Abstract
Named entity extraction is a key subtask of Information Extraction (IE), and also an important component for many Natural Language Processing (NLP) and Information Retrieval (IR) tasks. This paper proposes a weakly-supervised named entity extraction method by learning word representations on web-scale corpus. The highlights of our method include: (1) Word representations could be trained on either web documents or query logs; (2) Finding correct named entities is guided by a small set of seed entities, without any need for domain knowledge or human labor, allowing for the acquisition of named entities of any domain. Extensive experiments have been conducted to verify the effectiveness and efficiency of our method, comparing with the state-of-art approaches.
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Deng, K., Wang, D., Liu, J. (2017). Weakly-Supervised Named Entity Extraction Using Word Representations. In: Bao, Z., Trajcevski, G., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10179. Springer, Cham. https://doi.org/10.1007/978-3-319-55705-2_15
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DOI: https://doi.org/10.1007/978-3-319-55705-2_15
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