參考文獻 |
[1] S. Abney. Parsing by chunks. In Principle-based parsing: Computation and Psycholinguistics, pages 257-278, 1991.
[2] R. K. Ando and T. Zhang. A high-performance semi-supervised learning method for text chunking. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), pages 1-9. 2005.
[3] G. Attardi. Experiments with a multilanguage non-projective dependency parser. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), pages 166-170, 2006.
[4] G. Attardi, F. Dell'Orletta, M. Simi, A. Chanev and M. Ciaramita. 2007. Multilingual Dependency Parsing and Domain Adaptation using DeSR. In Joint Conference on Empirical Methods in Natural Language Processing and Conference on Computational Natural Language Learning (EMNLP-CoNLL), 2007.
[5] S. J. Benson and J. J. Moré. A limited memory variable metric method for bound constraint minimization. Technical Report ANL/MCS-P909-0901, Argonne National Laboratory, 2001.
[6] A. Berger, A. Della Pietra, and J. Della Pietra. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1):39-71, 1996.
[7] D. M. Bikel and D. Chiang. Two statistical parsing models applied to the Chinese Treebank. In Proceedings of the Chinese language processing workshop, pages 1-6, 2000.
[8] J. Blitzer, R. McDonald, and F. Pereira. Multilingual dependency parsing with a two-stage discriminative parser. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), pages 216-220, 2006.
[9] T. Brants. Cascaded markov models. In Proceedings of the Conference on European Chapter of the Association for Computational Linguistics (EACL) pages 118-125, 1999.
[10] T. Brants. TNT - A Statistical part-of-speech tagger. In Proceedings of the Conference on Applied Natural Language Processing (ANLP), 2000.
[11] E. Brill. Transformation-based error-driven learning and natural language processing: a case study in part of speech tagging. Computational Linguistics, 21(4), 543-565, 1995.
[12] E. Briscoe and J. Carroll. Generalised probabilistic lr parsing of natural language (corpora) with unification-based grammars. Computational Linguistics, 19(1): 25-29, 1993.
[13] S. Buchholz, E. Marsi, A. Dubey, and Y. Krymolowski. 2006. CoNLL-X Shared Task on Multilingual Dependency Parsing, In Proceedings of Conference on Computational Natural Language Learning (CoNLL), pages 149-164, 2006.
[14] C. J. C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, pages 121-167, 1998.
[15] X. Carreras, L. Marquez, and J. Castro. Filtering-ranking perceptron learning for partial parsing. Machine Learning, 60: 41-71, 2005.
[16] M. W. Chang, Q. Do, and D. Roth. Multilingual dependency parsing: a pipeline approach. In Recent Advances in Natural Language Processing (RANLP), pages 195-204, 2006.
[17] E. Charniak. A maximum-entropy-inspired parser. In Proceedings of the Conference on North American Chapter of the Association for Computational Linguistics (ANLP-NAACL), pages 132-139, 2000.
[18] E. Charniak and M. Johnson. Coarse-to-fine n-best parsing and MaxEnt discriminative reranking. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), pages 173-180, 2005.
[19] C. Chelba and A. Acero. Adaptation of maximum entropy capitalizer: Little data can help a lot. In Proceedings of the Empirical. Methods in Natural Language Processing (EMNLP), 2004.
[20] K. J. Chen, C. R. Huang, F. Y Chen, C. C. Luo, M. C. Chang, and C. J. Chen. Sinica Treebank: design criteria, representational issues and implementation. Building and Using Parsed Corpora, Kluewer, 2004.
[21] S. Chen and R. Rosenfeld. A gaussian prior for smoothing maximum entropy models. Technical Report CMU-CS-99-108, Carnegie Mellon University, 1999.
[22] Y. Cheng, M. Asahara, and Y. Matsumoto, Deterministic dependency analyzer for Chinese. In Proceedings of the First International Joint Conference on Natural Language Processing (IJCNLP), pages 135-140, 2004.
