3339 Web Web 1 Web Web 2 3 Wikipedia 19),24) Web 7) Web ID 17) Table 1 47 semantic categories, represented in the form of ID:
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1 Vol. 52 No (Dec. 2011) Web Web CGM Web Web Web Wikipedia Web Semi-automatically Building Linguistic Resources for Word Sense Disambiguation of Web Text Hideaki Muramoto, 1 Nobuhiro Kaji, 2 Naoki Yoshinaga 2 and Masaru Kitsuregawa 2 With the recent advent of consumer generated media (CGM) on the Web, the textual data on the Web has been given much attention as a target of social analysis or marketing. To extract useful information from such texts, it is crucial to precisely distinguish meanings of polysemous words (i.e., word sense disambiguation or WSD). However, due to the tremendous labor required to build a large amount of linguistic resources for WSD (e.g., training examples or dictionaries), it is still hard to perform WSD with enough accuracy. This is especially problematic in dealing with Web texts, which contains much more diverse topics than conventional news articles. To overcome this, we present a semi-automatic approach to building those linguistic resources from existing Web data. Our experiments confirmed that the proposed method is indeed able to build much larger linguistic resources than existenting ones. We also investigated the performance of WSD systems learned from those linguistic resources. 1. Web CGM Consumer Generated Media Web 8),10),20),21) 1 2 Web word sense disambiguation 11),16) e.g., e.g., 1 1 Graduate School of Information Science and Technology, the University of Tokyo 2 Institute of Industrial Science, the University of Tokyo c 2011 Information Processing Society of Japan
2 3339 Web Web 1 Web Web 2 3 Wikipedia 19),24) Web 7) Web ID 17) Table 1 47 semantic categories, represented in the form of ID: category name. For detailed explanations of each category, interested readers may refer to the definitions of Sekine s extended named entity hierarchy GPE GOE Web Web 17)
3 3340 Web WordNet 13) synset 11),16) 9) SemEval coarse-graind WSD 15) supersense tagging 4) class-based WSD 9) 1 WordNet synset 1 14) 10 IREX 19) 2 Sekine ),18) Whitelaw 32 22) one-versus-the-rest ) ) bag-of-words n-gram n=1, 2, Wikipedia Wikipedia 3.1 Wikipedia Wikipedia Wikipedia Wikipedia Wikipedia Wikipedia
4 3341 Web Wikipedia Wikipedia [ ] (1) a. [ ] b. (1a) Wikipedia (1b) 3.2 Wikipedia (1) (2) a. Wikipedia 1 25) Table 2 Mapping rules between hypernyms and semantic categories, and Wikipedia articles that are associated with the semantic categories. ( )... UFJ... ( ) h h LinkNum(h) h LinkNum(h) LinkNum(h) h LinkNum(h) h 5 4. Web
5 3342 Web 3 Table 3 Examples of the semantic category dictionary WordNet Web Web 4.2 7) Web (w, h) h w 23) 26) w h w h w h h w c h c h c 1 c 3 2 c 3 h p(c h) τ h freq(c, h) p(c h) = c C freq(c,h) freq(c, h) c h 4 C 47 4 w h c 1 w c 5. Web Wikipedia Wikipedia 1 MeCab 2 J.DepP 3 Web Wikipedia 3 Wikipedia Wikipedia 4 5 Web ynaga/jdepp/
6 3343 Web 4 ID Table 4 Target words in the polysemous data. The numbers in the parentheses are the indices of the semantic categories assigned in the data. (1, 43) (11, 23, 28) (4, 12, 13, 47) (4, 18, 30) (11, 29, 43, 46) (11, 30) (4, 29) (11, 23, 28) (29, 30, 32, 47) (4, 11) (4, 23, 28) (12, 13, 30, 32, 47) (8, 43) (1, 23, 28) (4, 43) (4, 20) (1, 30) (18, 23, 28) (18, 23, 47) (8, 36, 47) (23, 28, 34) (1, 2, 18, 23, 27) (2, 11) (4, 47) (31, 47) (4, 34) (36, 42, 47) (4, 30, 47) (14, 17, 23, 28) (28, 38) 5 ID Table 5 Target words in the monosemous data. The numbers in the parentheses are the indices of the semantic categories assigned in the data. (1) (30) (11) (8) (13) (11) (30) (1) (20) (16) (34) (8) (32) (6) (8) (27) (11) (11) (8) (17) (16) (34) (16) (16) (30) (28) (34) (8) (6) (34) (32) (1) (30) (37) (11) (34) (18) (23) (13) (30) Wikipedia 80% 1 24) Table 6 6 Number of training examples in each semantic category. 859,435 14,454 4,077 34,195 29,665 13,047 76, , ,641 4,286 GPE 1,294,979 10, ,546 15,173 2,160 GOE 459, , ,486 2,283 4,742 3,871 3,040 22,484 18,134 2,679 7,203 16,621 73,036 42, ,701 58, ,006 54, ,700 84,948 35,871 85,566 94,170 5,842 16,781 50,605 5,137 92,794 10,946 16,434 5,719 5,438,637 7 Table 7 Comparison with existing linguistic resources. 24) Wikipedia 326,966 5,438,
