KCC2011 우수발표논문 휴먼오피니언자동분류시스템구현을위한비결정오피니언형용사구문에대한연구 1) Study on Domain-dependent Keywords Co-occurring with the Adjectives of Non-deterministic Opinion

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Transcription:

KCC2011 우수발표논문 휴먼오피니언자동분류시스템구현을위한비결정오피니언형용사구문에대한연구 1) Study on Domain-dependent Keywords Co-occurring with the Adjectives of Non-deterministic Opinion 요약 본연구에서는, 웹문서로부터특정상품에대한의견문장을분석하는오피니언마이닝 (Opinion Mining) 연구의일환으로, 특히함께공기하는자질명사에따라그극성값이달라지는 비결정오피니언어휘 의처리를위해서도메인을 맛집 으로한정하여공기하는도메인키워드의목록을결정하고, 이를부분문법그래프 (Local Grammar Graphs) 방법론을통해서이들간의어휘 통사적관계를결정해주었다. 1.,. (Information Extraction),, (review) (Opinion Mining). (Subjectivity) [1],[2], (Polarity) [3],[4],[5].,..,.. 1).,.. DECO-AS 2) [6] <QXDE>, <QXAD>. (Local Grammar Graph)[7],[8].. 2.,. 2) DECO-AS. 12,000,,,.

<1> DECO-AS 1,,. 8000. DECO-AS,. 890, 1. 220, 374 594. DECO-AS <QXPO, QXSP> <QXNG>,<QXSN>. <QXDE>,<QXAD> 296..,. 32., DECO-S (QDIM).... 16,. <2>. (1) a. b. (2) a.. b. (1). (2)... 259 105 43. 259, 111 79 2.

(Dimension) 1, 2, 3,,,,,,,,,,,,,, ( ), 16 32. 33.3%, 3 1..,. ( 890 ) 220 374 594(66.7%) 105 43-32 259(33.3%) 표 1. 맛집 코퍼스실현형용사유형결과 3. (QDIM),,. 3,700 2. < 3>. 3.,,,,... DECO-NS. DECO-NS. 16,964 62.. <QFOD> 239 <QPRO> 659 <QBUI> 247 <QLOC> 381 <QHUM> 1,611 <QEVN> <QPES> 1,047 <QHUM> 381 <QCOG> 451 <QPRO> 659 2.,,. 4. LGG (Local Grammar Graph: LGG). (LGG), (Maurice Gross) [7][8], (lexicon). (finite-state automata). <4> LGG.,.,.

,, <MOT> 3. <MOT>. 3. 4. LGG (QDEE), (QPLE), (QLIG), (QSOF), (QBIG),. Unitex.. FoodConcPosi.grf FoodConcNega.grf FoodAbsN1Posi.grf N1 FoodAbsN1Nega.grf FoodAbsN2Posi.grf N2 FoodAbsN2Nega.grf LocationConcPosi.grf LocationConcNega.grf LocationAbsPosi.grf LocationAbsNega.grf ServiceConcPosi.grf ServiceConcNega.grf ServiceAbsN1Posi.grf N1 ServiceAbsN1Nega.grf ServiceAbsN2Posi.grf N2 ServiceAbsN2Nega.grf ReputationConcPosi.grf ReputationConcNega.grf ReputationAbsN1Posi.grf N1 ReputationAbsN1Nega.grf ReputationAbsN2Posi.grf N2 ReputationAbsN2Nega.grf PriceConcPosi.grf PriceConcNega.grf 표 5. 맛집 평가분석을위한비결정형용사 LGG 5.. (LGG). (twitter) 1,000. LGG.. + LGG recall 572/1000(57.2%) 600/1000(60%) precise 572/674(84.9%) 600/702(85.5%) 5. LGG 24 LGG LGG 1,000 702, 600. 3%.

..,,., LGG.. 6. [1] J. M. Wiebe, R. Bruce, & T. O 'Hara. 1999. Development and use of a gold standard data set for Subjectivity classifications. Proceedings of the 37th Annual Meeting of the Assoc. for Computational Linguistics (ACL-99). pp. 246-253. [2] Riloff, Ellen, J. Wiebe, and T. Wilson. 2003. Learning subjective nouns using extraction pattern bootstrapping. In Conf. on Natural Language Learning (CoNLL), pages 25-32. [3] V. Hatzivassiloglou et al. 1997. Predicting the semantic orientation of adjectives, Proceedings of the eighth conference on Europe anchapter of the Association for Computational Linguistics, p.174-181. [4] P. D. Turney. 2002. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. [5] C. Fellbaum. 1998. WordNet: An Electronic Lexical Database, MIT Press. [6]. 2010. Korea Electronic Dictionary DECO, TR of DICORA 2010-02, HUFS. [7] M. Gross. 1988. The use of finite automata in the lexical representation of natural languages. In Electronic Dictionaries and Automata in Computational Linguistics, number 337 in Lecture Notes in Computer Science, pages 34-50. [8] M. Gross, 1997. The construction of Local Grammars. In Finite-State Language Processing, E.Roche & Y.Schabes(eds). Language. Speech. and Communication. Cambridge. Mess. :MITPress. P329-354