Paper Info
Title
A Semi-automatic System for Tagging Specialized Corpora
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 Brill's tagger and to adapt it to a corpus of specialty. The user visualizes an initial and basic tagging and corrects it either by extending Brill's lexicon or by the insertion of specialized lexical and contextual rules. The inserted rules are richer and more flexible than Brill's 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.
Year
DOI
Venue
2004
10.1007/978-3-540-24775-3_79
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS
Keywords
Field
DocType
machine learning,molecular biology
Inductive logic programming,Regular expression,Lexical rule,Computer science,Expert system,Lexicon,Lexico,Artificial intelligence,Natural language processing,Parsing,Knowledge base,Machine learning
Conference
Volume
ISSN
Citations 
3056
0302-9743
9
PageRank 
References 
Authors
0.58
12
3
Name
Order
Citations
PageRank
Ahmed Amrani1102.28
Yves Kodratoff2581172.25
Oriane Matte-tailliez3183.23