Thailand

Author affiliation information plays a key role in bibliometric analyses and is essential for evaluating studies. However, as author affiliation information has not been standardized, which leads to difficulties such as synonym ambiguity and incomplete data during automated processing. To address the challenge, this paper proposes an end-to-end entity recognition and disambiguation framework for identifying author affiliation from literature publications. For entity disambiguation, an algorithm combining word embedding and spatial embedding is presented considering that author affiliation texts often contain rich geographic information. The disambiguation algorithm utilizes the semantic information and geographic information, which effectively enhances entity recognition and disambiguation effect. In addition, the proposed framework facilitates the effective utilization of the extensive literature in the PubMed database for comprehensive bibliometric analysis. The experimental results verify the robustness and effectiveness of the algorithm.

ACL 2024

An end-to-end entity recognition and disambiguation framework for identifying Author Affiliation from literature publications

geographic information

named entity disambiguation

semantic similarity

named entity recognition

workshop paper

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

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Simulating Expert Discussions with Multi-agent for Enhanced Scientific Problem Solving

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Guiding Large Language Models via External Attention Prompting for Scientific Extreme Summarization

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CoSAEmb: Contrastive Section-aware Aspect Embeddings for Scientific Articles

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Controllable Citation Sentence Generation with Language Models

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An end-to-end entity recognition and disambiguation framework for identifying Author Affiliation from literature publications

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