Sparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3ARRVYGZKU" target="_blank" >RIV/00216208:11320/25:RRVYGZKU - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195898062&partnerID=40&md5=17588cb08fb2dbf3f9408f5dba2ff623" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195898062&partnerID=40&md5=17588cb08fb2dbf3f9408f5dba2ff623</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Sparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks

Original language description

Descriptive grammars are highly valuable, but writing them is time-consuming and difficult. Furthermore, while linguists typically use corpora to create them, grammar descriptions often lack quantitative data. As for formal grammars, they can be challenging to interpret. In this paper, we propose a new method to extract and explore significant fine-grained grammar patterns and potential syntactic grammar rules from treebanks, in order to create an easy-to-understand corpus-based grammar. More specifically, we extract descriptions and rules across different languages for two linguistic phenomena, agreement and word order, using a large search space and paying special attention to the ranking order of the extracted rules. For that, we use a linear classifier to extract the most salient features that predict the linguistic phenomena under study. We associate statistical information to each rule, and we compare the ranking of the model's results to those of other quantitative and statistical measures. Our method captures both well-known and less well-known significant grammar rules in Spanish, French, and Wolof. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Jt. Int. Conf. Comput. Linguist., Lang. Resour. Eval., LREC-COLING - Main Conf. Proc.

ISBN

978-249381410-4

ISSN

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e-ISSN

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Number of pages

12

Pages from-to

15114-15125

Publisher name

European Language Resources Association (ELRA)

Place of publication

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Event location

Torino, Italia

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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