@inproceedings{bhargava-etal-2024-lcgbank-corpus,
title = "{LCG}bank: A Corpus of Syntactic Analyses Based on Proof Nets",
author = "Bhargava, Aditya and
Fowler, Timothy A. D. and
Penn, Gerald",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.893",
pages = "10225--10236",
abstract = "In syntactic parsing, *proof nets* are graphical structures that have the advantageous property of invariance to spurious ambiguities. Semantically-equivalent derivations correspond to a single proof net. Recent years have seen fresh interest in statistical syntactic parsing with proof nets, including the development of methods based on neural networks. However, training of statistical parsers requires corpora that provide ground-truth syntactic analyses. Unfortunately, there has been a paucity of corpora in formalisms for which proof nets are applicable, such as Lambek categorial grammar (LCG), a formalism related to combinatory categorial grammar (CCG). To address this, we leverage CCGbank and the relationship between LCG and CCG to develop LCGbank, an English-language corpus of syntactic analyses based on LCG proof nets. In contrast to CCGbank, LCGbank eschews type-changing and uses only categorial rules; the syntactic analyses thus provide fully compositional semantics, exploiting the transparency between syntax and semantics that so characterizes categorial grammars.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bhargava-etal-2024-lcgbank-corpus">
<titleInfo>
<title>LCGbank: A Corpus of Syntactic Analyses Based on Proof Nets</title>
</titleInfo>
<name type="personal">
<namePart type="given">Aditya</namePart>
<namePart type="family">Bhargava</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Timothy</namePart>
<namePart type="given">A</namePart>
<namePart type="given">D</namePart>
<namePart type="family">Fowler</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Gerald</namePart>
<namePart type="family">Penn</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Nicoletta</namePart>
<namePart type="family">Calzolari</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Min-Yen</namePart>
<namePart type="family">Kan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Veronique</namePart>
<namePart type="family">Hoste</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alessandro</namePart>
<namePart type="family">Lenci</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sakriani</namePart>
<namePart type="family">Sakti</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nianwen</namePart>
<namePart type="family">Xue</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>In syntactic parsing, *proof nets* are graphical structures that have the advantageous property of invariance to spurious ambiguities. Semantically-equivalent derivations correspond to a single proof net. Recent years have seen fresh interest in statistical syntactic parsing with proof nets, including the development of methods based on neural networks. However, training of statistical parsers requires corpora that provide ground-truth syntactic analyses. Unfortunately, there has been a paucity of corpora in formalisms for which proof nets are applicable, such as Lambek categorial grammar (LCG), a formalism related to combinatory categorial grammar (CCG). To address this, we leverage CCGbank and the relationship between LCG and CCG to develop LCGbank, an English-language corpus of syntactic analyses based on LCG proof nets. In contrast to CCGbank, LCGbank eschews type-changing and uses only categorial rules; the syntactic analyses thus provide fully compositional semantics, exploiting the transparency between syntax and semantics that so characterizes categorial grammars.</abstract>
<identifier type="citekey">bhargava-etal-2024-lcgbank-corpus</identifier>
<location>
<url>https://aclanthology.org/2024.lrec-main.893</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>10225</start>
<end>10236</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T LCGbank: A Corpus of Syntactic Analyses Based on Proof Nets
%A Bhargava, Aditya
%A Fowler, Timothy A. D.
%A Penn, Gerald
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F bhargava-etal-2024-lcgbank-corpus
%X In syntactic parsing, *proof nets* are graphical structures that have the advantageous property of invariance to spurious ambiguities. Semantically-equivalent derivations correspond to a single proof net. Recent years have seen fresh interest in statistical syntactic parsing with proof nets, including the development of methods based on neural networks. However, training of statistical parsers requires corpora that provide ground-truth syntactic analyses. Unfortunately, there has been a paucity of corpora in formalisms for which proof nets are applicable, such as Lambek categorial grammar (LCG), a formalism related to combinatory categorial grammar (CCG). To address this, we leverage CCGbank and the relationship between LCG and CCG to develop LCGbank, an English-language corpus of syntactic analyses based on LCG proof nets. In contrast to CCGbank, LCGbank eschews type-changing and uses only categorial rules; the syntactic analyses thus provide fully compositional semantics, exploiting the transparency between syntax and semantics that so characterizes categorial grammars.
%U https://aclanthology.org/2024.lrec-main.893
%P 10225-10236
Markdown (Informal)
[LCGbank: A Corpus of Syntactic Analyses Based on Proof Nets](https://aclanthology.org/2024.lrec-main.893) (Bhargava et al., LREC-COLING 2024)
ACL
- Aditya Bhargava, Timothy A. D. Fowler, and Gerald Penn. 2024. LCGbank: A Corpus of Syntactic Analyses Based on Proof Nets. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 10225–10236, Torino, Italia. ELRA and ICCL.