• Journal of the Korea Society of Computer and Information
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• v.23 no.5
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• pp.1-7
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• 2018

Reference string recognition is to extract individual reference strings from a reference section of an academic article, which consists of a sequence of reference lines. This task has been attacked by heuristic-based, clustering-based, classification-based approaches, exploiting lexical and layout characteristics of reference lines. Most classification-based methods have used sequence labeling to assign labels to either a sequence of tokens within reference lines, or a sequence of reference lines. Unlike the previous token-level sequence labeling approach, this study attempts to assign different labels to the beginning, intermediate and terminating tokens of a reference string. After that, post-processing is applied to identify reference strings by predicting their beginning and/or terminating tokens. Experimental evaluation using English and German reference string recognition datasets shows that the proposed method obtains above 94% in the macro-averaged F1.

• Proceedings of the KSMRM Conference
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• 2001.11a
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• pp.144-144
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• 2001

목적： 심장관련 질환과 연관성이 있는 심장의 움직임을 관찰, 확인해야 하는 필요성이 날로 증가함에 따라 심장의 움직임을 관찰할 수 있도록 심장 영상 내에 일정한 간격의 saturation된 선(grid)을 만들어, 시간에 따른 grid 된 영상을 얻어 grid 의 변형에 따른 심장의 움직임을 확인하여 본다.

• Proceedings of the Korea Information Processing Society Conference
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• 2018.10a
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• pp.613-616
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• 2018

Sequence tagging is an important task in Natural Language Processing (NLP), in which the Named Entity Recognition (NER) is the key issue. So far the most widely adopted model for NER in NLP is that of combining the neural network of bidirectional long short-term memory (BiLSTM) and the statistical sequence prediction method of Conditional Random Field (CRF). In this work, we improve the prediction accuracy of the BiLSTM by supporting an aligned word representation mechanism. We have performed experiments on multilingual (English, Spanish and Dutch) datasets and confirmed that our proposed model outperformed the existing state-of-the-art models.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.544-547
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• 2021

한국어의 품사 태깅 문제는 입력 어절의 형태소 분석 후보들로부터 통계적으로 적절한 품사 태그를 가지는 후보들을 찾는 방식으로 해결하여 왔다. 어절을 형태소 단위로 분리하고 품사를 부착하는 기존의 방식은 품사태그 정보를 딥러닝 feature로 사용할 때 문장의 의미를 이해하는데 복잡도를 증가시키는 요인이 된다. 본 연구에서는 품사 태깅 문제를 단순화 하여 한 어절을 Head와 Tail이라는 두 가지 유형의 형태소 토큰으로 분리하여 Head와 Tail에 대해 품사를 부착한다. Head-Tail 품사 태깅 방법을 Sequence-to-Sequence 문제로 정의하여 Transformer를 이용한 Head-Tail 품사 태거를 설계하고 구현하였다. 학습데이터로는 KCC150 말뭉치의 품사 태깅 말뭉치 중에서 788만 문장을 사용하고, 실험 데이터로는 10만 문장을 사용하였다. 실험 결과로 토큰 정확도는 99.75%, 태그 정확도는 99.39%, 토큰-태그 정확도는 99.31%로 나타났다.

• Journal of KIISE:Software and Applications
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• v.41 no.7
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• pp.523-532
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• 2014

In this paper, we propose a lattice-based discriminative approach for Korean morphological analysis and POS tagging. In our approach, for an input sentence, a morpheme lattice is first created from a lexicon where each node corresponds to a morpheme in the lexicon and each edge is formed between two consecutive morphemes. A candidate result of morphological analysis is then represented as a path in the morpheme lattice which is defined as the sequence of edges, starting in the initial state and ending with the final state. In this setting, the morphological analysis is simply considered as the process of finding the best path among all possible paths. Experiment results show that the proposed lattice-based method outperforms the first-order linear-chain CRF.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.253-266
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• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• 한국생물공학회:학술대회논문집
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• 2005.10a
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• pp.301-304
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• 2005

S-Adenosyl-L-Methionine(SAM) has an important role for DNA methylation and cell signaling. SAM was synthesized from methionine and ATP by SAM synthetase and play an pivotal function in the primary and secondary metabolism of cells. Recent studies have revealed in the effect of SAM in case of morphological differentiation in both eukaryotes and prokaryotes. We isolated SAM gene from Saccharomyces cerevisiae and cloned it into expression vector for E. coli respectively. An 1.15 kb SAM-s gene fragment was isolated by Low-strigency PCR using ORF primer. By the analysed primary sequence deduced from DNA sequence, this gene included conserved domains similar with other well-known SAM synthetase. First of all, SAM synthetase gene cloned pGEM-T vector and subcloned into histidine tagging system to purify the expressed protein using metal chelating resin. Typical characteristic analysis of this enzyme is underway.

