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Parallel Clustering of English Verbs into Levin Classes

Parallel Clustering of English Verbs into Levin Classes. 6.338/18.337 Final Project Melanie Goetz Andrew Hogue May 13, 2004. Background. Levin [1993] hand-classified verbs 3086 verbs into 264 classes (with overlaps) Utilized verb arguments and alternations

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Parallel Clustering of English Verbs into Levin Classes

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  1. Parallel Clustering of English Verbs into Levin Classes 6.338/18.337 Final Project Melanie Goetz Andrew Hogue May 13, 2004

  2. Background • Levin [1993] hand-classified verbs • 3086 verbs into 264 classes (with overlaps) • Utilized verb arguments and alternations • E.g. “the glass broke” or “broke the glass” • Classes correlated with semantic meaning of verbs

  3. Our Approach • Automatically classify verbs • Build graph G with node for each word, edges if words appear in same sentence • First, build bipartite graph with verbs and prepositions • Extend with subject nouns, object nouns • Use spectral partitioning to divide verbs into classes

  4. Our Approach

  5. Our Approach

  6. Parallel Implementation • Three components: • Extract meaningful words from parsed corpus • Merge per-processor sparse matrices without bringing data to front end • Run parallel spectral partitioning on full graph

  7. Parsing • Embarrassingly parallel • Wall Street Journal corpus of 99 documents • Each processor separately extracts tree from corpus and relevant words from tree

  8. Indexing • Need to combine matrices from separate processors into one indexing scheme • Bringing to front end is inefficient • Solution: share “vocabulary lists” between processes • Allows each process to use the same index for each word

  9. Indexing

  10. Indexing

  11. Partitioning • Based on specpart.m from Meshpart toolkit • Serial version uses Cholesky decomposition • Our parallel version uses eigs() function as we only need a few eigenvalues

  12. Results • Clustered 3317 sentences from Wall Street Journal corpus • 2827 unique words • Included subjects, verbs, objects, prepositions

  13. Results - Parsing

  14. Results - Indexing May 13, 2004 6.338/18.337 Final Project 15

  15. Results - Partitioning May 13, 2004 6.338/18.337 Final Project 16

  16. Results - Clustering May 13, 2004 6.338/18.337 Final Project 17

  17. Future Work • Parse other corpora (Project Gutenberg) • Restrict word types to verb/preposition or subject/verb/object • Other ways to use eigenvectors for partitioning into more than 2 parts

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