HJ-Ky-0.1: an Evaluation Dataset for Kyrgyz Word Embeddings

Autor: Alekseev, Anton, Kabaeva, Gulnara
Rok vydání: 2024
Předmět:
Zdroj: Herald of KSTU 68(4) (2023)
Druh dokumentu: Working Paper
DOI: 10.56634/16948335.2023.4.1723-1731
Popis: 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.
Comment: The translation of the 2023 paper into English
Databáze: arXiv