Systematization and Identification of Semantic Relations in Ontologies for Scientific and Technical Subject Areas

Maksimov, N. V.; Gavrilkina, Anastasia; Andronova, V. V.; Tazieva, I. A.

doi:10.3103/s000510551806002x

Cited by 11 publications

(1 citation statement)

References 7 publications

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“…If we consider the NLP applications, it has a significant impact on several tasks including semantic search, automatic question/answering, story generation, relation extraction, and machine translation (Barkan et al, 2020). However, most of the prior works focused mainly on syntactic (Luu et al, 2014) tual relation (Maksimov et al, 2018) to infer the lexical relation in between words.…”

Section: Introductionmentioning

confidence: 99%

CSECU-DSG at SemEval-2022 Task 3: Investigating the Taxonomic Relationship Between Two Arguments using Fusion of Multilingual Transformer Models

Wang¹,

Hossain²,

Chy³

2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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Recognizing lexical relationships between words is one of the formidable tasks in computational linguistics. It plays a vital role in the improvement of various NLP tasks. However, the diversity of word semantics, sentence structure as well as word order information make it challenging to distill the relationship effectively. To address these challenges, SemEval-2022 Task 3 introduced a shared task PreTENS focusing on semantic competence to determine the taxonomic relations between two nominal arguments. This paper presents our participation in this task where we proposed an approach through exploiting an ensemble of multilingual transformer models. We employed two finetuned multilingual transformer models including XLM-RoBERTa and mBERT to train our model. To enhance the performance of individual models, we fuse the predicted probability score of these two models using weighted arithmetic mean to generate a unified probability score. The experimental results showed that our proposed method achieved competitive performance among the participants' methods.

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Section: Introductionmentioning

confidence: 99%