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Unformatted text preview: 2 ) 2 log P(geological-formation ) log P(hill) + log P(coast ) 2 ln 0.00176 ln 0.0000189 + ln 0.0000216 = .59 = Dan Jurafsky The (extended) Lesk Algorithm •  A thesaurus ­based measure that looks at glosses •  Two concepts are similar if their glosses contain similar words •  Drawing paper: paper that is specially prepared for use in dra^ing •  Decal: the art of transferring designs from specially prepared paper to a wood or glass or metal surface •  For each n ­word phrase that’s in both glosses •  Add a score of n2 •  Paper and specially prepared for 1 + 22 = 5 •  Compute overlap also for other rela-ons •  glosses of hypernyms and hyponyms Dan Jurafsky Summary: thesaurus ­based similarity sim path (c1, c2 ) = 1 pathlen(c1, c2 ) sim resnik (c1, c2 ) = ! log P( LCS (c1, c2 )) sim lin (c1, c2 ) = 2 log P( LCS (c1, c2 )) log P(c1 ) + log P(c2 ) 1 sim jiangconrath (c1, c2 ) = log P(c1 ) + log P(c2 ) ! 2 log P( LCS (c1, c2 )) sim eLesk (c1, c2 ) = # r, q" RELS overlap(gloss(r (c1 )), gloss(q(c2 ))) Dan Jurafsky Libraries for compu1ng thesaurus ­based similarity •  NLTK •  hnp://nltk.github.com/api/nltk.corpus.reader.html?highlight=similarity  ­ nltk.corpus.reader.WordNetCorpusReader.res_similarity •  WordNet::Similarity •  hnp://wn ­similarity.sourceforge.net/ •  Web ­based interface: •  hnp://marimba.d.umn.edu/cgi ­bin/similarity/similarity.cgi 48 Dan Jurafsky Evalua1ng similarity •  Intrinsic Evalua-on: •  Correla-on between algorithm and human word similarity ra-ngs •  Extrinsic (task ­based, end ­to ­end) Evalua-on: •  Malapropism (spelling error) detec-on •  WSD •  Essay grading •  Taking TOEFL mul-ple ­choice vocabulary tests Levied is closest in meaning to:! imposed, believed, requested, correlated! Word Meaning and Similarity Word Similarity: Thesaurus Methods Word Meaning and Similarity Word Similarity: Distribu-onal Si...
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This document was uploaded on 02/14/2014.

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