Danushka Bollegala Keigo WATANABE Danushka BOLLEGALA Yutaka MATSUO and Mitsuru ISHIZUKA Graduate School of Information Science and Technology, T

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1 Automatic Extraction of Related Terms using Web Search Engines 1

2 Danushka Bollegala Keigo WATANABE Danushka BOLLEGALA Yutaka MATSUO and Mitsuru ISHIZUKA Graduate School of Information Science and Technology, The University of Tokyo School of Engineering, The University of Tokyo 7-3-1, Hongo, Bunkyo-ku, Tokyo, Japan 2

3 . WordNet..., 3

4 Abstract Semantic lexicons, such as Roget s Thesaurus or WordNet, act as useful knowledge resources in natural language processing applications. However, such manually created lexical resources do not always reflect the new terms and named entities frequently found in the Web. Moreover, manually maintaining lexical resources are costly and time consuming. Motivated by this challenge, we propose a method to automatically extract related terms using the web as a corpus. The proposed method exploits snippets retrieved from a web search engine and efficiently finds related terms. Key words Web mining, information extraction, related terms, search engines 4

5 1 Miller 1 3) X Y X is a Y X Y is-a car automobile drive rental car automobile 6, 10) 100 Google URL URL URL WordNet 3) 12) R W car W R : automobile

6 1 Synonymy Synonym car - automobile Antonymy Antonym rise - fall Hyponymy Hypernym dog - animal Hyponym dog - poodle Meronymy Holonym dog - canis Meronym dog - flag Miller 1 3) Hearst 6) Pantel 10) Snow 13) Lin 2) 14) Girju 5) Hearst Hearst 1 2 6) NP 0 such as NP 1, NP 2..., (and or)np n (1) NP 1,..., NP n (and or)other NP 0 (2) 1 Pantel 10) Part-of-Speech Tagging semantic drift ) Bollegala 1) Cimiano 9) 3 6

7 2 dog animal...no dog or other animal shall be left... no X or other Y no X or other Y shall no X or other Y shall be X or other Y X or other Y shall X or other Y shall be 1 1. : 2. : 3. : : 1 Google R R R R 4 k k = 1, 2, 3 X Y X Y X Y X Y Y X Y X Y X M 1. (X,Y) X Y X Y 2. X prefix X Y midfix Y suffix prefix midfix suffix n pre, n in, n post X Y (context window) 2 3. X Y (2) dog animal...no dog or other animal shall be left... 2 χ 2 v 3 3 χ n pre = 2, n in = 3, n post = 2 Google

8 3 χ 2 v v p v P p v P n v N n v N χ 2 χ 2 = (P + N)(p v(n n v ) n v (P p v )) 2 P N(p v + n v )(P + N p v n v ) (3) χ 2 3 p v (N n v ) n v (P p v ) 2 χ 2 N : X Y X and Y are dog dog and are and dog are M n 3 : the, or, of... 4 PorterStemmer 8) : dog dogs 3.3 3: PF(c)( 4) PF(c) = f(c v ) (4) v f(c v ) = {v c v > 0} c 2 v c v c TF(c)( 5) TF(c) = c v (5) v 1 χ 2 X synonyms Y χ 2 X and Y χ 2 P F (c) T F (c) WeightPF(c)( 6) WeightTF(c)( 7) 3 n = blog.al/2009/04/14/list-of-english-stop-words/ 8

9 WeightPF(c) = v WeightTF(c) = v weight v f(c v ) (6) weight v c v (7) weight v v F. F ( 10) ( 8) ( 9) = = F = 2 + (8) (9) (10) (weight v ) 4 F 4 PF( 4),TF( 5),WeightPF( 6) WeightTF( 7) PF TF WeightPF WeightTF PF WeightTF TF PF WeightTF WeightPF weight v = 1 WeightTF WeightPF 4 WordNet WordNet synset normalized F measure pattern ID 2 WordNet synset 1000 WordNet WordNet synset synset hypernym set M = 100 ( N = 100 F χ F weight v 3.3 weight v 10 F 2 F 2 9

10 4 F 10 X,Y: X: X: F χ 2 F χ 2 F χ 2 synset X Y X is a who or other X X synonyms Y X by of a is a X syn X Y X a who X such as a an X or Y X or other X or as X or Y a a or X n the X of X or Y was and X for X or in X or Y is or X that X called a X or Y in the or X X or is a X or Y or X of the X or of the as a X or Y X a that X by of PF WeightTF Lin 98 5 Lin Lin PF WeightTF Lin Lin 2) Lin Lin {cord, forest, fruit, glass, slave} 50 Lin Lin Roget s Thesaurus WordNet 4-6 / F F

