RIJP at SemEval-2020 Task 1: Gaussian-based Embeddings for Semantic Change Detection
24649Authors
nan
Abstract
AbstractThis paper describes the model proposed and submitted by our RIJP team to SemEval 2020 Task1: Unsupervised Lexical Semantic Change Detection. In the model, words are represented by Gaussian distributions. For Subtask 1, the model achieved average scores of 0.51 and 0.70 in the evaluation and post-evaluation processes, respectively. The higher score in the post-evaluation process than that in the evaluation process was achieved owing to appropriate parameter tuning. The results indicate that the proposed Gaussian-based embedding model is able to express semantic shifts while having a low computational
Keywords
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nan
Labeled Text
AbstractThis paper describes the model proposed and submitted by our RIJP team to SemEval 2020 Task1: Unsupervised Lexical Semantic Change Detection. In the model, words are represented by Gaussian distributions. For Subtask 1, the model achieved average scores of 0.51 and 0.70 in the evaluation and post-evaluation processes, respectively. The higher score in the post-evaluation process than that in the evaluation process was achieved owing to appropriate parameter tuning. The results indicate that the proposed Gaussian-based embedding model is able to express semantic shifts while having a low computational
Publication Details
- Paper ID:
- 24649
- Year:
- 2020
- Publication:
- TIPSTER
- Publication Type:
- conf
- DOI:
- nan