[23] Y. Cheng, M. Asahara, and Y. Matsumoto. Machine learning-based Dependency Analyzer for Chinese. In Proceedings of the International Conference on Chinese Computing (ICCC), pages 66-73, 2005a.
[24] Y. Cheng, M. Asahara, and Y. Matsumoto, Chinese Deterministic Dependency Analyzer: Examining Effects of Global Features and Root Node Finder. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, pages 17-24, 2005b.
[25] Y. Cheng, M. Asahara and Y. Matsumoto. Multi-lingual Dependency Parsing at NAIST. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), pages 191-195, 2006.
[26] S. Clark, J. R. Curran, and M. Osborne. Bootstrapping POS taggers using unlabelled data. In Proceedings of the Meeting of North American Chapter of Association for Computational Linguistics (NAACL), pages 49-55, 2003.
[27] M. Collins, Head-driven statistical models for natural language processing. PhD thesis, University of Pennsylvania, 1999.
[28] T. H. Cormen, C. E. Leiserson, R. L. Rivest, and C. Stein. Introduction to algorithms: second edition. McGraw-Hill Higher Education, 2002.
[29] S. Corston-Oliver, A. Aue, K. Duh, and E. Ringger, Multilingual dependency parsing using Bayes point machines. In Proceedings of the Meeting of North American Chapter of Association for Computational Linguistics (NAACL), pp. 160-167, 2006.
[30] K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-basd vector machines. Journal of Machine Learning Research, 2: 265-292, 2001.
[31] K. Crammer and Y. Singer. Ultraconservative online algorithms for multiclass problems, Journal of Machine Learning Research, 3: 951-991, 2003.
[32] H. Cui, R. Sun, K. Li, M. Kan, and T. Chua. Question answering passage retrieval using dependency relations. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, pages 400-407, 2005.
[33] W. Daelemans, J. Zavrel, P. Berck, and S. Gillis. TiMBLL MBT: a memory-mased part of speech tagger-generator. In Proceedings of Workshop on Very Large Corpora (WVLC), pages 14-27, 1996.
[34] H. Daume and D. Marcu. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 26: 101-126, 2006.
[35] T. G. Dietterich. Approximate statistical tests for comparing supervised classification learning algorithms. Neural computation, 10: 1895-1923, 1998.
[36] D. Duchier and R. Debusmann. Topological dependency parsing: a constraint-based account of linear precedence. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), pages 180-187, 2001.
[37] J. Eisner. Bilexical grammars and their cubic-time parsing algorithms. In Bunt and Nijholt (eds.) Advances in Probabilistic and Other Parsing Technologies, Kluwer, 2000.
[38] J. Eisner. Three New Probabilistic Models for Dependency Parsing: An Exploration. In Proceedings of International Conference on Computational Linguistics (COLING), 1996.
[39] D. Elworthy. Does Baum-Welch re-estimation help taggers? In Proceedings of the Conference on Applied Natural Language Processing (ANLP), pages 53-58, 1994.
[40] T. Emerson. The second international Chinese word segmentation bakeoff. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, 2005.
[41] R. E. Fan, P. H. Chen, and C. J. Lin. Working set selection using the second order information for training SVM, Journal of Machine Learning Research 6: 1889-1918, 2005.
[42] R. Florian, H. Hassan, A. Ittycheriah, H. Jing, N. Kambhatla, X. Luo, N. Nicolov, and S. Roukos. A statistical model for multilingual entity detection and tracking. In Proceedings of the Meeting of North American Chapter of Association for Computational Linguistics (NAACL), 2004.
[43] R. Florian, J.C. Henderson, and G. Ngai. Coaxing confidence from an old friend: Probabilistic classifications from transformation rule lists. In Proceedings of the Empirical. Methods in Natural Language Processing (EMNLP), 2000.
[44] R. Florian, A. Ittycheriah, H. Jing, and T. Zhang. Named entity recognition through classifier combination. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), pages 168-171, 2003.
[45] P. Fung, G. Ngai, Y. Yang, and B. Chen. A Maximum-entropy Chinese parser augmented by transformation-based learning. ACM Transactions on Asian Language Information Processing, 3(2): 159-168, 2004.
