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Unified Methodology of Multiple POS Taggers for Large-scale Korean Linguistic GS Set Construction
Tae-Young Kim, Pum-Mo Ryu, Hansaem Kim, Hyo-Jung Oh
http://doi.org/10.5626/JOK.2020.47.6.596
In recent years, there has been national support for constructing, sharing, and spreading a large-scale Korean linguistic GS set for Korean information processing. As part of the corpus construction project, this study proposes the methodology for constructing the Korean linguistic GS set using various Korean language analysis modules developed in Korea. To build a large-scale training set, we referred to automatic tagged candidate answers from the N-modules. We then minimized manual effort by classifying the error types from the candidate responses and semi- automatically correcting the major error types. In this study, we normalized results of the morphological analysis and constructed a large-scale Korean linguistic GS set based on the unified format U-POS. As a result of this study, 348,229 sentences, a total of 9,455,930 words, were constructed as the Korean linguistic GS set. This can be practically applied later as a basic training resource for Korean information processing.
Pattern Extraction from Lifelog Based on Semantic Network Structure Using Petri-Net
http://doi.org/10.5626/JOK.2020.47.6.553
Recently, with the spread of smart devices, the user’s lifelog data is automatically stored through various types of sensors. But the lifelog collected from smart devices records heterogeneous information from different sensors. In addition, since the user"s life patterns are determined by different judgment cycles, it is difficult to express them in a simple rule-based system. Therefore, in order to extract and provide useful life patterns for users from the lifelog, it is necessary to express the relationship of numerous dynamic elements. In this paper, we propose a method to automatically extract user life patterns using Petri-nets from the lifelog represented by the semantic network. Petri-net reduces the uncertainty in smart device sensor data and increases the diversity of life patterns. The proposed life pattern extraction method is structured by the semantic network to represent the semantic relationship of heterogeneously collected user lifelog. Also, the Petri-net graph automatically determines the lifelog and then extracts individual sleep and eating patterns.
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