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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.
A Neuro Symbolic Ensemble Language Representation Using Syntactic and Semantic Information
http://doi.org/10.5626/JOK.2022.49.12.1124
With the advent of the giant language model, natural language processing has presented an opportunity to break through the wall. However, since super-large language models only use information extracted from the context, they chose to simply increase the size of the model or the amount of data to improve performance. This approach increases the resources consumed by the language model. In this paper, we propose a Neuro Symbolic Ensemble Language Representation (NSELR) that learns the semantic information of vocabulary together with a language model that uses only contextual information. This model uses the semantic constraint information of hypernym and verb-noun relation in the Korean WordNet (UWordNet) and additionally uses the semantic vectors of words. The NSELR was tested in four domains, and it showed better performance than the existing model in the machine reading comprehension. In addition, the speed of learning convergence was faster than that of the existing model, and when there was insufficient data in the application area, it showed better performance than the existing model.
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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