11 WeightTF PF WeightTF 0 F /= 1 F F / Bollegala Jaccard Overlap Dice PMI W ebjaccard W eboverlap W ebdice W ebp MI ) H(P ) P H(P Q) P AND Q P Q AND W ebp MI N Google WebJaccard(P, Q) = WebOverlap(P, Q) = WebDice(P, Q) = WebPMI(P, Q) = log 2 ( H(P Q) H(P ) + H(Q) H(P Q) (11) H(P Q) min(h(p ), H(Q)) 2H(P Q) H(P ) + H(Q) H(P Q) N H(P ) H(Q) N N ) (12) (13) (14) magician PF WeightTF WebJaccard WebOverlap WebDice WebPMI F 7 F Web- Dice WebJaccard 134 F WebPMI WebOverlap 4, 15) WebPMI WebOverlap PF WeightTF F WebDice WebJaccard PF WeightTF F 120 WeightTF Recall Precision F F magician 11 5

12 WeightTF PF WeightTF PF WeightTF PF magician PF WeightTF *wizard 49 *sorcerer 9.78 *illusionist 47 clown 7.37 *sorcerer 43 *wizard 5.93 magic 40 *illusionist 5.62 clown 37 *shaman 5.55 *juggler 36 *witch 5.09 priest 36 *enchanter 4.30 *artist 35 priest 4.07 *witch 35 *juggler 4.01 warrior 34 *artist Roget s Thesaurus 5 Lin 3 6 Roget s Thesaurus clown 1) D. Bollegala, Y. Matsuo, and M. Ishizuka. Measuring semantic similarity between words using web search engines. In Proceedings of the 16th International World Wide Web Conference (WWW-07), pages , ) D. Lin. Automatic retrieval and clustering of similar words. In Proceedings of the 19th International Conference on Computational Linguistics and the 36th Annual Meeting of the Association for Computational Linguistics (COLING- ACL-98), pages , ) G. Miller, R. Beckwith, C. Fellbaum, D. Gross, and K. Miller. Introduction to WordNet: An On-line Lexical Database. International Journal of Lexicography, 3(4): pages , ) P. Turney. Mining the Web for Synonyms: PMI- IR versus LSA on TOEFL. In Proceedings of the 12th European Conference on Machine Learning (ECML-01), pages , ) R. Girju, A. Badulescu, and D. Moldovan. Automatic Discovery of Part-Whole Relations. In 12

13 Computational Linguistics, 32(1), pages , ) M. Hearst. Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th International Conference on Computational Linguistics (COLING-92), pages , ) M. Pasca. Organizing and searching the World Wide Web of facts - step two: harnessing the wisdom of the crowds. In Proceedings of the 16th International Conference on World Wide Web (WWW-07), pages , ) M. Porter. An Algorithm for Suffix Stripping. Program, 14, pages , Accesible at martin/porterstemmer/. 9) P. Cimiano and J. Wenderoth. Automatic acquisition of ranked qualia structures from the web. In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL-07), pages , ) P. Pantel, D. Ravichandran, and E. Hovy. Towards Terascale Knowledge Acquisition. In Proceedings of the 20th International Conference on Computational Linguistics (COLING-04), pages , ) P. Pantel and M. Pennacchiotti. Espresso: Leveraging generic patterns for automatically harvesting semantic relations. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics (COLING-ACL-06), pages , ) P. Roget. Thesaurus of English words and phrases. Longmans, Green and Co., ) R. Snow, D. Jurafsky, and A. Ng. Learning syntactic patterns for automatic hypernym discovery. Advances in Neural Information Processing Systems 17, ),,,. Web., Vol. 14, Number 2, pages 3-31, ) Y. Matsuo, J. Mori, M. Hamasaki, K. Ishida, T. Nishimura, H. Takeda, K. Hasida and M. Ishizuka. POLYPHONET: an advanced social network extraction system from the web. In Proceedings of the 15th International Conference on World Wide Web, (WWW-06), Tel: Fax: danushka@iba.t.u-tokyo.ac.jp ( DeNA. Bollegala Danushka ( ) ,, WWW,ACL ( ) AAAI 13

14 ( ) NTT Purdue 1992,Web,,.IEEE,AAAI,,,. 14

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