TY - JOUR

T1 - An empirical study on the contribution of formal and semantic features to the grammatical gender of nouns

AU - Basirat, Ali

AU - Allassonnière-Tang, Marc

AU - Berdicevskis, Aleksandrs

N1 - Funding Information: Research funding: The second author expresses his gratitude for the support of the IDEXLYON Fellowship Grant (16-IDEX-0005), University of Lyon Grant NSCO ED 476 (ANR-10-LABX-0081), and French National Research Agency (ANR-11-IDEX-0007). Publisher Copyright: © 2021 Walter de Gruyter GmbH. All rights reserved.

PY - 2021/1/1

Y1 - 2021/1/1

N2 - This study conducts an experimental evaluation of two hypotheses about the contributions of formal and semantic features to the grammatical gender assignment of nouns. One of the hypotheses (Corbett and Fraser 2000) claims that semantic features dominate formal ones. The other hypothesis, formulated within the optimal gender assignment theory (Rice 2006), states that form and semantics contribute equally. Both hypotheses claim that the combination of formal and semantic features yields the most accurate gender identification. In this paper, we operationalize and test these hypotheses by trying to predict grammatical gender using only character-based embeddings (that capture only formal features), only context-based embeddings (that capture only semantic features) and the combination of both. We performed the experiment using data from three languages with different gender systems (French, German and Russian). Formal features are a significantly better predictor of gender than semantic ones, and the difference in prediction accuracy is very large. Overall, formal features are also significantly better than the combination of form and semantics, but the difference is very small and the results for this comparison are not entirely consistent across languages.

AB - This study conducts an experimental evaluation of two hypotheses about the contributions of formal and semantic features to the grammatical gender assignment of nouns. One of the hypotheses (Corbett and Fraser 2000) claims that semantic features dominate formal ones. The other hypothesis, formulated within the optimal gender assignment theory (Rice 2006), states that form and semantics contribute equally. Both hypotheses claim that the combination of formal and semantic features yields the most accurate gender identification. In this paper, we operationalize and test these hypotheses by trying to predict grammatical gender using only character-based embeddings (that capture only formal features), only context-based embeddings (that capture only semantic features) and the combination of both. We performed the experiment using data from three languages with different gender systems (French, German and Russian). Formal features are a significantly better predictor of gender than semantic ones, and the difference in prediction accuracy is very large. Overall, formal features are also significantly better than the combination of form and semantics, but the difference is very small and the results for this comparison are not entirely consistent across languages.

KW - Formal features

KW - Gender

KW - Neural networks

KW - Semantics

KW - Word embeddings

UR - http://www.scopus.com/inward/record.url?scp=85102994991&partnerID=8YFLogxK

U2 - 10.1515/lingvan-2020-0048

DO - 10.1515/lingvan-2020-0048

M3 - Journal article

AN - SCOPUS:85102994991

VL - 7

SP - 2020

EP - 2048

JO - Linguistics Vanguard

JF - Linguistics Vanguard

SN - 2199-174X

IS - 1

ER -