A multi-granularity knowledge association model of geological text based on hypernetwork

Zhuang, Can; Li, Wenjia; Xie, Zhen; Wu, Liang

doi:10.1007/s12145-020-00534-w

Cited by 10 publications

(1 citation statement)

References 53 publications

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“…Modeling semantic similarity between textual components is an important NLP research problem and has gained significant attention by researchers from a variety of scientific domains, such as biology, geology, hydrology, and urban planning (Pesquita et al, 2009; Zhang et al, 2021; Zhuang et al,2020; Zhuang et al, 2021). The goal is to determine to what degree two textual components are semantically similar in terms of their content.…”

Section: Literature Reviewmentioning

confidence: 99%

Semantic similarity measure of natural language text through machine learning and a keyword‐aware cross‐encoder‐ranking summarizer—A case study using UCGIS GIS&T body of knowledge

Tian

Wang

et al. 2023

Transactions in GIS

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Initiated by the University Consortium of GeographicInformation Science (UCGIS), the GIS&T Body of Knowledge (BoK) is a community-driven endeavor to define, develop, and document geospatial topics related to geographic information science and technologies (GIS&T). In recent years, GIS&T BoK has undergone rigorous development in terms of its topic re-organization and content updating, resulting in a new digital version of the project. While the BoK topics provide useful materials for researchers and students to learn about GIS, the semantic relationships among the topics, such as semantic similarity, should also be identified so that a better and automated topic navigation can be achieved. Currently, the related topics are either defined manually by editors or authors, which may result in an incomplete assessment of topic relationships. To address this challenge, our research evaluates the effectiveness of multiple natural language processing (NLP) techniques in extracting semantics from text, including both deep neural networks and traditional machine learning approaches.Besides, a novel text summarization-KACERS (Keyword-Aware Cross-Encoder-Ranking Summarizer)-is proposed to generate a semantic summary of scientific publications.

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