@inproceedings{subba-etal-2006-building,
title = "Building lexical resources for {P}rinc{P}ar, a large coverage parser that generates principled semantic representations",
author = "Subba, Rajen and
Di Eugenio, Barbara and
Terenzi, Elena",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Gangemi, Aldo and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Tapias, Daniel",
booktitle = "Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}{'}06)",
month = may,
year = "2006",
address = "Genoa, Italy",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2006/pdf/495_pdf.pdf",
abstract = "Parsing, one of the more successful areas of Natural Language Processing has mostly been concerned with syntactic structure. Though uncovering the syntactic structure of sentences is very important, in many applications a meaningrepresentation for the input must be derived as well. We report on PrincPar, a parser that builds full meaning representations. It integrates LCFLEX, a robust parser, with alexicon and ontology derived from two lexical resources, VerbNet and CoreLex that represent the semantics of verbs and nouns respectively. We show that these two different lexical resources that focus on verbs and nouns can be successfully integrated. We report parsing results on a corpus of instructional text and assess the coverage of those lexical resources. Our evaluation metric is the number of verb frames that are assigned a correct semantics: 72.2{\%} verb frames are assigned a perfect semantics, and another 10.9{\%} are assigned a partially correctsemantics. Our ultimate goal is to develop a (semi)automatic method to derive domain knowledge from instructional text, in the form of linguistically motivated action schemes.",
}
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<abstract>Parsing, one of the more successful areas of Natural Language Processing has mostly been concerned with syntactic structure. Though uncovering the syntactic structure of sentences is very important, in many applications a meaningrepresentation for the input must be derived as well. We report on PrincPar, a parser that builds full meaning representations. It integrates LCFLEX, a robust parser, with alexicon and ontology derived from two lexical resources, VerbNet and CoreLex that represent the semantics of verbs and nouns respectively. We show that these two different lexical resources that focus on verbs and nouns can be successfully integrated. We report parsing results on a corpus of instructional text and assess the coverage of those lexical resources. Our evaluation metric is the number of verb frames that are assigned a correct semantics: 72.2% verb frames are assigned a perfect semantics, and another 10.9% are assigned a partially correctsemantics. Our ultimate goal is to develop a (semi)automatic method to derive domain knowledge from instructional text, in the form of linguistically motivated action schemes.</abstract>
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%0 Conference Proceedings
%T Building lexical resources for PrincPar, a large coverage parser that generates principled semantic representations
%A Subba, Rajen
%A Di Eugenio, Barbara
%A Terenzi, Elena
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F subba-etal-2006-building
%X Parsing, one of the more successful areas of Natural Language Processing has mostly been concerned with syntactic structure. Though uncovering the syntactic structure of sentences is very important, in many applications a meaningrepresentation for the input must be derived as well. We report on PrincPar, a parser that builds full meaning representations. It integrates LCFLEX, a robust parser, with alexicon and ontology derived from two lexical resources, VerbNet and CoreLex that represent the semantics of verbs and nouns respectively. We show that these two different lexical resources that focus on verbs and nouns can be successfully integrated. We report parsing results on a corpus of instructional text and assess the coverage of those lexical resources. Our evaluation metric is the number of verb frames that are assigned a correct semantics: 72.2% verb frames are assigned a perfect semantics, and another 10.9% are assigned a partially correctsemantics. Our ultimate goal is to develop a (semi)automatic method to derive domain knowledge from instructional text, in the form of linguistically motivated action schemes.
%U http://www.lrec-conf.org/proceedings/lrec2006/pdf/495_pdf.pdf
Markdown (Informal)
[Building lexical resources for PrincPar, a large coverage parser that generates principled semantic representations](http://www.lrec-conf.org/proceedings/lrec2006/pdf/495_pdf.pdf) (Subba et al., LREC 2006)
ACL