• Annual Conference on Human and Language Technology
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• 2002.10e
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• pp.33-39
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• 2002

중국어의 품사 태깅(part-of-speech tagging)을 위해서는 중국어 문장들은 내부 단어간의 명확한 분리가 없기 때문에 단어 분할(word segmentation)과 품사 태깅을 동시에 처리해야 한다. 본 논문은 규칙 기반(rule base)과 사전 기반(dictionary base) 기법을 혼합하여 구현한 단어 분할 시스템을 사용하여 입력 문장을 단어 단위로 분할하고, HMM(hidden Markov model) 기반 통계적 품사 태깅 기법을 사용한다. 특히, 본 논문에서는 주어진 말뭉치(corpus)로부터 자동 학습(automatic training)을 통해 품사 사전을 구축하여 구현된 시스템과 말뭉치간의 독립성을 유지한다. 말뭉치는 중국어 간체와 번체 모두를 대상으로 하고, 각 말뭉치로부터 자동 학습을 통해 얻어진 품사 사전으로 단어 분할과 품사 태깅을 한다. 실험결과들은 간체, 번체 각각의 단어 분할 성능과 품사 태깅 성능을 보여준다.

• ETRI Journal
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• v.33 no.5
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• pp.712-719
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• 2011

In this paper, we study the problem of domain adaptation for structural support vector machines (SVMs). We consider a number of domain adaptation approaches for structural SVMs and evaluate them on named entity recognition, part-of-speech tagging, and sentiment classification problems. Finally, we show that a prior model for structural SVMs outperforms other domain adaptation approaches in most cases. Moreover, the training time for this prior model is reduced compared to other domain adaptation methods with improvements in performance.

• Proceedings of the Korean Information Science Society Conference
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• 1999.10b
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• pp.57-59
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• 1999

본 논문에서는 최대 엔트로피 원리에 기반한 문서 분류기의 학습을 제안한다. 최대 엔트로피 기법은 자연언어 처리에서 언어 모델링(Language Modeling), 품사 태깅 (Part-of-Speech Tagging) 등에 널리 사용되는 방법중의 하나이다. 최대 엔트로피 모델의 효율성을 위해서는 자질 선정이 중요한데, 본 논문에서는 자질 집합의 선택을 위한 기준으로 chi-square test, log-likelihood ratio, information gain, mutual information 등의 방법을 이용하여 실험하고, 전체 후보 자질에 대한 실험 결과와 비교해 보았다. 데이터 집합으로는 Reuters-21578을 사용하였으며, 각 클래스에 대한 이진 분류 실험을 수행하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04c
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• pp.522-524
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• 2003

품사 결정 문제는 자연언어처리의 가장 기본적인 문제들 중 하나이며, 기계학습의 관점에서 보면 분류 문제(classification problem)로 쉽게 표현된다. 본 논문에서는 품사 결정의 모호성을 해소하기 위해서 최대 엔트로피 부스팅 모델(maximum entropy boosting model)을 이 문제에 적응하였다. 그리고, 품사 결정에서 중요한 요소 중의 하나인 미지어 처리를 위해서 특별히 설계된 일차 자질을 고려하였다. 최대 엔트로피 부스팅 모델의 장점은 쉬운 모델링인데, 실제로 품사 결정을 위한 일차 자질만 작성하는 노려만 들이고도 96.78%의 정확도를 보여 지금까지 알려진 최고의 성능과 거의 비슷한 결과를 보였다.

• Annual Conference on Human and Language Technology
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• 1999.10e
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• pp.433-437
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• 1999

본 논문에서는 품사 태깅을 위한 방법으로 어절간 품사 패턴 정보를 이용하는 방법을 제안한다. 품사 태깅을 위하여 여러 어절들 간의 품사 패턴 정보를 통계 정보로 구축하고 품사 태깅시에 품사 패턴 정보를 이용하여 품사 태깅을 수행한다. 이때 품사 패턴 적용시 몇가지 제약 규칙을 둠으로써 품사 태깅의 정확률을 높이는 방법을 연구하였다.

