Abstract
We present two convolutional neural networks for predicting the complexity of words and phrases in context on a continuous scale. Both models utilize word and character embeddings alongside lexical features as inputs. Our system displays reasonable results with a Pearson correlation of 0.7754 on the task as a whole. We highlight the limitations of this method in properly assessing the context of the target text, and explore the effectiveness of both systems across a range of genres. Both models were submitted as part of LCP 2021, which focuses on the identification of complex words and phrases as a context dependent, regression based task.- Anthology ID:
- 2021.semeval-1.76
- 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:
- 603–608
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.76
- DOI:
- 10.18653/v1/2021.semeval-1.76
- Cite (ACL):
- Robert Flynn and Matthew Shardlow. 2021. Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 603–608, Online. Association for Computational Linguistics.
- Cite (Informal):
- Manchester Metropolitan at SemEval-2021 Task 1: Convolutional Networks for Complex Word Identification (Flynn & Shardlow, SemEval 2021)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.semeval-1.76.pdf