Leveraging Task Information in Grammatical Error Correction for Short Answer Assessment through Context-based Reranking
Ramon Ziai | Anna Karnysheva
Proceedings of the 10th Workshop on NLP for Computer Assisted Language Learning
This paper describes our system for SemEval 2020 Task 1: Unsupervised Lexical Semantic Change Detection. Target words of corpora from two different time periods are classified according to their semantic change. The languages covered are English, German, Latin, and Swedish. Our approach involves clustering ELMo embeddings using DBSCAN and K-means. For a more fine grained detection of semantic change we take the Jensen-Shannon Distance metric and rank the target words from strongest to weakest change. The results show that this is a valid approach for the classification subtask where we rank 13th out of 33 groups with an accuracy score of 61.2%. For the ranking subtask we score a Spearman’s rank-order correlation coefficient of 0.087 which places us on rank 29.