[46] H. Gaifman. Dependency systems and phrase-structure systems. Information and Control, 8: 304-337.
[47] J. Gao, J. Y. Nie, G. Wu, and G. Cao. Dependence language model for information retrieval, In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, pages 172-179, 2004.
[48] X. Ge, W. Pratt, and P. Smyth. Discovering Chinese words from unsegmented text. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, 1999.
[49] J. Giménez, and L. Márquez. Fast and accurate Part-of-Speech tagging: the SVM approach revisited. In Recent Advances in Natural Language Processing (RANLP), pages 158-165, 2003.
[50] C. L. Goh, M. Asahara, and Y. Matsumoto. Chinese word segmentation by classification of characters. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, pages 57-64, 2004.
[51] K. Hacioglu. Semantic role labeling using dependency trees. In Proceedings of International Conference on Computational Linguistics (COLING), pages 1273-1276, 2004.
[52] J. Hall, J. Nivre, and J. Nilsson, Discriminative classifiers for deterministic dependency parsing. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), pp. 316-323, 2006.
[53] D. Hays. Dependency theory: a formalism and some observations. Language, 40: 511-525, 1964.
[54] H. Isozaki, H. Kazawa, and T. Hirao. A deterministic word dependency analyzer enhanced with preference learning. In Proceedings of International Conference on Computational Linguistics (COLING), pages 275-281, 2004.
[55] T. K. Huang, R. C. Weng, and C. J. Lin. Generalized bradley-terry models and multi-class probability estimates, Journal of Machine Learning Research, 7: 85-115, 2006.
[56] R. Hudson. Word grammar. Basil Blackwell, 1984.
[57] M. Jin, M. Y. Kim, and J. H. Lee. Two-phase shift-reduce deterministic dependency parser of Chinese. In Proceedings of the Second International Joint Conference on Natural Language Processing (IJCNLP), 2005.
[58] T. Joachims. Text categorization with support vector machines: learning with many relevant features. In Proceedings of the European Conference on Machine Learning (ECML), pages 137-142, 1998.
[59] J. Kazama and J. Tsujii. Evaluation and extension of maximum entropy models with inequality constraints. In Proceedings of the Empirical. Methods in Natural Language Processing (EMNLP), pages 137-144, 2003.
[60] S. Keerthi and D. DeCoste. A modified finite Newton method for fast solution of large scale linear SVMs. Journal of Machine Learning Research. 6: 341-361, 2005.
[61] C. Kit and Y. Wilks. Unsupervised learning of word boundary with description length gain. In Proceedings CoNLL99 ACL Workshop, 1999.
[62] A. Klautau, N. Jevtic, and A. Orlitsky. On nearest-neighbor error-correcting output codes with application to all-pairs multiclass support vector machines. Journal of Machine Learning Research, 4: 1-15, 2003.
[63] D. Klein and C. D. Manning. A generative constituent-context model for improved grammar inductrion. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), pages 128-135, 2002.
[64] U. Kreßel. Pairwise classification and support vector machines. Advances in Kernel Methods: Support Vector Learning, pages 255-268, 1999.
[65] T. Kudo and Y. Matsumoto. Use of support vector learning for chunk identification. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), pages 142-144, 2000.
[66] T. Kudo and Y. Matsumoto. Chunking with support vector machines. In Proceedings of the Meeting of North American Chapter of Association for Computational Linguistics (NAACL) In Proceedings of the Meeting of North American Chapter of Association for Computational Linguistics (NAACL), pages 192-199, 2001.
[67] T. Kudo and Y. Matsumoto. Fast methods for kernel-based text analysis. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), pages 24-31, 2003.
[68] T. Kudo and Y. Matsumoto. Appliying conditional random fields to Japanese morphological analysis. In Proceedings of the Empirical. Methods in Natural Language Processing (EMNLP), pages 230-237, 2004.
