This paper presents a lightweight approach to pronoun resolution in the case when the antecedent is named entity. It falls under the category of the so-called "knowledge poor" approaches that do not rely extensively on linguistic and domain knowledge. We provide a practical implementation of this approach as a component of the General Architecture for Text Engineering (GATE). The results of the evaluation show that even such shallow and inexpensive approaches provide acceptable performance for resolving the pronoun anaphors of named entities in texts.
%0 Conference Paper
%1 Dimitrov2005
%A Dimitrov, Marin
%A Bontcheva, Kalina
%A Cunningham, Hamish
%A Maynard, Diana
%B Anaphora Processing: Linguistic, Cognitive and Computational Modelling
%D 2005
%E Branco, A.
%E McEnery, T.
%E Mitkov, R.
%I John Benjamins
%K coreference classification nlp clustering
%T A Light-weight Approach to Coreference Resolution for Named Entities in Text
%X This paper presents a lightweight approach to pronoun resolution in the case when the antecedent is named entity. It falls under the category of the so-called "knowledge poor" approaches that do not rely extensively on linguistic and domain knowledge. We provide a practical implementation of this approach as a component of the General Architecture for Text Engineering (GATE). The results of the evaluation show that even such shallow and inexpensive approaches provide acceptable performance for resolving the pronoun anaphors of named entities in texts.
@inproceedings{Dimitrov2005,
abstract = {This paper presents a lightweight approach to pronoun resolution in the case when the antecedent is named entity. It falls under the category of the so-called "knowledge poor" approaches that do not rely extensively on linguistic and domain knowledge. We provide a practical implementation of this approach as a component of the General Architecture for Text Engineering (GATE). The results of the evaluation show that even such shallow and inexpensive approaches provide acceptable performance for resolving the pronoun anaphors of named entities in texts.},
added-at = {2010-06-11T17:25:47.000+0200},
author = {Dimitrov, Marin and Bontcheva, Kalina and Cunningham, Hamish and Maynard, Diana},
biburl = {https://www.bibsonomy.org/bibtex/2050cef3cee72d89ee41ea2b291f0152d/lama},
booktitle = {Anaphora Processing: Linguistic, Cognitive and Computational Modelling},
description = {The big one},
editor = {Branco, A. and McEnery, T. and Mitkov, R.},
file = {:./DAARC2002.pdf:PDF},
interhash = {bd0d88fe0d4137a1d21790d3d1b38a5b},
intrahash = {050cef3cee72d89ee41ea2b291f0152d},
keywords = {coreference classification nlp clustering},
publisher = {John Benjamins},
timestamp = {2010-06-11T17:25:47.000+0200},
title = {{A Light-weight Approach to Coreference Resolution for Named Entities in Text}},
year = 2005
}