ESP [10] 2 (1) (2),,, Extracting Domain-Specific Expressions from ESP Corpora in View of Syntacti
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1 ESP [10] 2 (1) (2),,, Extracting Domain-Specific Expressions from ESP Corpora in View of Syntactic Structures TANAKA, Shosaku KOYAMA, Yukie Ritsumeikan University, College of Letters Toji-inkitamachi 56-1, Kita-ku, Kyoto, JAPAN Nagoya Institute of Techonology, Center for Research & Development in Higher Education Gokiso-cho, Showa-ku, Nagoya, Aichi, JAPAN Abstract This paper proposes a sophisticated method for extracting domain-specific expressions on the basis of a classification model The advanced points of the proposed method are as follows: (1) it extracts domain-specific expressions per grammatical role by applying syntactic analysis to sentences and (2) it restricts expressions to extract by grammatical patterns The results show that the proposed method can extract compact and easily comprehensible expressions from ESP corpora Keyword English for specific purpose, Domain-specific expression, Syntactic analysis, Classification model 1 English for Specific Purposes: ESP English for General Purposes
2 ESP ESP [1, 5, 7, 10] ESP (1) (2) / (3) [10] (1) (2) [10] ESP / [10] 2 ESP ESP [10] Multi-Word Expression 21 ESP 2 1 ESP 2 ESP 1 m [5] 1 MI-score B-score ESP bigram [1] 2 ESP BNC applied science British National Corpus (BNC) 9 [10] 2 9 ESP / [10] [1, 5] (1)
3 (2) (3) [10] [10] 22 [10] [10] 1 1 S t, S s (a) S = S t S s / M (b) M S α 2 (c) S t, S s (b) S t, S s (a) 2 (a) M F (b) f F f S s 3 S f F 1 1-(a) M [8] Boosting Algorithm for Tree Classification: BACT BACT Support Vector Machine 1 x, t y {+1, 1} h h t,y (x) = { y y t x otherwise t x t x t, y
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8 33 [10] 2 S t, S s 0 S t, S s i = 1, 2,, n G t,i, G s,i i i i 1 (a) G i = G t,i G s,i G t,i /G s,i M i (b) M i G i α 2 (c) G t,i, G s,i (b) G t,i, G s,i (a) 2 (a) M i F i (b) f F i 32 f G s,i 3 G i f F i Web 41 [10] 14 ELPA 3 New Crown, New Horizon, Sunshine 4 Dream-Maker, English 21, Phoenix, Spectrum 21 ESP Nature The
9 53, ,840 Nature 81,330 1,974,934 ACL 159,080 3,861,550 2: Association for Computational Linguistics(ACL) 3 ESP TreeTagger 4 31 Charniak parser CD a/an/the DT 1-(b) α 099 BACT 10,000 2-(b) 7 7-gram : This paper S NP 2 NP 46,071 ACL 154, ,464 ACL 149, ACL 4 TreeTagger - a language independent part-of-speech tagger, 5 Eugene Charniak, 6 ACL all of that, each boy in DT group, DT picture story, DT United States goverment ACL that one, they both, that of knot and dale, japanese and arabic
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11 S1 S ADVP, However, NP VP these issues will not be addressed here = : however, SUBJ PRED 3: However, these issues will not be addressed here S1 S NP VP This paper proposes a new approach for word similarity measurement = : SUBJ PRED 4: This paper proposes a new approach for word similarity measurement 43 2: 431 However, these issues will not be addressed here However,, 1 S NP,VP S S NP VP SUBJ,PRED However, 3 however, SUBJ PRED 4 SUBJ PRED 46,071 ACL 154,569
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13 [1],, :,, 12, pp (2005) [2] Franzti, K, Ananiadou: Extracting Nested Collocations, Proceedings of 16th International Conference on Computational Linguistics, pp (1996) [3] Franzti, K, Ananiadou, S and Mima, H: Automatic Recognition of Multi-Word Terms: the C-value/NC-value Method, Digital Libraries, Vol 3, No 2, pp (2000) [4] Kita, K, Kato Y, Omoto, T and Yano, Y: A Comparative Study of Automatic Extraction of Collocations from Corpora: Mutal Information vs Cost Criteria,, Vol 1, No 1, pp (1994) [5] : Multi-Word Expression, 216 ESP, pp (2008) [6] : -Nature -, (in press) [7] : 1110, (2009) [8], :,, Vol 45, No 9, pp (2004) [9],, :,, Vol 10, No 1, pp (2003) [10], : ESP, 233, pp (2009) [11] Tjong Kim Sang, E, F and Buchholz, S: Introduction to the CoNLL-2000 Shared Task: Chunking, Proceedings of CoNLL-2000 and LLL-2000, pp (2000) A A1 [10] Rank C-value Expression ### The of the [ CD ] [ CD ] ### in the
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