@inproceedings{dufour-etal-2022-bl,
title = "{BL}.{R}esearch at {S}em{E}val-2022 Task 8: Using various Semantic Information to evaluate document-level Semantic Textual Similarity",
author = "Dufour, Sebastien and
Mehdi Kandi, Mohamed and
Boutamine, Karim and
Gosse, Camille and
Billami, Mokhtar Boumedyen and
Bortolaso, Christophe and
Miloudi, Youssef",
editor = "Emerson, Guy and
Schluter, Natalie and
Stanovsky, Gabriel and
Kumar, Ritesh and
Palmer, Alexis and
Schneider, Nathan and
Singh, Siddharth and
Ratan, Shyam",
booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
month = jul,
year = "2022",
address = "Seattle, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.semeval-1.173/",
doi = "10.18653/v1/2022.semeval-1.173",
pages = "1221--1228",
abstract = "This paper presents our system for document-level semantic textual similarity (STS) evaluation at SemEval-2022 Task 8: {\textquotedblleft}Multilingual News Article Similarity{\textquotedblright}. The semantic information used is obtained by using different semantic models ranging from the extraction of key terms and named entities to the document classification and obtaining similarity from automatic summarization of documents. All these semantic information`s are then used as features to feed a supervised system in order to evaluate the degree of similarity of a pair of documents. We obtained a Pearson correlation score of 0.706 compared to the best score of 0.818 from teams that participated in this task."
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<abstract>This paper presents our system for document-level semantic textual similarity (STS) evaluation at SemEval-2022 Task 8: “Multilingual News Article Similarity”. The semantic information used is obtained by using different semantic models ranging from the extraction of key terms and named entities to the document classification and obtaining similarity from automatic summarization of documents. All these semantic information‘s are then used as features to feed a supervised system in order to evaluate the degree of similarity of a pair of documents. We obtained a Pearson correlation score of 0.706 compared to the best score of 0.818 from teams that participated in this task.</abstract>
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%0 Conference Proceedings
%T BL.Research at SemEval-2022 Task 8: Using various Semantic Information to evaluate document-level Semantic Textual Similarity
%A Dufour, Sebastien
%A Mehdi Kandi, Mohamed
%A Boutamine, Karim
%A Gosse, Camille
%A Billami, Mokhtar Boumedyen
%A Bortolaso, Christophe
%A Miloudi, Youssef
%Y Emerson, Guy
%Y Schluter, Natalie
%Y Stanovsky, Gabriel
%Y Kumar, Ritesh
%Y Palmer, Alexis
%Y Schneider, Nathan
%Y Singh, Siddharth
%Y Ratan, Shyam
%S Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
%D 2022
%8 July
%I Association for Computational Linguistics
%C Seattle, United States
%F dufour-etal-2022-bl
%X This paper presents our system for document-level semantic textual similarity (STS) evaluation at SemEval-2022 Task 8: “Multilingual News Article Similarity”. The semantic information used is obtained by using different semantic models ranging from the extraction of key terms and named entities to the document classification and obtaining similarity from automatic summarization of documents. All these semantic information‘s are then used as features to feed a supervised system in order to evaluate the degree of similarity of a pair of documents. We obtained a Pearson correlation score of 0.706 compared to the best score of 0.818 from teams that participated in this task.
%R 10.18653/v1/2022.semeval-1.173
%U https://aclanthology.org/2022.semeval-1.173/
%U https://doi.org/10.18653/v1/2022.semeval-1.173
%P 1221-1228
Markdown (Informal)
[BL.Research at SemEval-2022 Task 8: Using various Semantic Information to evaluate document-level Semantic Textual Similarity](https://aclanthology.org/2022.semeval-1.173/) (Dufour et al., SemEval 2022)
ACL