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Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases

Study on evaluating the semantic relations represented by word embeddings using external knowledge bases.

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Evaluating semantic relations in neural word embeddings with biomedical and general domain knowledge bases

By Zhiwei Chen, Zhe He, Xiuwen Liu, Jiang BianBMC Medical Informatics and Decision Making
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This paper proposes a novel approach to evaluate the semantic relations in word embeddings using Wikipedia, WordNet, and UMLS. The authors trained multiple word embeddings using health-related articles and evaluated their performance in analogy and semantic relation term retrieval tasks.

The study found that domain-specific corpora improve the performance of word embeddings for specific text mining tasks.

Abstract

This paper proposes a novel approach to evaluate the semantic relations in word embeddings using Wikipedia, WordNet, and UMLS. The authors trained multiple word embeddings using health-related articles and evaluated their performance in analogy and semantic relation term retrieval tasks. The study found that domain-specific corpora improve the performance of word embeddings for specific text mining tasks.

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word embeddingssemantic relationsneural networkstext miningdomain-specific corporaKnowledge GraphsStructured ContentContent EngineeringAI Agents
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