Improving the performance of graph based dependency parsing by guiding bi-affine layer with augmented global and local features

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AE2K66JV3" target="_blank" >RIV/00216208:11320/23:E2K66JV3 - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148029592&doi=10.1016%2fj.iswa.2023.200190&partnerID=40&md5=9076de4c2f23b6d5f724e67a11bfd3bb" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148029592&doi=10.1016%2fj.iswa.2023.200190&partnerID=40&md5=9076de4c2f23b6d5f724e67a11bfd3bb</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.iswa.2023.200190" target="_blank" >10.1016/j.iswa.2023.200190</a>

Result language

angličtina

Original language name

Improving the performance of graph based dependency parsing by guiding bi-affine layer with augmented global and local features

Original language description

"The growing interaction between humans and machines raises the necessity to more sophisticated tools for natural language understanding. Dependency parsing is crucial for capturing the semantics of a sentence. Although graph-based dependency parsing approaches outperform transition-based methods because they are not exposed to error propagation as their compeer, their feature space is comparatively limited. Thus, the main issue with graph-based parsing is how to expand the set of features to improve performance. In this research, we propose to expand the feature space of graph-based parsers. To benefit from the global meaning of the entire sentence content, we employee the sentence representation as an additional token feature. Also, to highlight local word collaborations that build sub-tree structures, we use convolutional neural network layers over token embeddings. We achieve the state-of-art results for Turkish, English, Hungarian, and Korean by getting the unlabeled and labeled attachment scores respectively on the test sets; 82.64% and 76.35% on Turkish IMST, 93.36% and 91.34% on English EWT, 90.85% and 87.39% on Hungarian Szeged, 92.44% and 89.58% on Korean GSD treebanks. Our experimental findings show that augmented global and local features empower the performance of graph-based dependency parsers. © 2023 The Author(s)"

Czech name

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Czech description

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Type

J_SC - Article in a specialist periodical, which is included in the SCOPUS database

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

—

Continuities

—

Publication year

2023

Confidentiality

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

Name of the periodical

"Intelligent Systems with Applications"

ISSN

2667-3053

e-ISSN

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Volume of the periodical

18

Issue of the periodical within the volume

2023

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

1-14

UT code for WoS article

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EID of the result in the Scopus database

2-s2.0-85148029592