PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Semi-automatic System for Tagging Specialized Corpora
Ahmed Amrani, Yves Kodratoff and Oriane Matte-Tailliez
In: Advances in Knowledge Discovery and Data Mining. 8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 2004, Proceedings Lecture Notes in Artificial Intelligence. Subseries of Lecture Notes in Computer Science , 3056 (3056). (2004) Springer , Heidelberg, Germany , pp. 670-681. ISBN 3-540-22064-X

Abstract

In this paper, we treat the problem of the grammatical tagging of non-annotated corpora of specialty. The existing taggers are trained on general language corpora, and give inconsistent results on the specialized texts, as technical and scientific ones. In order to learn rules adapted to a specialized field, the usual approach labels manually a large corpus of this field. This is extremely time-consuming. We propose here a semi-automatic approach for tagging corpora of specialty. ETIQ, the new tagger we are building, make it possible to correct the base of rules obtained by Brills tagger and to adapt it to a corpus of specialty. The user visualizes an initial and basic tagging and corrects it either by extending Brills lexicon or by the insertion of specialized lexical and contextual rules. The inserted rules are richer and more flexible than Brills ones. To help the expert in this task, we designed an inductive algorithm biased by the correct knowledge he acquired beforehand. By using techniques of machine learning and enabling the expert to incorporate knowledge of the field in an interactive and friendly way, we improve the tagging of specialized corpora. Our approach has been applied to a corpus of molecular biology.

EPrint Type:Book Section
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Natural Language Processing
Information Retrieval & Textual Information Access
ID Code:647
Deposited By:Oriane Matte-Tailliez
Deposited On:29 December 2004