Bootstrapping public entities. Domain-specific NER for public speakers

IF 6.3 1区文学 Q1 COMMUNICATION

Communication Methods and Measures Pub Date : 2024-08-13 DOI:10.1080/19312458.2024.2388098

Sami Nenno

引用次数: 0

Abstract

Named Entity Recognition (NER) is a supervised machine learning task that finds various applications in automated content analysis, as the identification of entities is vital for understanding publ...

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引导公共实体。针对公共发言人的特定领域 NER

命名实体识别（NER）是一项有监督的机器学习任务，在自动内容分析中有着多种应用，因为实体的识别对于理解公共内容至关重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Communication Methods and Measures COMMUNICATION-

CiteScore

21.10

自引率

1.80%

发文量

期刊介绍： Communication Methods and Measures aims to achieve several goals in the field of communication research. Firstly, it aims to bring attention to and showcase developments in both qualitative and quantitative research methodologies to communication scholars. This journal serves as a platform for researchers across the field to discuss and disseminate methodological tools and approaches. Additionally, Communication Methods and Measures seeks to improve research design and analysis practices by offering suggestions for improvement. It aims to introduce new methods of measurement that are valuable to communication scientists or enhance existing methods. The journal encourages submissions that focus on methods for enhancing research design and theory testing, employing both quantitative and qualitative approaches. Furthermore, the journal is open to articles devoted to exploring the epistemological aspects relevant to communication research methodologies. It welcomes well-written manuscripts that demonstrate the use of methods and articles that highlight the advantages of lesser-known or newer methods over those traditionally used in communication. In summary, Communication Methods and Measures strives to advance the field of communication research by showcasing and discussing innovative methodologies, improving research practices, and introducing new measurement methods.

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