[69] O. Y. Kwong. Categorical fluidity in Chinese and its Implications for part-of-speech tagging. In Proceedings of the Conference on European Chapter of the Association for Computational Linguistics (EACL), pages 115-118. 2003.
[70] J. Lafferty, A. McCallum, and F. Pereira. Conditional random fields: probabilistic models for segmenting and labeling sequence data. In Proceedings of the International Conference on Machine Learning (ICML), pages 282-289, 2001.
[71] Y. S. Lee and Y. C. Wu. A robust multilingual portable phrase chunking system. Expert Systems with Applications, 33(3): 1-26, 2007.
[72] G. A. Levow. The third international Chinese word segmentation bakeoff: word segmentation and named entity recognition. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, pages 108-117, 2006.
[73] H. Lian. Chinese language parsing with maximum-entropy-inspired parser. Master's thesis, Brown University, 2005.
[74] D. Liu and J. Nocedal. On the limited memory BFGS method for large-scale optimization. Mathematical Programming, 45:503-528, 1989.
[75] X. Luo. A maximum entropy Chinese character-based parser. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2003.
[76] J. Ma, Y. Zhang, and S. Li, A statistical dependency parser of Chinese under small training data. In Proceedings of the 1st International Joint Conference of Natural Language Processing, pp. 1-5, 2004.
[77] R. Malouf. A comparison of algorithms for maximum entropy parameter estimation. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), pages 49-55, 2002.
[78] H. Maruyama. Structural disambiguation with constraint propagation. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), 1990.
[79] D. McClosky, E. Charniak, and M. Johnson. Reranking and self-training for parser adaptation. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), 2006.
[80] R. McDonald, C. Krammer, and F. Pereira. Online large-margin training of dependency parsers. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), 2005.
[81] R. McDonald, K. Lerman, and F. Pereira. Multilingual dependency analysis with a two-stage discriminative. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), pages 216-220, 2006
[82] I. Mel’cuk. Dependency syntax: theory and practice. The SUNY Press, Albany, 1988.
[83] W. Menzel, and I. Schröder. Decision Procedures for Dependency Parsing Using Graded Constraints. In Workshops of the Processing of Dependency-based Grammars, 1998.
[84] A. Molina and F. Pla. Shallow parsing using specialized HMMs. Journal of Machine Learning Research, 2: 595-613, 2002.
[85] T. Nakagawa, T. Kudo, and Y. Matsumoto. Unknown word guessing and Part-of-Speech tagging using support vector machines. In Proceedings of the 6th Natural Language Processing Pacific Rim Symposium, pages 325-331, 2001.
[86] H. T. Ng and J. K. Low. Chinese part-of-speech tagging. one-at-a-time or all-at-once? word-based or character-based? In Proceedings of the Empirical. Methods in Natural Language Processing (EMNLP), 2004.
[87] J. Nivre. An efficient algorithm for projective dependency parsing. In Proceedings of the International Workshop on Parsing Technology (IWPT), pages 149-160, 2003.
[88] J. Nivre and J. Nilsson. Pseudo-projective dependency parsing. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), pages 99-106, 2005.
[89] J. Nivre, J. Hall, J. Nilsson, G. Eryigit, and S. Marinov. Labeled pseudo-projective dependency parsing with support vector machines. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), pages 221-225, 2006.
[90] J. Nivre, J. Hall, S. Kübler, R. McDonald, J. Nilsson, S. Riedel, and D. Yuret. The CoNLL 2007 shared task on dependency parsing. In Joint Conference on Empirical Methods in Natural Language Processing and Conference on Computational Natural Language Learning (EMNLP-CoNLL), 2007.
[91] J. Nivre, J. Hall, and J. Nilsson. Memory-based dependency parsing. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), 2004.
[92] P. Pantel and D. Lin. Discovering word senses from text. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. Pages 613-619, 2002.
[93] J.C. Platt, N. Cristianini, and J. Shawe-Taylor, Large margin dags for multiclass classification. Advanced in Neural Information Processing Systems, 12: 547-553, 2000.
