ABSTRACT
Models for many natural language tasks benefit from the flexibility to use overlapping, non-independent features. For example, the need for labeled data can be drastically reduced by taking advantage of domain knowledge in the form of word lists, part-of-speech tags, character n-grams, and capitalization patterns. While it is difficult to capture such inter-dependent features with a generative probabilistic model, conditionally-trained models, such as conditional maximum entropy models, handle them well. There has been significant work with such models for greedy sequence modeling in NLP (Ratnaparkhi, 1996; Borthwick et al., 1998).
- A. Borthwick, J. Sterling, E. Agichtein, and R. Grishman. 1998. Exploiting diverse knowledge sources via maximum entropy in named entity recognition. In Proceedings of the Sixth Workshop on Very Large Corpora, Association for Computational Linguistics.Google Scholar
- M. Collins and Y. Singer. 1999. Unsupervised models for named entity classification. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora.Google Scholar
- Stephen Della Pietra, Vincent J. Della Pietra, and John D. Lafferty. 1997. Inducing Features of Random Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(4):380--393. Google ScholarDigital Library
- Rosie Jones, Andrew McCallum, Kamal Nigam, and Ellen Riloff. 1999. Bootstrapping for Text Learning Tasks. In IJCAI-99 Workshop on Text Mining: Foundations, Techniques and Applications.Google Scholar
- John Lafferty, Andrew McCallum, and Fernando Pereira. 2001. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proc. ICML. Google ScholarDigital Library
- Robert Malouf. 2002. A comparison of algorithms for maximum entropy parameter estimation. In Sixth Workshop on Computational Language Learning (CoNLL-2002). Google ScholarDigital Library
- Andrew McCallum and Fang-Fang Feng. 2003. Chinese Word Segmentation with Conditional Random Fields and Integrated Domain Knowledge. In Unpublished Manuscript.Google Scholar
- Andrew McCallum. 2003. Efficiently Inducing Features of Conditional Random Fields. In Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI03). (Submitted). Google ScholarDigital Library
- Adwait Ratnaparkhi. 1996. A Maximum Entropy Model for Part-of-Speech Tagging. In Eric Brill and Kenneth Church, editors, Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 133--142. Association for Computational Linguistics.Google Scholar
- Fei Sha and Fernando Pereira. 2003. Shallow Parsing with Conditional Random Fields. In Proceedings of Human Language Technology, NAACL. Google ScholarDigital Library
Recommendations
Conditional Random Fields for Spanish Named Entity Recognition Using Unsupervised Features
Advances in Artificial Intelligence - IBERAMIA 2016AbstractUnsupervised features based on word representations such as word embeddings and word collocations have shown to significantly improve supervised NER for English. In this work we investigate whether such unsupervised features can also boost ...
Named Entity Recognition in Hindi Using Conditional Random Fields
ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive StrategiesNamed Entity Recognition (NER) is the process of finding the Named Entities or proper nouns from a text. In the following paper, we have presented NER in Hindi using CRF++0.58. We have discussed about NER, Challenges in NER in Indian languages and CRF++...
Comments