1. Computing Word-Pair Antonymy *Saif Mohammad *Bonnie Dorr φ Graeme Hirst *Univ. of Maryland φ Univ. of Toronto EMNLP 2008

2. Introduction Antonymy: pair of semantically contrasting words. Ex: Strongly antonymous: Hot  Cold Semantically contrasting:Enemy  Fan Not antonymous:Penguin  Clown

3. Usage Detecting contradictions Detecting humor Automatic creation of thesaurus

4. Problem Definition Given a thesaurus, find out the antonymous category pairs. Assign the degree of antonymy to each pair of antonymous categories.

5. Hypothesis(1) The Co-occurrence Hypothesis of Antonyms – Antonymous word pairs occur together much more often than other word pairs.

6. Hypothesis(1) Empirical proof: – 1,000 antonymous pairs from Wordnet – 1,000 randomly generated word pairs – Use BNC as corpus, set window size 5. – Calculate the MI for each word pairs and average it AverageStandard deviation Antonymous pair0.942.27 Random pair0.010.37

7. Hypothesis(2) The Distributional Hypothesis of Antonyms – Antonyms occur in similar contexts more often than non-antonymous words – Ex work: activity of doing job play: activity of relaxation

8. Hypothesis(2) Empirical proof: – Use the same set of word pairs in hypothesis(1) – Calculate the distributional distance between their categories AverageStandard deviation Antonymous pair0.300.23 Random pair0.230.11

9. Distributional Distance between Two Thesaurus Categories c 1,c 2 : thesaurus category I(x,y):pointwise mutual information between x and y T(c):the set of all words w such that I(c,w)>0

10. Method Determine pairs of thesaurus categories that are contrasting in meaning Use the co-occurrence and distributional hypotheses to determine the degree of antonymy of word pairs

11. Method 16 affix rules were applied to Macquarie Thesaurus 2,734 word pairs were generated as a seed set. Exceptions: sect  X  insect Relatively few

12. Method 10,807 pairs of semantically contrasting word pairs from WordNetWordNet

13. Method If any word in thesaurus category C1 is antonymous to any word in category C2 as per a seed antonym pair, then the two categories are marked as contrasting. If no word in C1 is antonymous to any word in C2, then the categories are considered not contrasting

14. Method Degree of antonymy----category level – By distributional hypothesis of antonyms, we claim that the degree of antonymy between two contrasting thesaurus categories is directly proportional to the distributional closeness of the two concepts

15. Method Degree of antonymy----word level – target words belong to the same thesaurus paragraphs as any of the seed antonyms linking the two contrasting categories  highly antonymous – target words do not both belong to the same paragraphs as a seed antonym pair, but occur in contrasting categories  medium antonymous – target words with low tendency to co-occur  lowly antonymous

16. Method Adjacency Heuristic – Most thesauri are ordered such that contrasting categories tend to be adjacent

17. Evaluation 1,112 Closest-opposite questions designed to prepare students for GRE(Graduate Record Examination) – 162 questions as the development set – 950 questions as the test set

18. Evaluation Closest-opposite questions – Ex: adulterate: a. renounce b. forbid c. purify d. criticize e. correct

19. Evaluation Closest-opposite questions – Ex: adulterate: a. renounce b. forbid c. purify d. criticize e. correct 摻雜的 純淨的批評 正確 禁止聲明放棄

20. Evaluation

21. Discussion The automatic approach does indeed mimic human intuitions of antonymy. In languages without a wordnet, substantial accuracies may be achieved. Wordnet and affix-generated seed are complementary.

22. Conclusion Proposed an empirical approach to antonymy that combines corpus co-occurrence statistics with the structure of a thesaurus. The system can identify the degree of antonymy between word pairs. An empirical proof that antonym pairs tend to be used in similar contexts.

23. Thanks