Improving Latin Dependency Parsing by Combining Treebanks and Predictions

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AIHFX53EU" target="_blank" >RIV/00216208:11320/25:IHFX53EU - isvavai.cz</a>

Result on the web

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216580264&partnerID=40&md5=ef3021de3b5b84c97577b16b7f7d7772" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85216580264&partnerID=40&md5=ef3021de3b5b84c97577b16b7f7d7772</a>

DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Improving Latin Dependency Parsing by Combining Treebanks and Predictions

Original language description

This paper introduces new models designed to improve the morpho-syntactic parsing of the five largest Latin treebanks in the Universal Dependencies (UD) framework.First, using two state-of-the-art parsers, Trankit and Stanza, along with our custom UD tagger, we train new models on the five treebanks both individually and by combining them into novel merged datasets.We also test the models on the CIRCSE test set.In an additional experiment, we evaluate whether this set can be accurately tagged using the novel LASLA corpus (https://github.com/CIRCSE/LASLA).Second, we aim to improve the results by combining the predictions of different models through an atomic morphological feature voting system.The results of our two main experiments demonstrate significant improvements, particularly for the smaller treebanks, with LAS scores increasing by 16.10 and 11.85%-points for UDante and Perseus, respectively (Gamba and Zeman, 2023a).Additionally, the voting system for morphological features (FEATS) brings improvements, especially for the smaller Latin treebanks: Perseus 3.15% and CIRCSE 2.47%-points.Tagging the CIRCSE set with our custom model using the LASLA model improves POS 6.71 and FEATS 11.04%-points compared to our best-performing UD PROIEL model.Our results show that larger datasets and ensemble predictions can significantly improve performance. © 2024 Association for Computational Linguistics.

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

NLP4DH - Int. Conf. Nat. Lang. Process. Digit. Humanit., Proc. Conf.

ISBN

979-889176181-0

ISSN

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e-ISSN

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Number of pages

13

Pages from-to

216-228

Publisher name

Association for Computational Linguistics (ACL)

Place of publication

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Event location

Miami

Event date

Jan 1, 2025

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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