• 한국산학기술학회논문지
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• 제7권2호
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• pp.182-187
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• 2006

본 논문은 통계 기반 접근 방식인 HMM(Hidden Markov model)과 생물학의 개체명에 관한 온톨로지 정보를 이용한 생물학 문서에서의 개체명(named entity) 경계 인식 방법을 제안한다. 제안하는 방법은 31개의 자질 정보를 이용한 평탄화 기법을 사용하며 생물학 개체명의 계층 정보를 이용하여 HMM의 자료 부족 문제를 완화시킬 수 있도록 하였다. 개체명 경계 인식의 학습과 실험을 위하여 GENIA 코퍼스 ver 2.1을 사용하였으며 개체명 경계 인식 실험을 수행한 결과 모든 부류를 사용한 경우보다 정확도 및 실행 속도가 개선됨을 확인하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권2호
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• pp.111-120
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• 2015

Protein named entity identification is one of the most essential and fundamental predecessor for extracting information about protein-protein interactions from biomedical literature. In this paper, we explore the use of abstracts of biomedical literature in MEDLINE for protein name identification and present the results of the conducted experiments. We present a robust and effective approach to classify biomedical named entities into protein and non-protein classes, based on a rich set of features: orthographic, keyword, morphological and newly introduced Protein-Score features. Our procedure shows significant performance in the experiments on GENIA corpus using Random Forest, achieving the highest values of precision 92.7%, recall 91.7%, and F-measure 92.2% for protein identification, while reducing the training and testing time significantly.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.3-3
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• 2003

With biomedical literature expanding so rapidly, there is an urgent need to discover and organize knowledge extracted from texts. Although factual databases contain crucial information the overwhelming amount of new knowledge remains in textual form (e.g. MEDLINE). In addition, new terms are constantly coined as the relationships linking new genes, drugs, proteins etc. As the size of biomedical literature is expanding, more systems are applying a variety of methods to automate the process of knowledge acquisition and management. In my talk, I focus on the project, GENIA, of our group at the University of Tokyo, the objective of which is to construct an information extraction system of protein - protein interaction from abstracts of MEDLINE. The talk includes (1) Techniques we use fDr named entity recognition (1-a) SOHMM (Self-organized HMM) (1-b) Maximum Entropy Model (1-c) Lexicon-based Recognizer (2) Treatment of term variants and acronym finders (3) Event extraction using a full parser (4) Linguistic resources for text mining (GENIA corpus) (4-a) Semantic Tags (4-b) Structural Annotations (4-c) Co-reference tags (4-d) GENIA ontology I will also talk about possible extension of our work that links the findings of molecular biology with clinical findings, and claim that textual based or conceptual based biology would be a viable alternative to system biology that tends to emphasize the role of simulation models in bioinformatics.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.95-100
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• 2003

In this paper, we propose solutions to resolve the problem of many spelling variants and the problem of lack of annotated corpus for training, which are two among the main difficulties in named entity recognition in biomedical domain. To resolve the problem of spotting valiants, we propose a use of edit-distance as a feature for SVM. And we propose a use of virtual examples to automatically expand the annotated corpus to resolve the lack-of-corpus problem. Using virtual examples, the annotated corpus can be extended in a fast, efficient and easy way. The experimental results show that the introduction of edit-distance produces some improvements in protein name recognition performance. And the model, which is trained with the corpus expanded by virtual examples, outperforms the model trained with the original corpus. According to the proposed methods, we finally achieve the performance 75.80 in F-measure(71.89% in precision,80.15% in recall) in the experiment of protein name recognition on GENIA corpus (ver.3.0).

• Genomics & Informatics
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• 제18권2호
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• pp.13.1-13.6
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• 2020

The prototype version of the full-text corpus of Genomics & Informatics has recently been archived in a GitHub repository. The full-text publications of volumes 10 through 17 are also directly downloadable from PubMed Central (PMC) as XML files. During the Biomedical Linked Annotation Hackathon 6 (BLAH6), we experimented with converting, annotating, and updating 301 PMC full-text articles of Genomics & Informatics using PubAnnotation, a system that provides a convenient way to add PMC publications based on PMCID. Thus, this review aims to provide a tutorial overview of practicing the iterative task of named entity recognition with the PubAnnotation/PubDictionaries/TextAE ecosystem. We also describe developing a conversion tool between the Genia tagger output and the JSON format of PubAnnotation during the hackathon.