function of t for values of k.

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Results 1 & 2: Comparison LSA-1 and LSA-2, Before and After.

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Results: Contextually Similar tenet suspected warned alqaeda2 terrorism terrorist anaconda potential operation operations k = 5 cells alqaeda2 network suspects germany laden alleged cell terrorist warned k = 10 cells network alqaeda2 cell terrorist alleged suspects laden singapore germany k = 20 Solution: LSA with context ranks terms by ontological similarity.

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Applications Example alias in Movie Titles: Query N = “Ocean’s 12” Use Google to get top 100 hits Run two-stage LSA algorithm Create your own corpus Submit N as Google query Create corpus from top M hits Run two-stage LSA You might retrieve: 1. GoldenEye 2. Ocean’s 11 3. Die Hard: Vengeance 4. The Italian Job

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Review Find semantically related terms Obvious solution – LSA LSA is not so good We ran LSA again! LSA is great! Create a Corpus with Google

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Your Questions? Acknowledgements Dr. Tim Oates, oates@cs.umbc.eduoates@cs.umbc.edu References – the math… Berry, M., Fierro R. 1996. Low-rank orthogonal decompositions for information retrieval applications.

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