Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
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¿µ¹®Á¦¸ñ(English Title) |
A Comparative Study on the Performance of Named Entity Recognition in Materials and Chemistry Fields through Multiple Embedding Combination Based on a Pre-trained Neural Network Language Model |
ÀúÀÚ(Author) |
À̸íÈÆ
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ÀüÈ«¿ì
Myunghoon Lee
Hyeonho Shin
Hong-Woo Chun
ÀÌÀç¹Î ÇÏÅÂÇö ÃÖ¼ºÇÊ
Jae-Min Lee
Taehyun Ha
Sung-Pil Choi
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¿ø¹®¼ö·Ïó(Citation) |
VOL 48 NO. 06 PP. 0696 ~ 0706 (2021. 06) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English Abstract) |
Recently, with the rapid development of materials and chemistry fields, the academic literature has increased exponentially. Accordingly, studies are being conducted to extract meaningful information from the existing accumulated data, and Named Entity Recognition (NER) is being utilized as one of the methodologies. NER in materials and chemistry fields is a task of extracting standardized entities such as materials, material property information, and experimental conditions from academic literature and classifying types of the entities. In this paper, we studied the NER in materials and chemistry fields using a combination of embedding and a Bi-direction LSTM-CRF model with an existing published language model without pre-training a neural network language model. As a result, we found the best performing embedding combinations and analyzed their performance. Additionally, the pre-trained language model was used as a NER model to compare performance through fine-tuning. The process showed that the use of a public pre-trained language model for embedding combinations could derive meaningful results in NER in the materials and chemistry fields
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Å°¿öµå(Keyword) |
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ÀÚ¿¬¾î ó¸®
ÀÚ¿¬¾î ÀÌÇØ
°³Ã¼¸í ÀνÄ
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information retrieval
natural language processing
natural language understanding
named entity recognition
word embedding
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