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Dmitry Davidov, Ari Rappoport The Hebrew University ACL 2006

Efficient Unsupervised Discovery of Word Categories Using Symmetric Patterns and High Frequency Words. Dmitry Davidov, Ari Rappoport The Hebrew University ACL 2006. Introduction. Discovering word categories, sets of words sharing a significant aspect of their meaning context feature vectors

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Dmitry Davidov, Ari Rappoport The Hebrew University ACL 2006

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  1. Efficient Unsupervised Discovery of Word Categories Using Symmetric Patterns and High Frequency Words Dmitry Davidov, Ari Rappoport The Hebrew University ACL 2006

  2. Introduction • Discovering word categories, sets of words sharing a significant aspect of their meaning • context feature vectors • pattern-based discovery • Manually prepared pattern set (ex. x and y) • requiring POS tagging or partial or full parsing

  3. Pattern Candidates • high frequency word (HFW) • A word appearing more than TH times per million words • Ex. and, or, from, to … • content word (CW) • word appearing less than TC times per a million words

  4. Pattern Candidates • meta-patterns obey the following constraints • at most 4 words • exactly two content words • no two consecutive CWs • Example • CHC, CHCH, CHHC, and HCHC • from x to y (HCHC), x and y (CHC), x and a y (CHHC)

  5. Symmetric Patterns • In order to find a usable subset from pattern candidates, we focus on the symmetric patterns • Example • x belongs to y (asymmetric relationships) • X and y (asymmetric relationships)

  6. Symmetric Patterns • We use single pattern graphG(P) to identifying symmetric patterns • there is a directed arc A(x, y) from node x to node y iff • the words x and y both appear in an instance of the pattern P as its two CWs • x precedes y in P • SymG(P), the symmetric subgraph of G(P), containing only the bidirectional arcs and nodes of G(P)

  7. Symmetric Patterns • We compute three measures on G(P) • M1 counts the proportion of words that can appear in both slots of the pattern • M2, M3 measures count the proportion of the number of symmetric nodes and edges in G(P)

  8. Symmetric Patterns • We removed patterns that appear in the corpus less than TP times per million words • We remove patterns that are not in the top ZT in any of the three lists • We remove patterns that are in the bottom ZB in at least one of the lists

  9. Discovery of Categories • words that are highly interconnected are good candidates to form a category • word relationship graph G • merging all of the single-pattern graphs into a single unified graph • clique是一個圖中兩兩相鄰的一個點集，或是一個完全子圖

  10. The Clique-Set Method • Strong n-cliques • subgraphs containing n nodes that are all bidirectionally interconnected • A clique Q defines a category that contains the nodes in Q plus all of the nodes that are • (1) at least unidirectionally connected to all nodes in Q • (2) bidirectionally connected to at least one node in Q

  11. The Clique-Set Method • we use 2-cliques • For each 2-cliques, a category is generated that contains the nodes of all triangles that contain 2-cliques and at least one additional bidirectional arc. • For example • ‘book and newspaper’, ‘newspaper and book’, ‘book and note’, ‘note and book’ and ‘note and newspaper’

  12. The Clique-Set Method • a pair of nodes connected by a symmetric arc can appear in more than a single category • define exactly three strongly connected triangles, ABC,ABD,ACE • arc (A,B) yields a category {A,B,C,D} • arc (A,C) yields a category {A,C,B,E}

  13. Category Merging • In order to cover as many words as possible, we use the smallest clique, a single symmetric arc. This creates redundant categories • We use two simple merge heuristics • If two categories are identical we treat them as one • Given two categories Q,R, we merge them iff there’s more than a 50% overlap between them

  14. Corpus Windowing • In order to increase category quality and remove categories that are too context-specific • Instead of running the algorithm of this section on the whole corpus, we divide the corpus into windows of equal size • we select only those categories that appear in at least two of the windows

  15. Evaluation • three corpora, two for English and one for Russian • BNC, containing about 100M words • Dmoz, 68GB containing about 8.2G words from 50M web pages • Russian corpus was assembled from many web sites and carefully filtered for duplicates, to yield 33GB and 4G words

  16. Evaluation • Thresholds • thresholds TH, TC, TP ,ZT ,ZB ZB, were determined by memory size • 100, 50, 20, 100, 100 • window size • 5% of the different words in the corpus assigned into categories V=number of different words in the corpus. W=number of words belonging to at least one categories. C= is the number of categories AS= is the average category size

  17. Human Judgment Evaluation • Part I • given 40 triplets of words and were asked to rank them using 1-5 (1 is best) • 40 triplets were obtained as follows • 20 of our categories were selected at random from the non-overlapping categories • 10 triplets were selected from the categories produced by k-means (Pantel and Lin, 2002) • 10 triplets were generated by random selection of content words

  18. Human Judgment Evaluation • Part II, subjects were given the full categories of the triplets that were graded as 1 or 2 in Part I • They were asked to grade the categories from 1 (worst) to 10 (best) according to how much the full category had met the expectations they had when seeing only the triplet

  19. Human Judgment Evaluation • shared meaning • average percentage of triplets that were given scores of 1 or 2

  20. WordNet-Based Evaluation • Took 10 WN subsets referred to as ‘subjects’ • selected at random 10 pairs of words from each subject • For each pair we found the largest of our discovered categories containing it

  21. WordNet-Based Evaluation • For each found category C containing N words • Precision: the number of words present in both C and WN divided by N • Precision*: the number of correct words divided by N (Correct words are either words that appear in the WN subtree, or words whose entry in the American Heritage Dictionary) • Recall: the number of words present in both C and WN divided by the number of (single) words in WN

  22. WordNet-Based Evaluation • Number of correctly discovered words (New) that are not in WN. The Table also shows the number of WN words (:WN)

  23. WordNet-Based Evaluation • BNC ‘all words’ precision of 90.47%. This metric was reported to be 82% in (Widdows and Dorow, 2002).

  24. Discussion • Evaluate the task is difficult • Ex. {nightcrawlers, chicken,shrimp, liver, leeches} • The first pattern-based lexical acquisition method that is fully unsupervised • requiring no corpus annotation or manually provided patterns or words • Categories are generated using a novel graph clique-set algorithm

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