HJ-Ky-0.1: an Evaluation Dataset for Kyrgyz Word Embeddings
This provides a tool for researchers and practitioners working on Kyrgyz natural language processing, but it is incremental as it adapts existing methods to a new language.
The authors tackled the lack of evaluation resources for Kyrgyz word embeddings by creating the first 'silver standard' dataset, and they validated its suitability through quality metrics.
One of the key tasks in modern applied computational linguistics is constructing word vector representations (word embeddings), which are widely used to address natural language processing tasks such as sentiment analysis, information extraction, and more. To choose an appropriate method for generating these word embeddings, quality assessment techniques are often necessary. A standard approach involves calculating distances between vectors for words with expert-assessed 'similarity'. This work introduces the first 'silver standard' dataset for such tasks in the Kyrgyz language, alongside training corresponding models and validating the dataset's suitability through quality evaluation metrics.