[94] J. C. Platt. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Large Margin Classifiers, MIT Press, 1999.
[95] L. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. In Proceedings of IEEE, 77(2):257-286, 1989.
[96] L. A. Ramshaw and M. P. Marcus. Text chunking using transformation-based learning. In Proceedings of Workshop on Very Large Corpora (WVLC), pages 82-94, 1995.
[97] R. Rifkin and A. Klautau. In defense of one-vs-all classification. Journal of Machine Learning Research, 5: 101-141, 2004.
[98] A. Ratnaparkhi. A linear observed time statistical parser based on maximum entropy models. In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1-10, 1997.
[99] B. Roark and M. Bacchiani. Supervised and unsupervised PCFG adaptation to novel domains. In Proceedings of the Meeting of North American Chapter of Association for Computational Linguistics (NAACL), 2003.
[100] K. Sagae, A. Lavie, and B. MacWhinney. Automatic measurement of syntactic development in child language. In Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL), 2005.
[101] Y. Seginer. Fast unsupervised incremental parsing. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), pages 384-391, 2007.
[102] F. Sha and F. Pereira. Shallow parsing with conditional random fields. In Proceedings of the Meeting of North American Chapter of Association for Computational Linguistics (NAACL), 2003.
[103] H. Shen and A. Sarkar. Voting between multiple data representations for text chunking. In Proceedings of the Meeting of the Canadian Society for Computational Intelligence, pages 389-400, 2005.
[104] D. D. K. Sleator and D. Temperley. Parsing English with a link grammar. In Proceedings of the International Workshop on Parsing Technology (IWPT), 1993.
[105] R. Sproat and T. Emerson. The first International Chinese word segmentation Bakeoff. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, 2003.
[106] M. Steedman, R. Hwa, S. Clark, M. Osborne, A. Sarkar, J. Hockenmaier, P. Ruhlen, S. Baker, and J. Crim, Example selection for bootsrapping statistical parsers. In Proceedings of the Meeting of North American Chapter of Association for Computational Linguistics (NAACL), pages 157-164, 2003.
[107] W. Sun, and J. Chen, A multi-class classifier based on SVM decision tree. IEEE Computational Intelligence Society Electronic Letter, in press.
[108] F. Takahashi, and S. Abe, Decision-tree-based multicalss support vector machines. In Proceedings of the International Conference on Neural Information Processing Systems (NIPS), 2: 1418-1422, 2002.
[109] P. Tapanainen and T. Järvinen. A non-projective dependency parser. In Proceedings of the Conference on Applied Natural Language Processing (ANLP), 1997.
[110] L. Tesniere. Element de syntaxe structurale. Paris: Editions Klincksieck, 1959.
[111] I. Titov and J. Henderson. Porting statistical parsers with data-defined kernels. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), 2006.
[112] E. F. Tjong Kim Sang. Transforming a chunker to a parser. Computational Linguistics in the Netherlands, pages 177-188, 2001
[113] E. F. Tjong Kim Sang, and S. Buchholz. Introduction to the CoNLL-2000 shared task: chunking. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), pages 127-132, 2000.
[114] E. F. Tjong Kim Sang and J. Veenstra. Representing text Chunks. In Proceedings of the Conference on European Chapter of the Association for Computational Linguistics (EACL), pages 173-179, 1999.
[115] M. Tomita. The generalized lr parser/compiler – version 8.4. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), pages 59-63, 1990.
[116] H. Tseng, P. Chang, G. Andrew, D. Jurafsky, and C. Manning. A conditional random field word segmenter. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, pages 168-171, 2005.
[117] T. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research, 6: 1453-1484, 2005.
[118] V. N. Vapnik, The nature of statistical learning theory. Springer, 1995.
[119] V. Vural, and J. D. Dy, A hierarchical method for multi-class support vector machines, In Proceedings of the International Conference on Machine Learning (ICML), pages 831-838, 2004.
[120] Q. I. Wang, D. Lin, and D. Schuurmans, Simple training of dependency parsers via structured boosting. In Proceedings of International Joint Conference on Artificial Intelligence, pp. 1756-1762, 2007.
