SAO2Vec: Development of an algorithm for embedding the subject–action–object (SAO) structure using Doc2Vec

Sunhye Kim, Inchae Park and Byungun Yoon

PLOS ONE, 2020, vol. 15, issue 2, 1-26

Abstract: In natural-language processing, the subject–action–object (SAO) structure is used to convert unstructured textual data into structured textual data comprising subjects, actions, and objects. This structure is suitable for analyzing the key elements of technology, as well as the relationships between these elements. However, analysis using the existing SAO structure requires a substantial number of manual processes because this structure does not represent the context of the sentences. Thus, we introduce the concept of SAO2Vec, in which SAO is used to embed the vectors of sentences and documents, for use in text mining in the analysis of technical documents. First, the technical documents of interest are collected, and SAO structures are extracted from them. Then, sentence vectors are extracted through the Doc2Vec algorithm and are updated using word vectors in the SAO structure. Finally, SAO vectors are drawn using an updated sentence vector with the same SAO structure. In addition, document vectors are derived from the document’s SAO vectors. The results of an experiment in the Internet of things field indicate that the SAO2Vec method produces 3.1% better accuracy than the Doc2Vec method and 115.0% better accuracy than SAO frequency alone. This proves that the proposed SAO2Vec algorithm can be used to improve grouping and similarity analysis by including both the meanings and the contexts of technical elements.

Date: 2020
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0227930

DOI: 10.1371/journal.pone.0227930

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