@inproceedings{prasad-etal-2010-exploiting,
title = "Exploiting Scope for Shallow Discourse Parsing",
author = "Prasad, Rashmi and
Joshi, Aravind and
Webber, Bonnie",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Rosner, Mike and
Tapias, Daniel",
booktitle = "Proceedings of the Seventh International Conference on Language Resources and Evaluation ({LREC}'10)",
month = may,
year = "2010",
address = "Valletta, Malta",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2010/pdf/935_Paper.pdf",
abstract = "We present an approach to automatically identifying the arguments of discourse connectives based on data from the Penn Discourse Treebank. Of the two arguments of connectives, called Arg1 and Arg2, we focus on Arg1, which has proven more challenging to identify. Our approach employs a sentence-based representation of arguments, and distinguishes ''``intra-sentential connectives'''', which take both their arguments in the same sentence, from ''``inter-sentential connectives'''', whose arguments are found in different sentences. The latter are further distinguished by paragraph position into ''``ParaInit'''' connectives, which appear in a paragraph-initial sentence, and ''``ParaNonInit'''' connectives, which appear elsewhere. The paper focusses on predicting Arg1 of Inter-sentential ParaNonInit connectives, presenting a set of scope-based filters that reduce the search space for Arg1 from all the previous sentences in the paragraph to a subset of them. For cases where these filters do not uniquely identify Arg1, coreference-based heuristics are employed. Our analysis shows an absolute 3{\%} performance improvement over the high baseline of 83.3{\%} for identifying Arg1 of Inter-sentential ParaNonInit connectives.",
}
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<abstract>We present an approach to automatically identifying the arguments of discourse connectives based on data from the Penn Discourse Treebank. Of the two arguments of connectives, called Arg1 and Arg2, we focus on Arg1, which has proven more challenging to identify. Our approach employs a sentence-based representation of arguments, and distinguishes ”“intra-sentential connectives””, which take both their arguments in the same sentence, from ”“inter-sentential connectives””, whose arguments are found in different sentences. The latter are further distinguished by paragraph position into ”“ParaInit”” connectives, which appear in a paragraph-initial sentence, and ”“ParaNonInit”” connectives, which appear elsewhere. The paper focusses on predicting Arg1 of Inter-sentential ParaNonInit connectives, presenting a set of scope-based filters that reduce the search space for Arg1 from all the previous sentences in the paragraph to a subset of them. For cases where these filters do not uniquely identify Arg1, coreference-based heuristics are employed. Our analysis shows an absolute 3% performance improvement over the high baseline of 83.3% for identifying Arg1 of Inter-sentential ParaNonInit connectives.</abstract>
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%0 Conference Proceedings
%T Exploiting Scope for Shallow Discourse Parsing
%A Prasad, Rashmi
%A Joshi, Aravind
%A Webber, Bonnie
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Rosner, Mike
%Y Tapias, Daniel
%S Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10)
%D 2010
%8 May
%I European Language Resources Association (ELRA)
%C Valletta, Malta
%F prasad-etal-2010-exploiting
%X We present an approach to automatically identifying the arguments of discourse connectives based on data from the Penn Discourse Treebank. Of the two arguments of connectives, called Arg1 and Arg2, we focus on Arg1, which has proven more challenging to identify. Our approach employs a sentence-based representation of arguments, and distinguishes ”“intra-sentential connectives””, which take both their arguments in the same sentence, from ”“inter-sentential connectives””, whose arguments are found in different sentences. The latter are further distinguished by paragraph position into ”“ParaInit”” connectives, which appear in a paragraph-initial sentence, and ”“ParaNonInit”” connectives, which appear elsewhere. The paper focusses on predicting Arg1 of Inter-sentential ParaNonInit connectives, presenting a set of scope-based filters that reduce the search space for Arg1 from all the previous sentences in the paragraph to a subset of them. For cases where these filters do not uniquely identify Arg1, coreference-based heuristics are employed. Our analysis shows an absolute 3% performance improvement over the high baseline of 83.3% for identifying Arg1 of Inter-sentential ParaNonInit connectives.
%U http://www.lrec-conf.org/proceedings/lrec2010/pdf/935_Paper.pdf
Markdown (Informal)
[Exploiting Scope for Shallow Discourse Parsing](http://www.lrec-conf.org/proceedings/lrec2010/pdf/935_Paper.pdf) (Prasad et al., LREC 2010)
ACL
- Rashmi Prasad, Aravind Joshi, and Bonnie Webber. 2010. Exploiting Scope for Shallow Discourse Parsing. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).