7 3344 Web Fig. 1 1 Relation between the number of training examples and classification accuracy. Fig. 2 2 Effect of pruning by the semantic category dictionary τ τ=0 h c 1 τ= τ=0 τ 3% 5% τ τ τ τ τ τ=1 τ
8 3345 Web Table 8 8 Accuracy comparison with other classification methods. (3) ) 47 first sense heuristics 11),16) 100% 1 10 first sense heuristics 100% ) First sense Nan Nan first sense heuristics 75.7% 95.1% 5.5 (4) a b. 1 Wikipedia Bunescu 2)
9 3346 Web (4a) (4b) (4a) Web h= w= h c (5) 1 (5) (4b) Villeneuve 1) Wikipedia Wikipedia IREX IREX 6. Wikipedia Bunescu 2) Cucerzan 5) Wikipedia Wikipedia Wikipedia 1 1 Mihalcea 12) Wikipedia 2 Wikipedia Mihalcea 9,489 1 Whitelaw 22) Web Wikipedia 1 Bunescu Wikipedia 2 Mihalcea WordNet
10 3347 Web 11),16) 27) Wikipedia 7. Web Wikipedia Web 1) Brosseau-Villeneuve, B., Nie, J. and Kando, N.: Towards an optimal weighting of context words based on distance, Proc. COLING, pp (2010). 2) Bunescu, R. and Pasca, M.: Using Encyclopedic Knowledge for Named entity Disambiguation, Proc. EACL, pp.9 16 (2006). 3) Chawla, N., Japkowicz, N. and Kotcz, A.: Editorial: Special Issue on Learning from Imbalanced Data Sets, SIGKDD Explor. Newsl., Vol.6, pp.1 6 (2004). 4) Ciaramita, M. and Y., A.: Broad-coverage Sense Disambiguation and Information Extraction with a Supersense Sequence Tagger, Proc. EMNLP, pp (2006). 5) Cucerzan, S.: Large-Scale Named Entity Disambiguation Based on Wikipedia Data, Proc. EMNLP, pp (2007). 6) Freund, Y. and Schapire, R.E.: Large Margin Classification Using the Perceptron Algorithm, Machine Learning, Vol.37, No.3, pp (1999). 7) Hearst, M.: Automatic Acquisition of Hyponyms from Large Text Corpora, Proc. COLING, pp (1992). 8) Inui, K., Abe, S., Hara, K., Morita, H., Sato, C., Eguchi, M., Sumida, A. and Murakami, K.: Experience Mining: Building a large-scale database of personal experiences and opinions from Web documents, Proc. WI-IAT, pp (2008). 9) Izquierdo, R., Suárez, A. and Rigau, G.: An Empirical Study on Class-based Word Sense Disambiguation, Proc. EACL, pp (2009). 10) Kitsuregawa, M., Tamura, T., Toyoda, M. and Kaji, N.: Socio-Sence: A system for analysing the societal behavior from long term Web archive, Proc. APWeb, pp.1 8 (2008). 11) McCarthy, D.: Word Sense Disambiguation: An Overview, Language nad Linguistics Compass, Vol.3, pp (2009). 12) Mihalcea, R.: Using Wikipedia for Automatic Word Sense Disambiguation, Proc. HLT-NAACL, pp (2007). 13) Miller, G.A.: WordNet: A Lexical Database for English, Comm. ACM, Vol.38, pp (1995). 14) Nadeau, D. and Sekine, S.: A Survey of Named Entity Recognition and Classification, Lingvisticae Investigationes, Vol.30, No.1, pp.3 26 (2007). 15) Navigli, R., Litkowski, K.C. and Hargraves, O.: SemEval-2007 Task 07: Coarse- Grained English All-Words Task, Proc. SemEval-2007, pp (2007). 16) Navigli, R.: Word Sense Disambiguation: A Survey, ACM Computing Surveys, Vol.41, pp.1 69 (2009). 17) Sekine, S., available from extendednamedentityhierarchy. 18) Sekine, S., Sudo, K. and Nobata, C.: Extended Named Entity Hierarchy, Proc. LREC, pp (2002). 19) Sekine, S. and Isahara, H.: IREX: IR and IE Evaluation Project in Japanese, Proc. LREC (2000). 20) Shinzato, K., Shibata, T., Kawahara, D., Hashimoto, C. and Kurohashi, S.: TSUB- AKI: An open search engine infrastructure for developing new information access methodology, Proc. IJCNLP, pp (2008). 21) Torisawa, K., De Saeger, S., Kakizawa, Y., Kazama, J., Muratam, M., Noguchi, D. and Sumida, A.: TORISHIKI-KAI: An Autogenerated Web search directory, Proc. ISUC, pp (2008). 22) Whitelaw, C., Kehlenbeck, A., Petrovic, N. and Ungar, L.: Web-scale named entity recognition, Proc. CIKM, pp (2008).
11 3348 Web 23) pp (2003). 24) NL pp (2008). 25) Wikipedia Vol.1, No.3, pp.3 24 (2009). 26) 12 pp (2006). 27) Wikipedia 16 pp (2010). ( ) ( ) DC1 PD
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