Different Approach for Induction of Unsupervised Lexical Semantic Frames
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AJGQ96HRW" target="_blank" >RIV/00216208:11320/25:JGQ96HRW - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198225749&doi=10.1109%2fMIPRO60963.2024.10569421&partnerID=40&md5=6049c0697befefe1484e56759840a2f5" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198225749&doi=10.1109%2fMIPRO60963.2024.10569421&partnerID=40&md5=6049c0697befefe1484e56759840a2f5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/MIPRO60963.2024.10569421" target="_blank" >10.1109/MIPRO60963.2024.10569421</a>
Alternative languages
Result language
angličtina
Original language name
Different Approach for Induction of Unsupervised Lexical Semantic Frames
Original language description
The challenge of dealing with the inherent ambiguity and constant evolution of human language could be addressed through semantic role labeling, a process of assigning labels to words or phrases to indicate their roles in a sentence. The goal is to discern sentence meanings by detecting and assigning specific roles to arguments associated with predicates or verbs. This paper emphasizes the impact of lexicons, particularly VerbNet and FrameNet, on labeling and classification. To overcome language ambiguity, we look into solving two tasks: grouping verbs into frame type clusters and clustering verb arguments into frame-specific slots or generic roles. The research effort described herein employs a fully unsupervised approach, utilizing Agglomerative clustering and extracting information from the CoNLL-U format. Three models - BERT, ELMo, and Word2Vec - generate embeddings, and the results are analyzed through agglomerative clustering with optimized hyperparameters. The paper suggests potential enhancements, such as incorporating a small set of annotated data for semi-supervised learning, expanding the dataset, and assessing system performance across different languages by training language models on new language samples. Overall, the research strives to provide a comprehensive solution to the multifaceted challenges of understanding and interpreting evolving human language. © 2024 IEEE.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
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Continuities
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Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICT Electron. Conv., MIPRO - Proc.
ISBN
979-835038249-5
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
79-84
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
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Event location
Opatia
Event date
Jan 1, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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