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Computer Science > Computation and Language

arXiv:2204.03251 (cs)
[Submitted on 7 Apr 2022 (v1), last revised 19 Oct 2023 (this version, v3)]

Title:Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings

Authors:Dan John Velasco, Axel Alba, Trisha Gail Pelagio, Bryce Anthony Ramirez, Unisse Chua, Briane Paul Samson, Jan Christian Blaise Cruz, Charibeth Cheng
View a PDF of the paper titled Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings, by Dan John Velasco and 6 other authors
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Abstract:Wordnets are indispensable tools for various natural language processing applications. Unfortunately, wordnets get outdated, and producing or updating wordnets can be slow and costly in terms of time and resources. This problem intensifies for low-resource languages. This study proposes a method for word sense induction and synset induction using only two linguistic resources, namely, an unlabeled corpus and a sentence embeddings-based language model. The resulting sense inventory and synonym sets can be used in automatically creating a wordnet. We applied this method on a corpus of Filipino text. The sense inventory and synsets were evaluated by matching them with the sense inventory of the machine translated Princeton WordNet, as well as comparing the synsets to the Filipino WordNet. This study empirically shows that the 30% of the induced word senses are valid and 40% of the induced synsets are valid in which 20% are novel synsets.
Comments: To appear in SEALP 2023. Formerly titled "Automatic WordNet Construction using Word Sense Induction through Sentence Embeddings"
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2204.03251 [cs.CL]
  (or arXiv:2204.03251v3 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2204.03251
arXiv-issued DOI via DataCite

Submission history

From: Dan John Velasco [view email]
[v1] Thu, 7 Apr 2022 06:50:37 UTC (588 KB)
[v2] Fri, 4 Aug 2023 11:12:09 UTC (1,563 KB)
[v3] Thu, 19 Oct 2023 06:42:39 UTC (7,638 KB)
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