• Journal of KIISE
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• v.44 no.1
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• pp.57-62
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• 2017

Traditional Korean morphological analysis and POS tagging methods usually consist of two steps: 1 Generat hypotheses of all possible combinations of morphemes for given input, 2 Perform POS tagging search optimal result. require additional resource dictionaries and step could error to the step. In this paper, we tried to solve this problem end-to-end fashion using sequence-to-sequence model convolutional features. Experiment results Sejong corpus sour approach achieved 97.15% F1-score on morpheme level, 95.33% and 60.62% precision on word and sentence level, respectively; s96.91% F1-score on morpheme level, 95.40% and 60.62% precision on word and sentence level, respectively.

• Journal of information and communication convergence engineering
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• v.18 no.4
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• pp.207-215
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• 2020

Part of speech (POS) tagging is an indispensable part of major NLP models. Its progress can be perceived on number of languages around the globe especially with respect to European languages. But considering Indian Languages, it has not got a major breakthrough due lack of supporting tools and resources. Particularly for Odia language it has not marked its dominancy yet. With a motive to make the language Odia fit into different NLP operations, this paper makes an attempt to develop a POS tagger for the said language on a HMM (Hidden Markov Model) platform. The tagger judiciously considers bigram HMM with dynamic Viterbi algorithm to give an output annotated text with maximum accuracy. The model is experimented on a corpus belonging to tourism domain accounting to a size of approximately 0.2 million tokens. With the proportion of training and testing as 3:1, the proposed model exhibits satisfactory result irrespective of limited training size.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.162-179
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• 2008

We have developed a tool for annotation of electronic health record (EHR) data. Currently we are in the process of manually annotating a corpus of Norwegian general practitioners' EHRs with mainly linguistic information. The purpose of this project is to attain a linguistically annotated corpus of patient histories from general practice. This corpus will be put to future use in medical language processing and information extraction applications. The paper outlines some of our practical experiences from developing such a corpus and, in particular, the effects of semi-automated annotation. We have also done some preliminary experiments with part-of-speech tagging based on our corpus. The results indicated that relevant training data from the clinical domain gives better results for the tagging task in this domain than training the tagger on a corpus form a more general domain. We are planning to expand the corpus annotations with medical information at a later stage.

• Journal of KIISE:Software and Applications
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• v.27 no.1
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• pp.84-95
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• 2000

In this paper, we propose an integrated indexing method with compound noun segmentation and noun phrase synthesis. Statistical information is used in the compound noun segmentation and natural language processing techniques are carefully utilized in the noun phrase synthesis. Firstly, we choose index terms from simple words through morphological analysis and part-of-speech tagging results. Secondly, noun phrases are automatically synthesized from the syntactic analysis results. If syntactic analysis fails, only morphological analysis and tagging results are applied. Thirdly, we select compound nouns from the tagging results and then segment and re-synthesize them using statistical information. In this way, segmented and synthesized terms are used together as index terms to supplement the single terms. We demonstrate the effectiveness of the proposed integrated indexing method for Korean compound noun processing using KTSET2.0 and KRIST SET which are a standard test collection for Korean information retrieval.

• The KIPS Transactions:PartB
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• v.18B no.1
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• pp.45-50
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• 2011

Unknown morpheme errors in Korean morphological analysis are divided into two types: The one is the errors that a morphological analyzer entirely fails to return any morpheme sequences, and the other is the errors that a morphological analyzer returns incorrect combinations of known morphemes. Most previous unknown morpheme estimation techniques have been focused on only the former errors. This paper proposes a unknown morpheme estimation method which can handle both of the unknown morpheme errors. The proposed method detects Eojeols (Korean spacing units) that may include unknown morpheme errors using SVM (Support Vector Machine). Then, using CRFs (Conditional Random Fields), it segments morphemes from the detected Eojeols and annotates the segmented morphemes with new POS tags. In the experiments, the proposed method outperformed the conventional method based on the longest matching of functional words. Based on the experimental results, we knew that the second type errors should be dealt with in order to increase the performance of Korean morphological analysis.