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Rules-based Korean Dependency Parsing Using Sentence Pattern Information
Sung-Tae Kim, Minho Kim, Hyuna Kim, Hyuk-Chul Kwon
http://doi.org/10.5626/JOK.2020.47.5.488
The parser proposed in this paper is a wide range dependency parser that facilitates dependency r-elations to all the possible candidates appearing in sentences. Output a parse tree of all candidates appearing in a sentence in which neutrality can occur, and use the rules to advance the ranking. Use the agenda mechanism to form a dominance-dependency relationship with the graph analysis method and create a candidate tree from the input sentence through the four stages of the analysis process. Additionally, for the proper use of sentence pattern information corpus, we implemented rules and algorithms that overcome the limitations of previous studies and enhanced the ranking of candidate parse trees using the sentence pattern information. As well as difficulty in ranking the [noun - determiner] strengthened the ranking using sentence pattern information about qualities. As a result, the UAS (unlabeled attachment score) of the parse tree top-rank improved by 0.74%p, and the average correct ranking of the candidate tree improved by 28.1%. Additionally, the highest performance was UAS 94.02%.
Automatic Segmentation of Femoral Cartilage in Knee MR Images using Multi-atlas-based Locally-weighted Voting
Hyeun A Kim, Hyeonjin Kim, Han Sang Lee, Helen Hong
In this paper, we propose an automated segmentation method of femoral cartilage in knee MR images using multi-atlas-based locally-weighted voting. The proposed method involves two steps. First, to utilize the shape information to show that the femoral cartilage is attached to a femur, the femur is segmented via volume and object-based locally-weighted voting and narrow-band region growing. Second, the object-based affine transformation of the femur is applied to the registration of femoral cartilage, and the femoral cartilage is segmented via multi-atlas shape-based locally-weighted voting. To evaluate the performance of the proposed method, we compared the segmentation results of majority voting method, intensity-based locally-weighted voting method, and the proposed method with manual segmentation results defined by expert. In our experimental results, the newly proposed method avoids a leakage into the neighboring regions having similar intensity of femoral cartilage, and shows improved segmentation accuracy.
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