Constructing Biological Knowledge Bases by Extracting Information from Text Sources
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
An integrated model of semantic and conceptual interpretation from dependency structures
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Automatic acquisition of hyponyms from large text corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Motif Extraction and Protein Classification
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
A bootstrapping method for learning semantic lexicons using extraction pattern contexts
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Combining Information Extraction Systems Using Voting and Stacked Generalization
The Journal of Machine Learning Research
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
Semantic role chunking combining complementary syntactic views
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Alignment-based expansion of textual database fields
CICLing'08 Proceedings of the 9th international conference on Computational linguistics and intelligent text processing
| Hi-index | 0.00 |
In this paper we discuss an approach to named entity recognition (NER) based on grammatical inference (GI). Previous GI approaches have aimed at constructing a grammar underlying a given text source. It has been noted that the rules produced by GI can also be interpreted semantically [16] where a non-terminal describes interchangeable elements which are the instances of the same concepts. Such an observation leads to the hypothesis that GI might be useful for finding concept instances in a text. Furthermore, it should also be possible to discover relations between concepts, or more precisely, the way such relations are expressed linguistically. Throughout the paper, we propose a general framework for using GI for named entity recognition by discussing several possible approaches. In addition, we demonstrate that these methods successfully work on biomedical data using an existing GI tool.