• Journal of the Korean Society for information Management
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• v.18 no.2
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• pp.105-124
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• 2001

The purpose of this study is to develop a Korean word analysis system, which can improve performance of IRS, based on various methods of word analysis. In this study we focused on maximizing the speed of Korean word analysis, modulizing each functional system and analyzing Korean morpheme precisely. The system, developed in this study, implemented optimal algorithm to increase the speed of word analysis and to verify speed and performance of each subsystem. In addition, the numeral analysis processing was achieved to reduce a system burden by avoiding recursive analysis of compound nouns, based on numeral pattern recognition.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.4
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• pp.33-40
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• 2020

In order to satisfy customers, it is important to identify the quality elements that affect customers' satisfaction. The Kano model has been widely used in identifying multi-dimensional quality attributes in this purpose. However, the model suffers from various shortcomings and limitations, especially those related to survey practices such as the data amount, reply attitude and cost. In this research, a model based on the text sentiment analysis is proposed, which aims to substitute the survey-based data gathering process of Kano models with sentiment analysis. In this model, from the set of opinion text, quality elements for the research are extracted using the morpheme analysis. The opinions' polarity attributes are evaluated using text sentiment analysis, and those polarity text items are transformed into equivalent Kano survey questions. Replies for the transformed survey questions are generated based on the total score of the original data. Then, the question-reply set is analyzed using both the original Kano evaluation method and the satisfaction index method. The proposed research model has been tested using a large amount of data of public IT service project evaluations. The result shows that it can replace the existing practice and it promises advantages in terms of quality and cost of data gathering. The authors hope that the proposed model of this research may serve as a new quality analysis model for a wide range of areas.

• Journal of KIISE
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• v.41 no.9
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• pp.642-651
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• 2014

This paper proposes three probabilistic models for syllable-based Korean morphological analysis, and presents the performance of proposed probabilistic models. Probabilities for the models are acquired from POS-tagged corpus. The result of 10-fold cross-validation experiments shows that 98.3% answer inclusion rate is achieved when trained with Sejong POS-tagged corpus of 10 million eojeols. In our models, POS tags are assigned to each syllable before spelling recovery and morpheme generation, which enables more efficient morphological analysis than the previous probabilistic models where spelling recovery is performed at the first stage. This efficiency gains the speed-up of morphological analysis. Experiments show that morphological analysis is performed at the rate of 147K eojeols per second, which is almost 174 times faster than the previous probabilistic models for Korean morphology.

• Journal of Information Technology Services
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• v.15 no.3
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• pp.147-155
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• 2016

According to rapid development of technology, web text is growing explosively and attracting many fields as substitution for survey. The user of Facebook is reaching up to 113 million people per month, Twitter is used in various institution or company as a behavioral analysis tool. However, many research has focused on meaning of the text itself. And there is a lack of study for text's creation subject. Therefore, this research consists of sex/age text classification with by using 20,187 Facebook users' posts that reveal the sex and age of the writer. This research utilized Convolution Neural Networks, a type of deep learning algorithms which came into the spotlight as a recent image classifier in web text analyzing. The following result assured with 92% of accuracy for possibility as a text classifier. Also, this research was minimizing the Korean morpheme analysis and it was conducted using a Korean web text to Authorship Attribution. Based on these feature, this study can develop users' multiple capacity such as web text management information resource for worker, non-grammatical analyzing system for researchers. Thus, this study proposes a new method for web text analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.3
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• pp.358-364
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• 2006

At present age, great many informations are no exaggeration to say that supply information of better quality to users depend on that grasp correctly user's query intention through internet along with fast development of internet. Therefore, this thesis suggest that generating meaning relation between keywords with that result by passing through morpheme analysis and syntactic analysis about Natural Language Query. This approach is implied more meaning relation than query by simple keyword or simple combination between keywords. Therefore, it is going to permit much more efficient information retrieval because of solving problem about existent query form, and generating query that user's query intention is reflected more correctly.

