@inproceedings{conceicao-etal-2023-lasigebiotm,
title = "lasige{B}io{TM} at {S}em{E}val-2023 Task 7: Improving Natural Language Inference Baseline Systems with Domain Ontologies",
author = "Concei{\c{c}}{\~a}o, Sofia I. R. and
F. Sousa, Diana and
Silvestre, Pedro and
Couto, Francisco M",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.2",
doi = "10.18653/v1/2023.semeval-1.2",
pages = "10--15",
abstract = "Clinical Trials Reports (CTRs) contain highly valuable health information from which Natural Language Inference (NLI) techniques determine if a given hypothesis can be inferred from a given premise. CTRs are abundant with domain terminology with particular terms that are difficult to understand without prior knowledge. Thus, we proposed to use domain ontologies as a source of external knowledge that could help with the inference process in theSemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT). This document describes our participation in subtask 1: Textual Entailment, where Ontologies, NLP techniques, such as tokenization and named-entity recognition, and rule-based approaches are all combined in our approach. We were able to show that inputting annotations from domain ontologies improved the baseline systems.",
}
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<abstract>Clinical Trials Reports (CTRs) contain highly valuable health information from which Natural Language Inference (NLI) techniques determine if a given hypothesis can be inferred from a given premise. CTRs are abundant with domain terminology with particular terms that are difficult to understand without prior knowledge. Thus, we proposed to use domain ontologies as a source of external knowledge that could help with the inference process in theSemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT). This document describes our participation in subtask 1: Textual Entailment, where Ontologies, NLP techniques, such as tokenization and named-entity recognition, and rule-based approaches are all combined in our approach. We were able to show that inputting annotations from domain ontologies improved the baseline systems.</abstract>
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%0 Conference Proceedings
%T lasigeBioTM at SemEval-2023 Task 7: Improving Natural Language Inference Baseline Systems with Domain Ontologies
%A Conceição, Sofia I. R.
%A F. Sousa, Diana
%A Silvestre, Pedro
%A Couto, Francisco M.
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F conceicao-etal-2023-lasigebiotm
%X Clinical Trials Reports (CTRs) contain highly valuable health information from which Natural Language Inference (NLI) techniques determine if a given hypothesis can be inferred from a given premise. CTRs are abundant with domain terminology with particular terms that are difficult to understand without prior knowledge. Thus, we proposed to use domain ontologies as a source of external knowledge that could help with the inference process in theSemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT). This document describes our participation in subtask 1: Textual Entailment, where Ontologies, NLP techniques, such as tokenization and named-entity recognition, and rule-based approaches are all combined in our approach. We were able to show that inputting annotations from domain ontologies improved the baseline systems.
%R 10.18653/v1/2023.semeval-1.2
%U https://aclanthology.org/2023.semeval-1.2
%U https://doi.org/10.18653/v1/2023.semeval-1.2
%P 10-15
Markdown (Informal)
[lasigeBioTM at SemEval-2023 Task 7: Improving Natural Language Inference Baseline Systems with Domain Ontologies](https://aclanthology.org/2023.semeval-1.2) (Conceição et al., SemEval 2023)
ACL