Abstract
One approach to QA answering is to match a question to candidate answers in a background corpus based on semantic overlap, possibly in combination with other levels of matching, such as lexical vector space similarity and syntactic similarity. While the computation of deep semantic similarity is as yet generally infeasible, semantic analysis in a specific domain is feasible, if the analysis is constrained to finding domain-specific entities and basic relations. Finding domainspecific entities, the focus of this chapter, is still not a trivial task due to ambiguities of terms. This problem, like many others in Natural Language Processing, is a sequence labelling task. We describe the development of a new approach to sequence labelling in general, based on the constraint satisfaction inference. The output of the machine-learning-based classifiers that solve aspects of the task (such as subsequently predicting the output of the label sequence) are considered as constraints on the global structured output analysis. The constraint-satisfaction in- ference method is compared to other state-of-the-art sequence labelling approaches, showing competitive performance.
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References
Altun Y, Tsochantaridis I, Hofmann T (2003) Hidden markov support vector machines. In: Fawcett T, Mishra N (eds) Proceedings of the Twentieth International Conference on Machine Learning (ICML 2003), pp 3–10
Ando R, Zhang T (2005) A high-performance semi-supervised learning method for text chunking. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics, pp 1–9
Brill E (1994) Some advances in transformation-based part-of-speech tagging. In: Proceedings AAAI ’94
Canisius S, Tjong Kim Sang E (2007) A constraint satisfaction approach to dependency parsing. In: Proc. of the CoNLL Shared Task Session of EMNLPCoNLL 2007, Prague, Czech Republic, pp 1124–1128
Canisius S, Van den Bosch A (2009) A constraint satisfaction approach to machine translation. In: Proceedings of the 13th Annual Conference of the European Association for Machine Translation (EAMT-2009), pp 182–189
Carreras X (2005) Learning and inference in phrase recognition: A filtering-ranking architecture using perceptron. PhD thesis, Universitat Polit`ecnica de Catalunya
Carreras X, M`arquez L, Padr´o L (2003) A simple named entity extractor using adaboost. In: Daelemans W, Osborne M (eds) Proceedings of the seventh Conference on Natural Language Learning at HLT-NAACL 2003, pp 152–155
Chieu H, Ng H (2003) Named entity recognition with a maximum entropy approach. In: Daelemans W, Osborne M (eds) Proceedings of the seventh Conference on Natural Language Learning at HLT-NAACL 2003, pp 160–163
Chinchor N (1995) Named entity task definition. In: Proceedings of the Sixth Message Understanding Conference (MUC-6), pp 317–332
Collins M (2002) Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In: Hajic J, Matsumoto Y (eds) Proceedings of the ACL-02 conference on Empirical Methods in Natural Language Processing, pp 1–8
Cortes C, Mohri M, Weston J (2005) A general regression technique for learning transductions. In: Raedt LD, Wrobel S (eds) Proceedings of the Twenty-Second International Conference on Machine Learning (ICML 2005), pp 153–160
Daelemans W, Zavrel J, Van der Sloot K, Van den Bosch A (2009) TiMBL: Tilburg memory based learner, version 6.2, reference guide. Tech. Rep. ILK 09-01, ILK Research Group, Tilburg University
Daum´e III H (2006) Practical structured learning techniques for natural language processing. PhD thesis, University of Southern California
Doddington G, Mitchell A, Przybocki M, Ramshaw L, Strassel S, Weischedel R (2004) The Automatic Content Extraction (ACE) Program–Tasks, Data, and Evaluation. In: Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04), pp 837–840
Finkel J, Grenager T, Manning C (2005) Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL’05), pp 363–370
Florian R, Ittycheriah A, Jing H, Zhang T (2003) Named entity recognition through classifier combination. In: Daelemans W, Osborne M (eds) Proceedings of the seventh Conference on Natural Language Learning at HLT-NAACL 2003, pp 168–171
