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Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis

Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis. Shuai Yuan @ Emory. Semantic relatedness. Association between (two) texts according to background knowledge. Example: “Cat” <-> “mouse” “Preparing a manuscript” <-> “writing an article”

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Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis

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  1. Computing Semantic Relatedness using Wikipedia-based Explicit Semantic Analysis Shuai Yuan @ Emory

  2. Semantic relatedness Association between (two) texts according to background knowledge. • Example: • “Cat” <-> “mouse” • “Preparing a manuscript” <-> “writing an article” • “Chair” <-> “Sun”

  3. Why does it matter? Association of ideas is important for artificial intelligence. Help to extract meaningful information from texts.

  4. What is ESA? Explicit Semantic Analysis. • Explicitly represent the meaning of any text in terms of Wikipedia-based concepts • How about implicit one? “latent concepts”

  5. Indices • direct index • Concept -> words • Inversed index • Word -> Concepts

  6. Building Semantic Interpreter(1)

  7. Building Semantic Interpreter(2) • For a single word • Using TFIDF to decide weights of every concept • Discard insignificant associations.

  8. Building Semantic Interpreter(3)

  9. Building Semantic Interpreter(4)

  10. Building Semantic Interpreter(4) • For a text fragment TFID V1 W1 C1:K11 C2:K12 C3:K13 V2 W2 C1:K21 C2:K22 C3:K23 V3 W3 C1:K31 C2:K32 C3:K33 C1:K1 C2:K2 C3:K3

  11. Building Semantic Interpreter(5)

  12. Using Semantic Interpreter

  13. Concepts vector comparison. • Cosine metric.

  14. Implementation(1) • Using Wikipedia Wikipedia snapshot as of March 26, 2006. parsing the Wikipedia XML dump 2.9 GB of text in 1,187,839 articles removing small and overly specific concepts 241,393 articles removing stop words and rare words 389,202 distinct terms

  15. Implementation(2) • Using Open Directory Project (ODP, http://www.dmoz.org) ODP snapshot as of April 2004 pruning non-English material 436MB. 400k concepts and 2.8M URLs crawling all of its URLs 70 GB of additional textual data removing stop words and rare words 20,700,000 distinct terms

  16. Evaluation • The “gold standard” -- Human judgements

  17. Human evaluating word relatedness • WordSimilarity-353 collection2 containing 353 word pairs. • Hire people (13-16 for each word pair). • Average to a single relatedness score for each pair.

  18. Human evaluating doc similarity • 50 documents from the ABC’s news mail service. • Paired docs in all possible ways (how many pairs?) • Hire people (8-12 for each doc pair). • Average to a single relatedness score for each pair. • 1225= 50*49/2

  19. Results(1)

  20. Results(2)

  21. Conclusion • Explicit Semantic Analysis is a novel approach to computing semantic relatedness of natural language texts with the aid of large scale knowledge repositories (Wikipedia and the ODP). • Results are good!

  22. Q&A Thank you !

  23. Agenda or Summary Layout 10:00am 11:00am 1:00pm A second line of text could go here Discussion Item One – A Placeholder for text Add a second line of text here Discussion Item Two – A Placeholder for text Add a second line of text here Discussion Item Four – A Placeholder for text Add a second line of text here Discussion Item Five – A Placeholder for text Add a second line of text here Discussion Item Three – A Placeholder for text Add a second line of text here 2:00pm 12:00pm

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  34. Smart Art Page Layout PowerPoint 2007 Enhanced Version This chart utilizes Smart Art which is feature in PowerPoint 2007. If you wish to make charts like this and don’t have PPT 2007, we have provided the graphical elements to help you build this yourself. Here is the description of the chart. You may change or delete this text as you wish. Here is a placeholder for more text and description of the chart. Changing this text will not interfere with the formatting of this template.

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