The value of parsing as feature generation for gene mention recognition

Authors:
Larry H. Smith;W. John Wilbur
Affiliations:
Computational Biology Branch, National Center for Biotechnology Information, Room 6S614-N, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA;Computational Biology Branch, National Center for Biotechnology Information, Room 6S606, Building 38A, 8600 Rockville Pike, Bethesda, MD 20894, USA
Venue:
Journal of Biomedical Informatics
Year:
2009

Citing 19
Cited 1

A maximum entropy approach to natural language processing

Computational Linguistics
Making large-scale support vector machine learning practical

Advances in kernel methods
Approximate statistical tests for comparing supervised classification learning algorithms

Neural Computation
Improved Boosting Algorithms Using Confidence-rated Predictions

Machine Learning - The Eleventh Annual Conference on computational Learning Theory
A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems

Machine Learning
Text Categorization Based on Regularized Linear Classification Methods

Information Retrieval
A maximum-entropy-inspired parser

NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Principle-based parsing without overgeneration

ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Solving large scale linear prediction problems using stochastic gradient descent algorithms

ICML '04 Proceedings of the twenty-first international conference on Machine learning
Accurate unlexicalized parsing

ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
MedPost: a part-of-speech tagger for bioMedical text

Bioinformatics
Training linear SVMs in linear time

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic disambiguation models for wide-coverage HPSG parsing

ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
The second release of the RASP system

COLING-ACL '06 Proceedings of the COLING/ACL on Interactive presentation sessions
Design of a multi-lingual, parallel-processing statistical parsing engine

HLT '02 Proceedings of the second international conference on Human Language Technology Research
Exploiting context for biomedical entity recognition: from syntax to the web

JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Representing sentence structure in hidden Markov models for information extraction

IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Learning relations from biomedical corpora using dependency trees

KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
Parsing biomedical literature

IJCNLP'05 Proceedings of the Second international joint conference on Natural Language Processing

Guest Editorial: Current issues in biomedical text mining and natural language processing

Journal of Biomedical Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

We measured the extent to which information surrounding a base noun phrase reflects the presence of a gene name, and evaluated seven different parsers in their ability to provide information for that purpose. Using the GENETAG corpus as a gold standard, we performed machine learning to recognize from its context when a base noun phrase contained a gene name. Starting with the best lexical features, we assessed the gain of adding dependency or dependency-like relations from a full sentence parse. Features derived from parsers improved performance in this partial gene mention recognition task by a small but statistically significant amount. There were virtually no differences between parsers in these experiments.