Abstract
This article describes our proposed system named LIM-LIG. This system is designed for SemEval 2017 Task1: Semantic Textual Similarity (Track1). LIM-LIG proposes an innovative enhancement to word embedding-based model devoted to measure the semantic similarity in Arabic sentences. The main idea is to exploit the word representations as vectors in a multidimensional space to capture the semantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. LIM-LIG system achieves a Pearson’s correlation of 0.74633, ranking 2nd among all participants in the Arabic monolingual pairs STS task organized within the SemEval 2017 evaluation campaign- Anthology ID:
- S17-2017
- Volume:
- Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
- Month:
- August
- Year:
- 2017
- Address:
- Vancouver, Canada
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 134–138
- Language:
- URL:
- https://aclanthology.org/S17-2017
- DOI:
- 10.18653/v1/S17-2017
- Cite (ACL):
- El Moatez Billah Nagoudi, Jérémy Ferrero, and Didier Schwab. 2017. LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 134–138, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting (Nagoudi et al., SemEval 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/S17-2017.pdf