ABSTRACT
We analyze some of the fundamental design challenges and misconceptions that underlie the development of an efficient and robust NER system. In particular, we address issues such as the representation of text chunks, the inference approach needed to combine local NER decisions, the sources of prior knowledge and how to use them within an NER system. In the process of comparing several solutions to these challenges we reach some surprising conclusions, as well as develop an NER system that achieves 90.8 F1 score on the CoNLL-2003 NER shared task, the best reported result for this dataset.
- R. K. Ando and T. Zhang. 2005. A high-performance semi-supervised learning method for text chunking. In ACL. Google ScholarDigital Library
- P. F. Brown, P. V. deSouza, R. L. Mercer, V. J. D. Pietra, and J. C. Lai. 1992. Class-based n-gram models of natural language. Computational Linguistics, 18(4):467--479. Google ScholarDigital Library
- X. Carreras, L. Màrquez, and L. Padró. 2003. Learning a perceptron-based named entity chunker via online recognition feedback. In CoNLL. Google ScholarDigital Library
- H. Chieu and H. T. Ng. 2003. Named entity recognition with a maximum entropy approach. In Proceedings of CoNLL. Google ScholarDigital Library
- W. W. Cohen. 2004. Exploiting dictionaries in named entity extraction: Combining semi-markov extraction processes and data integration methods. In KDD. Google ScholarDigital Library
- M. Collins. 2002. Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms. In EMNLP. Google ScholarDigital Library
- L. Edward. 2007. Finding good sequential model structures using output transformations. In EMNLP).Google Scholar
- O. Etzioni, M. J. Cafarella, D. Downey, A. Popescu, T. Shaked, S. Soderland, D. S. Weld, and A. Yates. 2005. Unsupervised named-entity extraction from the web: An experimental study. Artificial Intelligence, 165(1):91--134. Google ScholarDigital Library
- J. R. Finkel, T. Grenager, and C. D. Manning. 2005. Incorporating non-local information into information extraction systems by gibbs sampling. In ACL. Google ScholarDigital Library
- R. Florian, A. Ittycheriah, H. Jing, and T. Zhang. 2003. Named entity recognition through classifier combination. In CoNLL. Google ScholarDigital Library
- Y. Freund and R. Schapire. 1999. Large margin classification using the perceptron algorithm. Machine Learning, 37(3):277--296. Google ScholarDigital Library
- J. Kazama and K. Torisawa. 2007a. Exploiting wikipedia as external knowledge for named entity recognition. In EMNLP.Google Scholar
- J. Kazama and K. Torisawa. 2007b. A new perceptron algorithm for sequence labeling with non-local features. In EMNLP-CoNLL.Google Scholar
- T. Koo, X. Carreras, and M. Collins. 2008. Simple semi-supervised dependency parsing. In ACL.Google Scholar
- V. Krishnan and C. D. Manning. 2006. An effective two-stage model for exploiting non-local dependencies in named entity recognition. In ACL. Google ScholarDigital Library
- J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML. Morgan Kaufmann. Google ScholarDigital Library
- P. Liang. 2005. Semi-supervised learning for natural language. Masters thesis, Massachusetts Institute of Technology.Google Scholar
- S. Miller, J. Guinness, and A. Zamanian. 2004. Name tagging with word clusters and discriminative training. In HLT-NAACL.Google Scholar
- A. Molina and F. Pla. 2002. Shallow parsing using specialized hmms. The Journal of Machine Learning Research, 2:595--613. Google ScholarDigital Library
- A. Niculescu-Mizil and R. Caruana. 2005. Predicting good probabilities with supervised learning. In ICML. Google ScholarDigital Library
- V. Punyakanok and D. Roth. 2001. The use of classifiers in sequential inference. In NIPS.Google Scholar
- L. R. Rabiner. 1989. A tutorial on hidden markov models and selected applications in speech recognition. In IEEE.Google Scholar
- E. Riloff and R. Jones. 1999. Learning dictionaries for information extraction by multi-level bootstrapping. In AAAI. Google ScholarDigital Library
- N. Rizzolo and D. Roth. 2007. Modeling discriminative global inference. In ICSC. Google ScholarDigital Library
- D. Roth and D. Zelenko. 1998. Part of speech tagging using a network of linear separators. In COLING-ACL. Google ScholarDigital Library
- H. Shen and A. Sarkar. 2005. Voting between multiple data representations for text chunking. Advances in Artificial Intelligence, pages 389--400. Google ScholarDigital Library
- J. Suzuki and H. Isozaki. 2008. Semi-supervised sequential labeling and segmentation using giga-word scale unlabeled data. In ACL.Google Scholar
- E. Tjong, K. and F. De Meulder. 2003. Introduction to the conll-2003 shared task: Language-independent named entity recognition. In CoNLL. Google ScholarDigital Library
- A. Toral and R. Munoz. 2006. A proposal to automatically build and maintain gazetteers for named entity recognition by using wikipedia. In EACL.Google Scholar
- K. Toutanova, D. Klein, C. Manning, and Y. Singer. 2003. Feature-rich part-of-speech tagging with a cyclic dependency network. In NAACL. Google ScholarDigital Library
- J. Veenstra. 1999. Representing text chunks. In EACL.Google Scholar
- T. Zhang and D. Johnson. 2003. A robust risk minimization based named entity recognition system. In CoNLL. Google ScholarDigital Library
Index Terms
- Design challenges and misconceptions in named entity recognition
Recommendations
Named entity recognition in Wikipedia
People's Web '09: Proceedings of the 2009 Workshop on The People's Web Meets NLP: Collaboratively Constructed Semantic ResourcesNamed entity recognition (NER) is used in many domains beyond the newswire text that comprises current gold-standard corpora. Recent work has used Wikipedia's link structure to automatically generate near gold-standard annotations. Until now, these ...
Exploring entity relations for named entity disambiguation
HLT-SS '11: Proceedings of the ACL 2011 Student SessionNamed entity disambiguation is the task of linking an entity mention in a text to the correct real-world referent predefined in a knowledge base, and is a crucial subtask in many areas like information retrieval or topic detection and tracking. Named ...
A joint named entity recognition and entity linking system
HYBRID '12: Proceedings of the Workshop on Innovative Hybrid Approaches to the Processing of Textual DataWe present a joint system for named entity recognition (NER) and entity linking (EL), allowing for named entities mentions extracted from textual data to be matched to uniquely identifiable entities. Our approach relies on combined NER modules which ...
Comments