AnlamVer: Semantic model evaluation dataset for Turkish - word similarity and relatedness
Küçük Resim Yok
Tarih
2018-08-26
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Association for Computational Linguistics (ACL)
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this paper, we present AnlamVer, which is a semantic model evaluation dataset for Turkish designed to evaluate word similarity and word relatedness tasks while discriminating those two relations from each other. Our dataset consists of 500 word-pairs annotated by 12 human subjects, and each pair has two distinct scores for similarity and relatedness. Word-pairs are selected to enable the evaluation of distributional semantic models by multiple attributes of words and word-pair relations such as frequency, morphology, concreteness and relation types (e.g., synonymy, antonymy). Our aim is to provide insights to semantic model researchers by evaluating models in multiple attributes. We balance dataset word-pairs by their frequencies to evaluate the robustness of semantic models concerning out-of-vocabulary and rare words problems, which are caused by the rich derivational and inflectional morphology of the Turkish language.
Açıklama
Anahtar Kelimeler
Computational linguistics, Semantics, Distributional semantics, Evaluating models, Human subjects, Model evaluation, Multiple attributes, Semantic modelling, Turkishs, Word problem, Word similarity, Word-pairs, Morphology
Kaynak
WoS Q Değeri
Scopus Q Değeri
N/A
Cilt
Sayı
Künye
Ercan, G. & Yıldız, O. T. (2018). AnlamVer: Semantic model evaluation dataset for Turkish - word similarity and relatedness. Paper presented at the COLING 2018 - 27th International Conference on Computational Linguistics, Proceedings, 3819 - 3836.