Hirschman L, Yeh A, Blaschke C, Valencia A (2005) Overview of BioCreAtIvE: critical assessment of information extraction for biology. BMC Bioinformatics 6(S1)
Klein D, Smarr J, Nguyen H, Manning C (2003) Named entity recognition with character-level models. In: Daelemans W, Osborne M (eds) Proceedings of the seventh Conference on Natural Language Learning at HLT-NAACL 2003, pp 180–183
Kudo T, Matsumoto Y (2000) Use of support vector learning for chunk identification. In: Cardie C, Daelemans W, Nedellec C, Tjong Kim Sang E (eds) Proceedings of the Fourth Conference on Computational Natural Language Learning and of the Second Learning Language in Logic Workshop, pp 142–144
Kudo T, Matsumoto Y (2001) Chunking with support vector machines. In: Second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies, pp 1–8
Lafferty J, McCallum A, Pereira F (2001) Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA
Lewis D, Yang Y, Rose T, Li F (2004) RCV1: A New Benchmark Collection for Text Categorization Research. Journal of Machine Learning Research 5:361–397
Marcus M, Santorini S, Marcinkiewicz M (1993) Building a Large Annotated Corpus of English: the Penn Treebank. Computational Linguistics 19(2):313–330
McCallum A, Freitag D, Pereira F (2000) Maximum entropy Markov models for information extraction and segmentation. In: Langley P (ed) Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), pp 591–598
Punyakanok V, Roth D (2001) The use of classifiers in sequential inference. In: NIPS-13; The 2000 Conference on Advances in Neural Information Processing Systems, The MIT Press, pp 995–1001
Ramshaw L, Marcus M (1995) Text chunking using transformation-based learning. In: Proceedings of the 3rd ACL/SIGDAT Workshop on Very Large Corpora, Cambridge, Massachusetts, USA, pp 82–94
Ratnaparkhi A (1996) A maximum entropy part-of-speech tagger. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, May 17-18, 1996, University of Pennsylvania
Roth D, Yih W (2005) Integer linear programming inference for conditional random fields. In: Proceedings of the 22nd International Conference on Machine Learning, ACM, p 743
Sarawagi S, Cohen W (2005) Semi-markov conditional random fields for information extraction. In: Advances in Neural Information Processing Systems, vol 17, pp 1185–1192
Sha F, Pereira F (2003) Shallow parsing with conditional random fields. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology, pp 134–141
Tjong Kim Sang E (2002) Introduction to the conll-2002 shared task: Languageindependent named entity recognition. In: Proceedings of CoNLL-2002, Taipei, Taiwan, pp 155–158
Tjong Kim Sang E, Buchholz S (2000) Introduction to the CoNLL-2000 shared task: Chunking. In: Proceedings of CoNLL-2000 and LLL-2000, pp 127–132
Tjong Kim Sang E, De Meulder F (2003) Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In: Daelemans W, Osborne M (eds) Proceedings of CoNLL-2003, Edmonton, Canada, pp 142–147
Van den Bosch A (2004) Wrapped progressive sampling search for optimizing learning algorithm parameters. In: Verbrugge R, Taatgen N, Schomaker L (eds) Proceedings of the Sixteenth Belgian-Dutch Conference on Artificial Intelligence, Groningen, The Netherlands, pp 219–226
Van den Bosch A, DaelemansW(2006) Improving sequence segmentation learning by predicting trigrams. In: Proceedings of the Ninth Conference on Natural Language Learning, CoNLL-2005, Ann Arbor, MI, pp 80–87
Van Rijsbergen C (1979) Information Retrieval. Buttersworth, London Zhang T, Johnson D (2003) A robust risk minimization based named entity recognition system. In: Daelemans W, Osborne M (eds) Proceedings of the seventh Conference on Natural Language Learning at HLT-NAACL 2003, pp 204–207
Zhang T, Damerau F, Johnson D (2002) Text chunking based on a generalization of winnow. Journal of Machine Learning Research 2:615–637
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Canisius, S., van den Bosch, A., Daelemans, W. (2011). Constraint-Satisfaction Inference for Entity Recognition. In: van den Bosch, A., Bouma, G. (eds) Interactive Multi-modal Question-Answering. Theory and Applications of Natural Language Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17525-1_9
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