Abstract
We present our approach to predicting lexical complexity of words in specific contexts, as entered LCP Shared Task 1 at SemEval 2021. The approach consists of separating sentences into smaller chunks, embedding them with Sent2Vec, and reducing the embeddings into a simpler vector used as input to a neural network, the latter for predicting the complexity of words and expressions. Results show that the pre-trained sentence embeddings are not able to capture lexical complexity from the language when applied in cross-domain applications.- Anthology ID:
- 2021.semeval-1.88
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venue:
- SemEval
- SIGs:
- SIGLEX | SIGSEM
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 683–687
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.88
- DOI:
- 10.18653/v1/2021.semeval-1.88
- Cite (ACL):
- Raul Almeida, Hegler Tissot, and Marcos Didonet Del Fabro. 2021. C3SL at SemEval-2021 Task 1: Predicting Lexical Complexity of Words in Specific Contexts with Sentence Embeddings. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 683–687, Online. Association for Computational Linguistics.
- Cite (Informal):
- C3SL at SemEval-2021 Task 1: Predicting Lexical Complexity of Words in Specific Contexts with Sentence Embeddings (Almeida et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.semeval-1.88.pdf