Construction of a Japanese Word Similarity Dataset

Yuya Sakaizawa, Mamoru Komachi
3/17/2017
cs.CL

Official code: tmu-nlp/JapaneseWordSimilarityDataset (103 stars, last commit 2021-12-07).

Abstract

An evaluation of distributed word representation is generally conducted using a word similarity task and/or a word analogy task. There are many datasets readily available for these tasks in English. However, evaluating distributed representation in languages that do not have such resources (e.g., Japanese) is difficult. Therefore, as a first step toward evaluating distributed representations in Japanese, we constructed a Japanese word similarity dataset. To the best of our knowledge, our dataset is the first resource that can be used to evaluate distributed representations in Japanese. Moreover, our dataset contains various parts of speech and includes rare words in addition to common words.

View on arXivView PDF

Code Implementations(1)

tmu-nlp/JapaneseWordSimilarityDatasetOfficial100%

Japanese Word Similarity Dataset

10312PythonJan 8, 20164 years ago

Similar Papers

Sparse Overcomplete Word Vector Representations

82% match1 repo572015

On Evaluation of Bangla Word Analogies

81% match2023

WordRank: Learning Word Embeddings via Robust Ranking

81% match1 repo512015

Spoken Word2Vec: Learning Skipgram Embeddings from Speech

80% match1 repo42023

Bridging Natural Language Processing and Psycholinguistics: computationally grounded semantic similarity datasets for Basque and Spanish

79% match2023

IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations

79% match2023

Cite this paper

@article{sakaizawa2017construction,
  title  = {Construction of a Japanese Word Similarity Dataset},
  author = {Yuya Sakaizawa and Mamoru Komachi},
  year   = {2017},
  eprint = {1703.05916},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1703.05916}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/1703.05916.svg)](https://codeofpaper.com/paper/1703.05916)

Discussion