Word association norms, mutual information, and lexicography
Computational Linguistics
C4.5: programs for machine learning
C4.5: programs for machine learning
Some advances in transformation-based part of speech tagging
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
ILP in part-of-speech tagging — an overview
Learning language in logic
Part-of-Speech Tagging Using Decision Trees
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Part-of-Speech Tagging Using Progol
ILP '97 Proceedings of the 7th International Workshop on Inductive Logic Programming
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Improving accuracy in word class tagging through the combination of machine learning systems
Computational Linguistics
TnT: a statistical part-of-speech tagger
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
A practical part-of-speech tagger
ANLC '92 Proceedings of the third conference on Applied natural language processing
Learning Constraint Grammar-style disambiguation rules using inductive logic programming
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Classifier combination for improved lexical disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Creating a multilingual collocation dictionary from large text corpora
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
| Hi-index | 0.00 |
In the context of Part-of-Speech (PoS)-tagging of specialized corpora, we proposed an inductive approach focusing on the most ‘important' PoStags because mistaking them can lead to a total misunderstanding of the text After a standard tagging of a biological corpus by Brill's tagger, we noted persistent errors that are very hard to deal with As an application, we studied two cases of different nature: first, confusion between past participle, adjective and preterit for verbs that end with ‘ed'; second, confusion between plural nouns and verbs, 3rd person singular present With a friendly user interface, the expert corrected the examples Then, from these well-annotated examples, we induced rules using a propositional rule induction algorithm Experimental validation showed improvement in tagging precision The relevance of the terminology of the considered field, here molecular biology, is greatly improved when the number of these tagging errors decreases.