[121] M. Wang and Y. Shi. Using part-of-speech reranking to improve Chiense word segmentation. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, pages 205-208, 2006.
[122] Y. C. Wu, C.H. Chang, and Y. S. Lee, A general and multi-lingual phrase chunking model based on masking method. Lecture Notes in Computer Science (LNCS): Computational Linguistics and Intelligent Text Processing, 3878: 144-155, 2006.
[123] Y. C. Wu and C.H. Chang. Efficient text chunking using linear kernel with mask method. Knowledge Based Systems, 20(3): 209-219, 2007.
[124] Y. C. Wu, Y. S. Lee, and J. C. Yang. The exploration of deterministic and efficient dependency parsing. In Proceedings of Conference on Computational Natural Language Learning (CoNLL), pages 241-245, 2006.
[125] Y. C. Wu, J. C. Yang, and Q. X. Lin. 2006. Description of the NCU Chinese word segmentation and named entity recognition system for SIGHAN bakeoff 2006. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, pages 209-212, 2006.
[126] Y. C. Wu, J. C. Yang, and Y. S. Lee, and S. J. Yen, Efficient and robust phrase chunking using support vector machines. Lecture Notes in Computer Science (LNCS): Asia Information Retrieval Symposium (AIRS), 4182: 350-36, 2006.
[127] Y. C. Wu, J. C. Yang, and Y. S. Lee, Multilingual deterministic dependency parsing framework using modified finite Newton method support vector machines. In Joint Conference on Empirical Methods in Natural Language Processing and Conference on Computational Natural Language Learning (EMNLP-CoNLL), 2007.
[128] Y. C. Wu, J. C. Yang, and Y. S. Lee. An approximate approach for training polynomial kernel SVMs in Linear Time. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), Poster, 2007.
[129] F. Xia, and L. Cheung. Features, bagging, and system combination for the Chinese POS tagging task. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, pages 25-32, 2006.
[130] F. Xia, M. Palmer, N. Xue, M. E. Okurowski, J. Kovarik, F. D. Chiou, S. Huang, T. Kroch, and M. Marcus. Developing guidelines and ensuring consistency for Chinese text annotation. In Proceedings of the second International Conference on Language Resources and Evaluation (LREC), 2000.
[131] J. Xu, S. Miller, and R. Weischedel. A statistical parser for Chinese. In Proceedings Human Language Technology Workshop, 2002.
[132] N. Xue and S. P. Converse. Combining classifiers for Chinese word segmentation. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, pages 63-70. 2002.
[133] N. Xue, F. D. Chiou, and M. Palmer. The Chinese Treebank: phrase structure annotation of a large corpus. Natural Language Engineering, 11(2): 207-238, 2005.
[134] H. Yamada and Y. Matsumoto. Statistical dependency analysis with support vector machines. In Proceedings of the 8th International Workshop on Parsing Technologies (IWPT), pages 195-206, 2003.
[135] Y. Yang and X. Liu. A re-examination of text categorization methods. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval, pages 42-49, 2005.
[136] T. Zhang, F. Damerau, and D. Johnson. Text chunking based on a generalization Winnow. Journal of Machine Learning Research, 2: 615-637, 2002.
[137] T. Zhang, F. Damerau, and D. Johnson. Text chunking using regularized Winnow. In Proceedings of the Annual Meetings of the Association for Computational Linguistics (ACL), pages 539-546, 2001.
[138] G. D. Zhou and J. Su, A Chinese efficient analyzer integrating word segmentation, part-of-speech tagging, partial parsing and full parsing. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, pages 78-83, 2003.
[139] G. D. Zhou. Chunking-based Chinese word tokenization. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, pages 188-191, 2003.
[140] H. Zhou, C. N. Huang, and M. Li. An improved word segmentation system with conditional random fields. In Proceedings of the SIGHAN Workshop on Chinese Language Processing Workshop, pages 162-165, 2006. |