DMCA
A modular approach to learning Dutch co-reference resolution (2007)
| Venue: | In Proceedings of WAR I |
| Citations: | 1 - 0 self |
BibTeX
@INPROCEEDINGS{Hoste07amodular,
author = {Véronique Hoste and Antal Van Den Bosch},
title = {A modular approach to learning Dutch co-reference resolution},
booktitle = {In Proceedings of WAR I},
year = {2007},
pages = {51--75}
}
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Abstract
This paper presents the first machine learning approach to the resolution of co-referential relations between nominal constituents in Dutch. Based on the hypothesis that different types of information sources contribute to a correct resolution of different types (pronominal, proper noun and common noun) of co-referential links, we propose a modular approach in which a separate module is trained per NP type. We present a thorough comparison of two machine learning techniques, a lazy learner and an eager learning approach, trained on the modular tasks as well as on the undecomposed task. In addition, we show that by postprocessing the resulting co-reference chains by means of a string-edit distance correction mechanism, we can avoid some unlikely local chainings and thereby improve precision. Lacking comparative results for Dutch, we also report results on the English MUC-6 and MUC-7 data sets, which are widely used for evaluation. 1
Keyphrases
modular approach dutch co-reference resolution different type proper noun nominal constituent lazy learner common noun correct resolution first machine thorough comparison information source unlikely local chainings modular task co-referential link string-edit distance correction mechanism eager learning approach co-reference chain co-referential relation comparative result np type english muc-6 separate module muc-7 data set undecomposed task
