• The Journal of the Korea institute of electronic communication sciences
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• v.19 no.4
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• pp.701-708
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• 2024

The advent of transformer architectures in deep learning has been a major breakthrough in natural language processing research. Object name recognition is a branch of natural language processing and is an important research area for tasks such as information retrieval. It is also important in the biomedical field, but the lack of Korean biomedical corpora for training has limited the development of Korean clinical research using AI. In this study, we built a new biomedical corpus for Korean biomedical entity name recognition and selected language models pre-trained on a large Korean corpus for transfer learning. We compared the name recognition performance of the selected language models by F1-score and the recognition rate by tag, and analyzed the errors. In terms of recognition performance, KlueRoBERTa showed relatively good performance. The error analysis of the tagging process shows that the recognition performance of Disease is excellent, but Body and Treatment are relatively low. This is due to over-segmentation and under-segmentation that fails to properly categorize entity names based on context, and it will be necessary to build a more precise morphological analyzer and a rich lexicon to compensate for the incorrect tagging.

• Journal of Korean Library and Information Science Society
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• v.47 no.4
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• pp.289-307
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• 2016

This paper introduces a software system and process model for constructing domain-specific relation extraction datasets semi-automatically. The system uses a set of terms such as genes, proteins diseases and so forth as inputs and then by exploiting massive biological interaction database, generates a set of term pairs which are utilized as queries for retrieving sentences containing the pairs from scientific databases. To assess the usefulness of the proposed system, this paper applies it into constructing a genic interaction dataset related to Alzheimer's disease domain, which extracts 3,510 interaction-related sentences by using 140 gene names in the area. In conclusion, the resulting outputs of the case study performed in this paper indicate the fact that the system and process could highly boost the efficiency of the dataset construction in various subfields of biomedical research.

• Journal of Information Management
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• v.40 no.4
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• pp.1-22
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• 2009

Large-scaled information extraction plays an important role in advanced information retrieval as well as question answering and summarization. Information extraction can be defined as a process of converting unstructured documents into formalized, tabular information, which consists of named-entity recognition, terminology extraction, coreference resolution and relation extraction. Since all the elementary technologies have been studied independently so far, it is not trivial to integrate all the necessary processes of information extraction due to the diversity of their input/output formation approaches and operating environments. As a result, it is difficult to handle scientific documents to extract both named-entities and technical terms at once. In this study, we define scientific as a set of 10 types of named entities and technical terminologies in a biomedical domain. in order to automatically extract these entities from scientific documents at once, we develop a framework for scientific core entity extraction which embraces all the pivotal language processors, named-entity recognizer, co-reference resolver and terminology extractor. Each module of the integrated system has been evaluated with various corpus as well as KEEC 2009. The system will be utilized for various information service areas such as information retrieval, question-answering(Q&A), document indexing, dictionary construction, and so on.