Selecting Proper Lexical Paraphrase for Children
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Tomoyuki Kajiwara, Hiroshi Matsumoto and Kazuhide Yamamoto. Selecting Proper Lexical Paraphrase for Children. The 25th Conference on Computational Linguistics and Speech Processing (ROCLING 2013), pp.59-73 (2013.10)
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Selecting Proper Lexical Paraphrase for Children
- 1. Selecting Proper Lexical Paraphrase for Children Tomoyuki Kajiwara Hiroshi Matsumoto Kazuhide Yamamoto Nagaoka University of Technology
- 2. Lexical Paraphrase for Children Elementary school Japanese dictionary 【大詰め：final stage】 The last scene of the play 芝居の最後の場面 Newspaper for Children Basic Vocabulary to Learn 5,404 words 最後の大一番 Total annual number of vocabulary 200,000 words Selected by the similarity between the headword Big match of the last Newspaper for Adults 大詰めの大一番 Big match of the final stage 2
- 3. BVL : Basic Vocabulary to Learn Vocabulary that registered in the elementary school dictionary Vocabulary that registered in the general dictionary Vocabulary that elementary school students General Vocabulary Vocabulary to Learn 25,000 words can use sufficient Vocabulary of the minimum necessary for a living 3 Basic Vocabulary to Learn 5,404 words Paraphrase to BVL from GV and VL Reading assistance for elementary school students Basic Vocabulary 2,000 words
- 4. Related Works • Paraphrase of utilizing a dictionary – headword → headword • Fujita et al. (2000)、Mino and Tanaka (2011) – headword → word from the end of definition statement • Kaji et al. (2002)、Mino and Tanaka (2011)、 Kajiwara and Yamamoto(2013) ”The definition statements are simpler than the headwords” ”The last segment represents the meaning of the headword” 4
- 5. Problem of Related Works Definition 【 大詰め 】芝居の最後の場面 【final stage】the last scene of the party Paraphrase ✕ 大詰めの大一番 → 場面の大一番 Big match of the final stage → Big match of the scene ✔ 大詰めの大一番 → 最後の大一番 Big match of the final stage → Big match of the last Appropriate target words are not always found at the end of definitions 5
- 7. Proposed Method（1/2） • Acquisition of the Target Word Candidates ① Difficult word is extracted ② Entries of the difficult word are searched ③ Words are extracted if they are the same part-of-speech as the difficult word 6 ① ③ Original Sentence ・・・ People professor ・・・ 【professor】People of status as professor. 【professor】Status as professor. 【professor】Teach learning and skill. 【professor】University teacher. Japanese Dictionary Status Professor Learning Skill University Teacher ②
- 8. Proposed Method（2/2） • Selection of the Proper Target Word ④ Simple words are extracted ⑤ Similarities of meaning are calculated ⑥ Simple word with the highest similarity is selected 7 Basic Vocabulary to Learn People Learning University Skill Teacher People Status Professor Learning Skill University Teacher ：0.17 ：0.11 ：0.08 ：0.13 ：0.25 ④ ⑤ ⑥
- 9. Experiments
- 10. Comparative Methods • Acquisition of the Target Word Candidates One word is extracted From the end of definition statements If it is the same part-of-speech as the difficult word • Selection of the Proper Target Word Weighted voting by following methods • Frequency • Co-occurrence frequency • Point-wise Mutual Information • Tri-gram frequency • Cosine similarity between document vectors 8
- 11. Experimental Setup • Experimental object : 152 difficult words – Do not appear in BVL – Appear more than 50 times in the Mainichi News Paper published in 2000 – Include paraphrasable simple words in the definition statements • Dictionary : Three Japanese dictionary • Thesaurus : Japanese WordNet 9
- 12. Procedure (1/2) • Experiments on the 52 difficult words – Decide weight • Experiments on the 100 difficult words – Weighted voting • Evaluation – Three evaluator are judged – Decide by majority vote – Definition of “paraphrasable” The simple word can be replaced with difficult word in the original sentence 10
- 13. Procedure (2/2) ③ Nouns are extracted 11 ① Difficult word is extracted Original Sentence ・・・ People professor ・・・ ② Entries of the professor are searched 【professor】People of status as professor. 【professor】Status as professor. 【professor】Teach learning and skill. 【professor】University teacher. Japanese Dictionary Status Professor Learning Skill University Teacher Basic Vocabulary to Learn People Learning University Skill Teacher People Status Professor Learning Skill University Teacher ：0.17 ：0.11 ：0.08 ：0.13 ：0.25 ④ Simple words are extracted ⑤ Similarities of meaning are calculated
- 14. Result (1/3) • Acquisition of the Target Word Candidates – More paraphrasable simple words are acquired – Only 3.2 points difference Number of paraphrasable words Percentage of paraphrasable words Proposed 165 / 221 74.7 % Comparative 158 / 221 71.5 % Many paraphrasable simple words appear at the end of definition statements 12
- 15. Result (2/3) 0 10 20 30 40 50 60 70 13 【Baseline】Randomness 【Proposed】WordNet-similarity (1) Frequency (2) Co-occurrence Frequency (3) Point-wise Mutual Information (4) Tri-gram frequency (5) Cosine similarity Acquisition by comparative method Acquisition by proposed method
- 16. Result (3/3) 0 10 20 30 40 50 60 70 14 【Baseline】Randomness 【Proposed】WordNet-similarity A) Weightless voting by comparative methods (1)-(5) B) Weighted voting by comparative methods (1)-(5) C) Weightless voting adds the WordNet-similarity to the A) D) Weighted voting adds the WordNet-similarity to the B) Acquisition by comparative method Acquisition by proposed method
- 17. Erroneous Examples (1/2) • Two or more simple words have the highest similarity Example • Original : A summary of the main points. • Definition :【Points】essential, score, game, spot essential score game spot The method utilizing frequency or context information selected paraphrasable word 15 : similarity 1.0 : similarity 1.0 : similarity 1.0 : similarity 1.0
- 18. Erroneous Examples (2/2) • The non-paraphrasable word have the highest similarity Example • Original : I can play the program during recording. • Definition : 【Play】Use the garbage again. What was gone once again regains power and life. 16 use : paraphrasable, similarity 0.8 power : non-paraphrasable, similarity 1.0 The method utilizing frequency or context information selected paraphrasable word
- 19. Conclusion We paraphrase difficult word to simple word with the highest similarity using the whole definition statements • Acquisition of the Target Word Candidates – More paraphrasable simple words are acquired – Many of them appear at the end of definitions • Selection of the Proper Target Word The selection based on the similarity is better than the selection by frequency or context information 17