Context-sensitive gender inference of named entities in text

Das, Sudeshna; Paik, Jiaul H.

doi:10.1016/j.ipm.2020.102423

Cited by 15 publications

(9 citation statements)

References 14 publications

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“…Several paid commercially available tools that use name databases are also mentioned in prior work (Das & Paik, 2021). Similar to GenderGuesser, these rely on name databases to assign gender labels.…”

Section: Related Workmentioning

confidence: 99%

“…Genderize is one such commercial API that is widely used (Karimi et al, 2016;Sebo, 2022;Wais, 2016). It uses a gender-labeled database of 114 million names from over 240 countries to infer the gender of a given name (Das & Paik, 2021). Müller et al, 2019 use name-gender frequencies coupled with demographic information such as nationality and date of birth to assign gender labels to names.…”

Section: Related Workmentioning

confidence: 99%

“…While these methods perform appreciably, they fail when a name previously not present in the database(s) is encountered (Blevins & Mullen, 2015). To counter this, a context-sensitive approach is used in the form of a sequence-to-sequence Transformer-based model (Das & Paik, 2021). However, their approach still requires reliable labeled data, which is expensive to obtain.…”

Section: Introductionmentioning

confidence: 99%

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

Das

Paik

2023

Asso for Info Science & Tech

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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 resolu-

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Das

Paik

2023

Asso for Info Science & Tech

Self Cite

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“…Therefore, integrating the self-assigned gender into the binary GI classifier could improve the accuracy of the results. The authors of [30] have introduced a supervised learning approach called the "Cascading Transformer" to identify the gender of named entities in text. Sequence labeling considering the context where the name appears outlines their solution for inferencing the gender; "Female", "Male", "Ambiguous", and "Other" represent the possible labels.…”

Section: Gender Identification Techniquesmentioning

confidence: 99%

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

et al. 2022

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Organizations analyze customers’ personal data to understand and model their behavior. Identifying customers’ gender is a significant factor in analyzing markets that help plan the promotional campaigns, determine target customers and provide relevant offers. Several techniques were developed to analyze different types of data, including text, image, speech, and biometrics, to identify the gender of the user. The method of synthesis of the profile name differs from one customer to another. Using numerical substitutions of specific letters, known as Leet language, impedes the gender identification task. Moreover, using acronyms, misspellings, and adjacent names impose additional challenges. Towards this goal, this work uses the customers’ profile names associated with submitted reviews to recognize the customers’ gender. First, we create datasets of profile names extracted from the customers’ reviews. Secondly, we introduce a dynamic pruned n-gram model for identifying the gender of the user. It starts with data segmentation to handle adjacent parts, followed by data conversion and cleaning to fix the use of Leet language. Feature selection through a dynamic pruned n-gram model is the next step with the recurrent misspelling correction using fuzzy matching. We evaluate the proposed approach on the real data collected from active web resources. The obtained results demonstrate its validity and reliability.

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“…The results of our tests show that the local context is very important for recognition and categorization of the entity. See, also, recently published paper [17], where importance of the local context, in the problem of gender recognition of named entities, was considered.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Feature Extraction for Polish Language Named Entities Recognition in Intelligent Office Assistant

Denisiuk¹,

Ganzha²,

Wasielewska-Michniewska³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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The purpose of this contribution is to present a feature extractor that was designed as a part of a Named Entity Recognition (NER) system, which is to be used in a Robotic Process Automation application with a self-learning ability. The NER system has a screen of the user interface as its input, and tries to recognize and categorize all the named entities that can be located within this screen. The set of features that can be extracted from the input, is discussed in the article. The local context features appear to be very important in the considered problem. Experiments show that the entities are recognized with a rate that is satisfactory from the business perspective.

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Context-sensitive gender inference of named entities in text

Cited by 15 publications

References 14 publications

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

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

Using Dynamic Pruned N-Gram Model for Identifying the Gender of the User

Feature Extraction for Polish Language Named Entities Recognition in Intelligent Office Assistant

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