• Journal of The Korean Association For Science Education
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• v.38 no.2
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• pp.219-234
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• 2018

In this study, we explored the possibility of automating the process of analyzing elements of scientific argument in the context of a Korean classroom. To gather training data, we collected 990 sentences from science education journals that illustrate the results of coding elements of argumentation according to Toulmin's argumentation structure framework. We extracted 483 sentences as a test data set from the transcription of students' discourse in scientific argumentation activities. The words and morphemes of each argument were analyzed using the Python 'KoNLPy' package and the 'Kkma' module for Korean Natural Language Processing. After constructing the 'argument-morpheme:class' matrix for 1,473 sentences, five machine learning techniques were applied to generate predictive models relating each sentences to the element of argument with which it corresponded. The accuracy of the predictive models was investigated by comparing them with the results of pre-coding by researchers and confirming the degree of agreement. The predictive model generated by the k-nearest neighbor algorithm (KNN) demonstrated the highest degree of agreement [54.04% (${\kappa}=0.22$)] when machine learning was performed with the consideration of morpheme of each sentence. The predictive model generated by the KNN exhibited higher agreement [55.07% (${\kappa}=0.24$)] when the coding results of the previous sentence were added to the prediction process. In addition, the results indicated importance of considering context of discourse by reflecting the codes of previous sentences to the analysis. The results have significance in that, it showed the possibility of automating the analysis of students' argumentation activities in Korean language by applying machine learning.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.169-178
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• 2022

Morphemes are most primitive units in a language that lose their original meaning when segmented into smaller parts. In Korean, a sentence is a sequence of eojeols (words) separated by spaces. Each eojeol comprises one or more morphemes. Korean morphological analysis (KMA) is to divide eojeols in a given Korean sentence into morpheme units. It also includes assigning appropriate part-of-speech(POS) tags to the resulting morphemes. KMA is one of the most important tasks in Korean natural language processing (NLP). Improving the performance of KMA is closely related to increasing performance of Korean NLP tasks. Recent research on KMA has begun to adopt the approach of machine translation (MT) models. MT is to convert a sequence (sentence) of units of one domain into a sequence (sentence) of units of another domain. Neural machine translation (NMT) stands for the approaches of MT that exploit neural network models. From a perspective of MT, KMA is to transform an input sequence of units belonging to the eojeol domain into a sequence of units in the morpheme domain. In this paper, we propose a deep learning model for KMA. The backbone of our model is based on the BERT-fused model which was shown to achieve high performance on NMT. The BERT-fused model utilizes Transformer, a representative model employed by NMT, and BERT which is a language representation model that has enabled a significant advance in NLP. The experimental results show that our model achieves 98.24 F1-Score.

• Proceedings of the Korean Society for Language and Information Conference
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• 2002.02a
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• pp.36-49
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• 2002

One main complexity of the copula constructions concerns a mismatch between morphology and syntactic constituency: the copula seems to form a morphological unit with the immediately preceding element, whereas in terms of syntax the copula appears to take this as its syntactic complement. In capturing such mismatches, we show that the copula is treated as an independent verb at the level of tectogrammatical structure (or syntax tree), whereas as a bound morpheme at the level of phonogram-matical structure (or domain tree), in terms of Dowty 1992 (or Reape 1994). This paper, adopting the notion of DOMAIN in HPSG, shows that copula constructions are a subtype of compacting-constructions. These constructions compact the domain value of the copula and that of its preceding element together into one domain unit, eventually making it inert to syntactic phenomena such as scrambling, deletion and pro-form substitution. This construction-based approach provides a clean analysis for the formation of the copula construction and related phenomena.

• Korean Journal of Cognitive Science
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• v.3 no.1
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• pp.25-39
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• 1991

When an ending which begins with 'a/a'combines to a stem,'a/a'can be deleted.Especially when ot combines to an h-irregular verb,it is represented as a variant like '-a-','-e-','-ia-',or'-ie-'.In order to analyze the variants of 'a/a',we suggest the format of a grammatical morpheme dictionary which is represented as a binary tree and several procedures which process the variants so that the unexpected errors can be removed which occur frequently when we analyze Korean worl phrase.

• Annual Conference on Human and Language Technology
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• 2013.10a
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• pp.13-17
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• 2013

본 논문은 CRF기반 한국어 형태소 분석 및 품사 태깅 과정에서 발생하는 미등록 복합형태소를 분해하기 위한 단순하고 효과적인 방법을 제안한다. 제안 방법은 1) 복합형태소를 내용형태소와 복합기능형태소로 분리하는 단계, 2) 복합기능형태소를 분해하는 두 단계로 구성된다. 실험 결과, 제안 알고리즘은 Sejong데이터에 대해, 기존의 lattice HMM 대비 높은 복합형태소 분해 정확률 및 두드러진 속도 개선을 보여준다.