• Annual Conference on Human and Language Technology
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• 2017.10a
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• pp.203-208
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• 2017

본 논문은 한글 자모 조합 임베딩을 이용하여 오타에 강건한 한국어 품사 태깅 시스템을 구축하는 방법에 대해 기술한다. 최근 딥 러닝 연구가 활발히 진행되면서 자질을 직접 추출해야 하는 기존의 기계학습 방법이 아닌, 스스로 자질을 찾아서 학습하는 딥 러닝 모델을 이용한 연구가 늘어나고 있다. 본 논문에서는 다양한 딥 러닝 모델 중에서 sequence labeling에 강점을 갖고 있는 bidirectional LSTM CRFs 모델을 사용하였다. 한국어 품사 태깅 문제에서 일반적으로 사용되는 음절 임베딩은 약간의 오타에도 품사 태깅 성능이 크게 하락하는 한계가 있었다. 따라서 이를 개선하기 위해 본 논문에서는 한글 자모 임베딩 값을 조합시킨 음절 임베딩 방식을 제안하였다. 강제로 오타를 발생시킨 테스트 집합에서 실험한 결과, 자모 조합 임베딩 기법이 word2vec 음절 임베딩 방식에 비해 형태소 분할은 0.9%, 품사 태깅은 3.5% 우수한 성능을 기록하였다.

• 한국어정보학회:학술대회논문집
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• 2017.10a
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• pp.203-208
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• 2017

본 논문은 한글 자모 조합 임베딩을 이용하여 오타에 강건한 한국어 품사 태깅 시스템을 구축하는 방법에 대해 기술한다. 최근 딥 러닝 연구가 활발히 진행되면서 자질을 직접 추출해야 하는 기존의 기계학습 방법이 아닌, 스스로 자질을 찾아서 학습하는 딥 러닝 모델을 이용한 연구가 늘어나고 있다. 본 논문에서는 다양한 딥 러닝 모델 중에서 sequence labeling에 강점을 갖고 있는 bidirectional LSTM CRFs 모델을 사용하였다. 한국어 품사 태깅 문제에서 일반적으로 사용되는 음절 임베딩은 약간의 오타에도 품사 태깅 성능이 크게 하락하는 한계가 있었다. 따라서 이를 개선하기 위해 본 논문에서는 한글 자모 임베딩 값을 조합시킨 음절 임베딩 방식을 제안하였다. 강제로 오타를 발생시킨 테스트 집합에서 실험한 결과, 자모 조합 임베딩 기법이 word2vec 음절 임베딩 방식에 비해 형태소 분할은 0.9%, 품사 태깅은 3.5% 우수한 성능을 기록하였다.

• Proceedings of the Botanical Society of Korea Conference
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• 1987.07a
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• pp.323-347
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• 1987

The R locus of maize in one of several genes that regulate the anthocyanin pigments throughout the body of the plant and seed. The R gene product may regulate pigment deposition by controlling the expression of the flavonoid biosynthetic gene pathway in a tissue-specific manner. To understand the basis for tissue specific regulation and allelic variation at R, the molecular study has been done by cloning a portion of the R complex by transposon tagging with Ac. R specific probe were cloned from the R-nj mutant induced by Ac insertion mutagenesis. From southern analysis of R-r complex using the R-nj probe, the structure of R-r was proposed that R-r containes the three elements, (P)(Q)(S). These elements may organize as the inversion triplication model which (S) sequence was inverted in relation to (P) and (Q). The R-sc derivated from R-mb or R-nj was cloned with R-nj probe, and molecular genetical data showed that R-sc containes tissue specific and tissue nonspecific area, and the sequencing of R-sc are progressed now.