Gender tagging of named entities using retrieval‐assisted multi‐context aggregation: An unsupervised approach

Sudeshna Das and Jiaul H. Paik

Journal of the Association for Information Science & Technology, 2023, vol. 74, issue 4, 461-475

Abstract: Inferring the gender of named entities present in a text has several practical applications in information sciences. Existing approaches toward name gender identification rely exclusively on using the gender distributions from labeled data. In the absence of such labeled data, these methods fail. In this article, we propose a two‐stage model that is able to infer the gender of names present in text without requiring explicit name‐gender labels. We use coreference resolution as the backbone for our proposed model. To aid coreference resolution where the existing contextual information does not suffice, we use a retrieval‐assisted context aggregation framework. We demonstrate that state‐of‐the‐art name gender inference is possible without supervision. Our proposed method matches or outperforms several supervised approaches and commercially used methods on five English language datasets from different domains.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jinfst:v:74:y:2023:i:4:p:461-475

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