Gender bias, a sociological issue, has attracted the attention of scholars working on natural language processing (NLP) in recent years. It is confirmed that some NLP techniques like word embedding could capture gender bias in natural language. Here, we investigate gender bias in Chinese word embeddings. Gender bias tests originally designed for English are adapted and applied to Chinese word embeddings trained with three different embedding models. After verifying the efficiency of the adapted tests, the changes of gender bias throughout several time periods are tracked and analysed. Our results validate the feasibility of bias test adaptation and confirm that word embedding trained by a model with character-level information captures more gender bias in general. Moreover, we build a possible framework for diachronic research of gender bias.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-462160 |
| Date | January 2021 |
| Creators | Jiao, Meichun |
| Publisher | Uppsala universitet, Institutionen för lingvistik och filologi |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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