A maximum entropy approach to natural language processing
Computational Linguistics
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Biomedical named entity recognition using two-phase model based on SVMs
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
ME-based biomedical named entity recognition using lexical knowledge
ACM Transactions on Asian Language Information Processing (TALIP)
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Maximum entropy based semantic role labeling
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Word folding: taking the snapshot of words instead of the whole
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
| Hi-index | 0.00 |
In this paper, we propose new external context features for the semantic classification of bio-entities. In the previous approaches, the words located on the left or the right context of bio-entities are frequently used as the external context features. However, in our prior experiments, the external contexts in a flat representation did not improve the performance. In this study, we incorporate predicate-argument features into training the ME-based classifier. Through parsing and argument identification, we recognize biomedical verbs that have argument relations with the constituents including a bio-entity, and then use the predicate-argument structures as the external context features. The extraction of predicate-argument features can be done by performing two identification tasks: the biomedically salient word identification which determines whether a word is a biomedically salient word or not, and the target verb identification which identifies biomedical verbs that have argument relations with the constituents including a bio-entity. Experiments show that the performance of semantic classification in the bio domain can be improved by utilizing such predicate-argument features.