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CRF based Named Entity Recognition Using a Korean Lexical Semantic Network
http://doi.org/10.5626/JOK.2021.48.5.556
Named Entity Recognition(NER) is the process of classifying words with unique meanings that often appear as OOV within sentence into categories of predefined entities. Recently, many researches have been conducted using deep learning to synthesize the words’ embedding via Convolution Neural Network(CNN), Long Short-Term Memory(LSTM) networks or training language models. However, models using these deep learning network or language model require high performance computing power and have low practicality due to slow speed. For practicality, this paper proposes Conditional Random Field(CRF) based NER model using Korean lexical network(UWordMap). By using hypernym, dependence and case particle information as training feature, our model showed 90.54% point of accuracy, 1,461 sentences/sec processing speed.
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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