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Joachims, T.: 11 Making Large-Scale Support Vector Machine Learning Practical.

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Vapnik, V.: The Nature of Statistical Learning Theory. In: Proceedings of the 2nd Meeting of the North American Chapter of the Association for Computational Linguistics, pp. Kudo, T., Matsumoto, Y.: Chunking with support vector machines. In: Poster Proceedings of the 11th International World Wide Web Conference (2002)īrill, E.: Transformation-based error-driven learning and natural language processing: A case study in part-of-speech tagging. Takahashi, T., Soonsang, H., Taura, K., Yonezawa, A.: World wide web crawler. In: Proceedings of the 19th International Conference on Computational Linguistics, pp. Volk, M.: Combining unsupervised and supervised methods for pp attachment disambiguation. Hindle, D., Rooth, M.: Structural ambiguity and lexical relations. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, pp. Vanschoenwinkel, B., Manderick, B.: A weighted polynomial information gain kernel for resolving pp attachment ambiguities with support vector machines. In: Proceedings of 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, pp. 136–144 (1997)Ībney, S., Schapire, R., Singer, Y.: Boosting applied to tagging and pp attachment. In: Proceedings of the Workshop on Computational Natural Language Learning, pp. Zavrel, J., Daelemans, W., Veenstra, J.: Resolving pp attachment ambiguities with memory-based learning. In: Proceedings of the 5th Workhop on Very Large Corpora, pp. Stetina, J., Nagao, M.: Corpus based pp attachment ambiguity resolution with a semantic dictionary. In: Proceedings of the 3rd Workhop on Very Large Corpora, pp. 1198–1204 (1994)Ĭollins, M., Brooks, J.: Prepositional phrase attachment through a backed-off model. In: Proceedings of the 15th International Conference on Computational Linguistics, pp. 250–255 (1994)īrill, E., Resnik, P.: A rule-based approach to prepositional phrase attachment disambiguation. In: Proceedings of the ARPA Human Language Technology Workshop, pp. Ratnaparkhi, A., Reynar, J., Roukos, S.: A maximum entropy model for prepositional phrase attachment. Marcus, M., Santorini, B., Marcinkiewicz, M.: Building a large annotated corpus of English: the Penn Treebank. Manning, C., Schütze, H.: Foundations of Statistical Natural Language Processing. In: Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics, pp. Pantel, P., Lin, D.: An unsupervised approach to prepositional phrase attachment using contextually similar words. In: Proceedings of the 17th International Conference on Computational Linguistics, pp. Ratnaparkhi, A.: Statistical models for unsupervised prepositional phrase attachment. In: Proceedings of the 1st Meeting of the North American Chapter of the Association for Computational Linguistics, pp. PhD thesis, University of Pennsylvania (1999)Ĭharniak, E.: A maximum-entropy-inspired parser. 428–434 (2004)Ĭollins, M.: Head-Driven Statistical Models for Natural Language Parsing. In: Proceedings of the 1st International Joint Conference on Natural Language Processing, pp.

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Zhao, S., Lin, D.: A nearest-neighbor method for resolving pp-attachment ambiguity.








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