• KIPS Transactions on Software and Data Engineering
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• v.3 no.7
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• pp.285-292
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• 2014

A named entity recognition method is used to improve the performance of information retrieval systems, question answering systems, machine translation systems and so on. The targets of the named entity recognition are usually PLOs (persons, locations and organizations). They are usually proper nouns or unregistered words, and traditional named entity recognizers use these characteristics to find out named entity candidates. The titles of books, movies and TV programs have different characteristics than PLO entities. They are sometimes multiple phrases, one sentence, or special characters. This makes it difficult to find the named entity candidates. In this paper we propose a method to quickly extract title named entities from news articles and automatically build a named entity dictionary for the titles. For the candidates identification, the word phrases enclosed with special symbols in a sentence are firstly extracted, and then verified by the SVM with using feature words and their distances. For the classification of the extracted title candidates, SVM is used with the mutual information of word contexts.

• The Journal of Korean Association of Computer Education
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• v.18 no.6
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• pp.81-88
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• 2015

Along with advent of Web 3.0 era and advanced technologies of IoT(Internet of Things), massive amounts of information are generated. Reflecting this trend, this paper developed a semi-automatic construction tool for named entity dictionary based on active learning. Our proposed method chose error candidates to verify among the preliminary results using initial trained model and re-trained the model for correctly labeled data by user. We adopt active learning approach for minimizing human effort utilized metadata features of Wikipedia. Based on experimental results using our tool, we show that 68.6% errors were automatically corrected.

• Journal of Digital Convergence
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• v.13 no.5
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• pp.187-194
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• 2015

Various linguistic knowledge resources are required in order that machine can understand diverse variation in natural languages. This paper aims to devise an automatic construction method of linguistic resources by reflecting characteristics of online contents toward continuous expansion. Especially we focused to build NE(Named-Entity) dictionary because the applicability of NEs is very high in linguistic analysis processes. Based on the investigation on Korean Wikipedia, we suggested an efficient construction method of NE dictionary using the syntactic patterns and structural features such as metadatas.

• Journal of KIISE
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• v.42 no.11
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• pp.1397-1403
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• 2015

A named entity(NE) dictionary is an important resource for the performance of NE recognition. However, it is not easy to construct a NE dictionary manually since human annotation is time consuming and labor-intensive. To save construction time and reduce human labor, we propose a semi-automatic system for the construction of a NE dictionary. The proposed system constructs a pseudo-document with Wiki-categories per NE class by using an active learning technique. Then, it calculates similarities between Wiki entries and pseudo-documents using the BM25 model, a well-known information retrieval model. Finally, it classifies each Wiki entry into NE classes based on similarities. In experiments with three different types of NE class sets, the proposed system showed high performance(macro-average F1-score of 0.9028 and micro-average F1-score 0.9554).

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.492-496
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• 2010

Wikipedia as an open encyclopedia contains immense human knowledge written by thousands of volunteer editors and its reliability is also high. In this paper, we propose to automatically construct a Korean named entity dictionary using the several features of the Wikipedia. Firstly, we generate class hierarchies using the class information from each article of Wikipedia. Secondly, the titles of each article are mapped to our class hierarchies, and then we calculate the entropy value of the root node in each class hierarchy. Finally, we construct named entity dictionary with high performance by removing the class hierarchies which have a higher entropy value than threshold. Our experiment results achieved overall F1-measure of 81.12% (precision : 83.94%, recall : 78.48%).

• Annual Conference on Human and Language Technology
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• 2013.10a
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• pp.82-85
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• 2013

개체명 인식기에 대한 연구에서 개체명 사전은 필수적으로 필요하다. 그러나 공개된 개체명 사전은 거의 없기 때문에, 본 논문에서는 디비피디아의 데이터로부터 개체명을 효과적으로 추출하여 자동으로 구축할 수 있는 방법을 제안한다. 제안 방법은 엔트리의 '이름'과 '분류' 정보를 사용한다. 엔트리의 '이름'은 개체명으로 사용하고, 엔트리의 '분류'는 각 개체명 클래스와의 상호정보량을 계산하여 엔트리와 개체명 클래스 사이의 점수를 계산한다. 이렇게 계산된 점수를 이용하여 개체명과 개체명 클래스를 매핑한다. 그 결과 76.7%의 평균 정확률을 보였다.

• 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.

• Journal of KIISE
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• v.44 no.8
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• pp.813-821
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• 2017

Entity Linking find the meaning of an entity mention, which indicate the entity using different expressions, in a user's query by linking the entity mention and the entity in the knowledge base. This task has four challenges, including the difficult knowledge base construction problem, multiple presentation of the entity mention, ambiguity of entity linking, and NIL entity recognition. In this paper, we first construct the entity name dictionary based on Wikipedia to build a knowledge base and solve the multiple presentation problem. We then propose various methods for NIL entity recognition and solve the ambiguity of entity linking by training the support vector machine based on several features, including the similarity of the context, semantic relevance, clue word score, named entity type similarity of the mansion, entity name matching score, and object popularity score. We sequentially use the proposed two methods based on the constructed knowledge base, to obtain the good performance in the entity linking. In the result of the experiment, our system achieved 83.66% and 90.81% F1 score, which is the performance of the NIL entity recognition to solve the ambiguity of the entity linking.

• Proceedings of the Korea Information Processing Society Conference
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• 2003.11b
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• pp.853-856
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• 2003

바이오 텍스트 마이닝을 위한 정보 추출의 첫 단계는 생물학적 문헌으로부터의 유전자, 단백질, 세포조직 등과 같은 생물학적 개체명의 인식이다. 생물학적 개체명의 명명법상 특징이 매우 다양하고 저자의 개성에 의해 쉽게 좌우되어 단순히 규칙이나 학습 방법 만으로는 쉽게 개체명들을 인식할 수 없다. 또한, 생물학 관련 문헌에 나오는 가능한 모든 개체명과 이들의 모든 변형을 수록하는 것은 현실적으로 불가능하므로 이를 해결하기 위해 이미 알려진 개체명에 대해서 기본적으로 사전을 탐색하고 알려지지 않은 용어들을 규칙과 통계 기반 방법을 통하여 인식하는 것이 효과적이다. 그러나 만족할 만한 수준의 양질의 사전을 구축하는 것은 쉽지 않을 뿐만 아니라 많은 비용이 소요되며, 어느 순간 만족할 만한 성능을 낼 수 있는 사전을 구축했다. 할지라도 유지 관리 하는 것이 결코 쉬운 일이 아니며 마찬가지로 많은 비용을 필요로 하게 된다. 따라서, 잘 구축된 자원으로부터 필요한 정보를 추출하여 적절한 사전을 자동으로 구축하여 활용하는 방법을 사용할 경우, 사전 구축 및 관리에 드는 많은 비용을 줄이면서도 상당히 효과적인 성능을 얻을 수 있을 것이다. 본 연구에서는 바이오 텍스트 마이닝 엔진을 위한 생물학적 개체명 사전을 자동으로 구축하고 이를 쉽게 관리하도록 하는 도구를 개발하였다.