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

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...

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Vydáno v:PloS one Ročník 15; číslo 2; s. e0227930
Hlavní autoři: Kim, Sunhye, Park, Inchae, Yoon, Byungun
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 05.02.2020
Public Library of Science (PLoS)
Témata:
Algorithms
Bibliographic records
Biology and Life Sciences
Computer and Information Sciences
Data Mining
Databases as Topic
Documentation
Embedding
Engineering and Technology
Engineering schools
Forecasts and trends
Information processing
Internet of Things
Methods
Mining industry
Models, Theoretical
Natural language processing
Patents as Topic
Pattern recognition
Physical Sciences
Research and Analysis Methods
Researchers
Semantics
Sentences
Social Sciences
Structure (Literature)
Technical information
Technology
Unstructured data
Wireless communications
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí: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.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0227930