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A Model for Topic Classification and Extraction of Sentimental Expression using a Lexical Semantic Network
JiEun Park, JuSang Lee, JoonChoul Shin, ChoelYoung Ock
http://doi.org/10.5626/JOK.2023.50.8.700
The majority of the previous sentiment analysis studies classified a single sentence or document into only a single sentiment. However, more than one sentiment can exist in one sentence. In this paper, we propose a method that extracts sentimental expression for word units. The structure of the proposed model is a UBERT model that uses morphologically analyzed sentences as input and adds layers to predict topic classification and sentimental expression. The proposed model uses topic feature of a sentence predicted by topic dictionary. The topic dictionary is built at the beginning of machine learning. The learning module collects topic words from a training corpus and expands them using the lexical semantic network. The evaluation is performed with the word unit F1-Score. The proposed model achieves an F1-Score of 58.19%, an improvement of 0.97% point over the baseline.
Transition-based Korean Dependency Analysis System Using Semantic Abstraction
ChungSeon Jeong, JoonChoul Shin, JuSang Lee, CheolYoung Ock
http://doi.org/10.5626/JOK.2019.46.11.1174
The existing learning-based dependency studies used as a learning features by combining the lemma and the part-of-speech tag. The part-of-speech tag is suitable for use as a feature due to its high recall, but there is a limit to increase the accuracy of analysis of dependency by using only the part-of-speech tag. In case of lemma, when the lemma is recalled, it shows high dependency accuracy, but it shows low recall compared to the part-of-speech tag. In this paper, we propose a transition-based dependency analysis method that uses abstractions of nouns as a feature by using lexical semantic network (UWordMap) in order to increase the recall rate of lemma. When the semantic abstraction of lemmas is used as a feature, the accuracy of dependency analysis is increased by up to 7.55% compared to the case of using only the lemma. In case of using word(eojeol), morphological and syllable unit features including semantic abstraction features, 90.75% dependence analysis accuracy was shown. With the learning speed of 562 sentences per second and the speed of 631 dependency analysis per second, the proposed method can be used practically.
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