Abstract
In this paper, we propose a cost-effective search strategy framework to extract keywords in the same semantic class from the Web. Constructing a dictionary based on the bootstrapping technique is one promising approach to harnessing knowledge scattered around the Web. Open web application programming interfaces (APIs) are powerful tools for the knowledge-gathering process. However, we have to consider the cost of API calls because too many queries can overload the search engines, and they also limit the number of API calls. Our goal is to optimize a search strategy that can collect as many new words as possible with the least API calls. Our results show that the optimized search strategy can extract 64,642 words in five different domains with a precision of 0.94 with only 1,000 search API calls.
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References
Bikel, D.M., Milier, S., Schwartz, R., Weischedel, R.: Nymble: A high-performance learning name-filter. In: Proc. the Fifth Conference on Applied Natural Language Processing (1997)
Seymore, K., McCallum, A., Rosenfeld, R.: Learning hidden Markov model structure for information extraction. In: AAAI Workshop on Machine Learning for Information Extraction (1999)
Collins, M., Singer, Y.: Unsupervised models for named entity classification. In: Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (1999)
Riloff, E.: Automatically constructing a dictionary for information extraction tasks. In: Proc. of the Eleventh National Conference on Artificial Intelligence, pp. 811–816. AAAI Press / The MIT Press (1993)
Soderland, S., Fisher, D., Aseltine, J.: W. Lehnert, W.: Crystal: Inducing a conceptual dictionary. In: Proc. the Fourteenth International Joint Conference on Artificial Intelligence, pp. 1314–1319 (1995)
Califf, M.E., Mooney, R.J.: Relational learning of pattern-match rules for information extraction. In: Working Notes of AAAI Spring Symposium on Applying Machine Learning to Discourse Processing (1998)
Riloff, E., Jones, R.: Learning dictionaries for information extraction using multi-level bootstrapping. In: Proc. the Sixteenth National Conference on Artificial Intelligence, pp. 1044–1049. AAAI Press / The MIT Press (1999)
Etzioni, O., Cafarella, M., Downey, D., Kok, S., Popescu, A.-M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Web-scale information extraction in KnowItAll. In: Proc. the 13th International Conference on World Wide Web (2004)
Pasca, M., Lin, D., Bigham, J., Lifchits, A., Jain, A.: Organizing and searching the World Wide Web of facts - step one: the one-million fact extraction challenge. In: Proc. AAAI 2006 (2006)
Thelen, M., Riloff, E.: A bootstrapping method for learning semantic lexicons using extraction pattern contexts. In: Proc. Conference on Empirical Methods in Natural Language Processing, pp. 214–222 (2002)
Hasegawa, T., Sekine, S., Grishman, R.: Discovering relations among named entities from large corpora. In: Proc. ACL, pp. 415–422 (2004)
O’Reilly, T.: What is Web 2.0: Design patterns and business models for the next generation of software, http://www.oreillynet.com/lpt/a/6228
Yahoo! Developer Network, http://developer.yahoo.com/
Google Code, http://code.google.com/
Windows Live Developer Center, http://msdn.microsoft.com/msn/default.aspx
WordNet, http://wordnet.princeton.edu/
Brin, S.: Extracting patterns and relations from the World Wide Web. In: Proc. the International Workshop on the World Wide Web and Databases, pp. 172–183 (1998)
Soderland, S., Etzioni, O., Shaked, T., Weld, D.: The use of Web-based statistics to validate information extraction. In: AAAI workshop on Adaptive Text Extraction and Mining (2004)
Kushmerick, N.: Wrapper induction: efficiency and expressiveness. In: AAAI Workshop on AI and Information Integration (1998)
Zhai, Y., Liu, B.: Web data extraction based on partial tree alignment. In: Proc. the 14th International Conference on World Wide Web (2005)
Chuang, S.-L., Chang, K.C.-C., Zhai, C.: Context-Aware wrapping: synchronized data extraction. In: Proc. the 33rd Very Large Data Bases Conference (VLDB) (2007)
Wang, R.C., Cohen, W.W.: Language-independent set expansion of named entities using the Web. In: Proc. IEEE International Conference on Data Mining (2007)
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Kawai, H., Mizuguchi, H., Tsuchida, M. (2008). Cost-Effective Web Search in Bootstrapping for Named Entity Recognition. In: Haritsa, J.R., Kotagiri, R., Pudi, V. (eds) Database Systems for Advanced Applications. DASFAA 2008. Lecture Notes in Computer Science, vol 4947. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78568-2_29
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DOI: https://doi.org/10.1007/978-3-540-78568-2_29
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