• Speech Sciences
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• v.8 no.4
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• pp.229-241
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• 2001

Using eighteen texts from various genera of present-day Japanese, I collected phonologically reduced forms frequently observed in conversational Japanese, and classified them in search of a unified. explanation of phonological phenomena. I found 7,516 cases of reduced forms which I divided into 43 categories according to the types of phonological changes they have undergone. The general tendencies are that deletion and fusion of a phoneme or an entire syllable takes place frequently, resulting in the decrease in the number of syllables. From a morphosyntactic point of view, phonological reduction often occurs at the NP and VP morpheme boundaries. The following findings are drawn from phonetical observations of reduction. (1) Vowels are more easily deleted than consonants. (2) Bilabials ([m], [b], and [w]) are the most likely candidates for deletion. (3) In a concatenation of vowels, closed vowels are absorbed into open vowels, or two adjacent vowels come to create another vowel, in which case reconstruction of the original sequence is not always predictable. (4) Alveolars are palatalized under the influence of front vowels. (5) Regressive assimilation takes place in a syllable starting with [r], changing the entire syllable into a phonological choked sound or a syllabic nasal, depending on the voicing of the following phoneme.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.504-505
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• 2017

Existing morpheme analyzers classify the words used in writing documents. A system for extracting sentences and paragraphs based on a morpheme analyzer is being developed. However, there are very few systems that compress documents and extract important paragraphs. The algorithm proposed in this paper calculates the weights of the keyword written in the document and extracts the paragraphs containing the keyword. Users can reduce the time to understand the document by reading the paragraphs containing the keyword without reading the entire document. In addition, since the number of extracted paragraphs differs according to the number of keyword used in the search, the user can search various patterns compared to the existing system.

• 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 the Korea Institute of Information and Communication Engineering
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• v.19 no.4
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• pp.780-786
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• 2015

Social media, such as Social Network Service include a lot of spontaneous opinions from customers, so recent companies collect and analyze information about customer feedback by using the system that analyzes Big Data on social media in order to efficiently operate businesses. However, it is difficult to analyze data collected from online sites accurately with existing morpheme analyzer because those data have spacing errors and spelling errors. In addition, many online sentences are short and do not include enough meanings which will be selected, so established meaning selection methods, such as mutual information, chi-square statistic are not able to practice Emotional Classification. In order to solve such problems, this paper suggests a module that can revise the meanings by using initial consonants/vowels and phase pattern dictionary and meaning selection method that uses priority of word class in a sentence. On the basis of word class extracted by morpheme analyzer, these new mechanisms would separate and analyze predicate and substantive, establish properties Database which is subordinate to relevant word class, and extract positive/negative emotions by using accumulated properties Database.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.4
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• pp.121-128
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• 2018

A Korean morphological analyzer adopts sequence-to-sequence (seq2seq) model, which can generate an output sequence of different length from an input. In general, a seq2seq based Korean morphological analyzer takes a syllable-unit based sequence as an input, and output a syllable-unit based sequence. Syllable-based morphological analysis has the advantage that unknown words can be easily handled, but has the disadvantages that morpheme-based information is ignored. In this paper, we propose a reranking model as a post-processor of seq2seq model that can improve the accuracy of morphological analysis. The seq2seq based morphological analyzer can generate K results by using a beam-search method. The reranking model exploits morpheme-unit embedding information as well as n-gram of morphemes in order to reorder K results. The experimental results show that the reranking model can improve 1.17% F1 score comparing with the original seq2seq model.

• Annual Conference on Human and Language Technology
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• 1998.10c
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• pp.396-404
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• 1998

본 논문에서는 언어학적 단위인 형태소의 특성을 유지하면서 음성인식 과정에 적합한 분리 기준의 새로운 디코딩 단위인 의사형태소(Pseudo-Morpheme)를 정의 하였다. 이러한 필요성을 확인하기 위해 새로이 정의된 40개의 품사 태그를 갖는 의사 형태소를 표제어 단위로 삼아 발음사전 생성과 형태소 해석에 초점을 두고 한국어 연속음성 인식 시스템을 구성하였다.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.12
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• pp.865-872
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• 2013

Two-level morphology analysis method is one of rule-based morphological analysis method. This approach handles morphological transformation using rules and analyzes words with morpheme connection information in a lexicon. It is independent of language and Korean Two-level system was also developed. But, it was limited in practical use, because of using very small set of lexicon built manually. And it has also a over-generation problem. In this paper, we propose an automatic construction method of Korean Two-level lexicon for PC-KIMMO from morpheme tagged corpus. We also propose a method to solve over-generation problem using lexical information and sub-tags. The experiment showed that the proposed method reduced over-generation by 68% compared with the previous method, and the performance increased from 39% to 65% in f-measure.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.919-927
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• 2010

Since the performance of mobile devices is recently improved, the requirement of information retrieval is increased in the mobile devices as well as PCs. If a mobile device with small memory uses a tradition language analysis tool to extract nouns from korean texts, it will impose a burden of analysing language. As a result, the need for the language analysis tools adequate to the mobile devices is increasing. Therefore, this paper proposes a new method for noun extraction using post-noun morpheme sequences and noun patterns from a large corpus. The proposed noun extractor has only the dictionary capacity of 146KB and its performance shows 0.86 $F_1$-measure; the capacity of noun dictionary corresponds to only the 4% capacity of the existing noun extractor with a POS tagger. In addition, it easily extract nouns for unknown word because its dependence for noun dictionaries is low.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.129-139
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• 1994

This paper presents a morphological analysis method for the Korean language. The characteristics and adjacency information of the words can be obtained from sentences in a large corpus. Generally a word can be analyzed to a result by applying the adjacency attributes and rules. However, we have to choose one from the several results for the ambiguous words. The collected morpheme's adjacency attributes and relations with neighbor words are recorded in a well designed dictionaries. With this information, abbreviated words as well as ambiguous words can be almost analyzed successfully. Efficiency of morphological analyzer depends on the information in the dictionaries. A morpheme dictionary and a phrase dictionary have been designed with lexical database, and necessary information extracted from the corpus is stored in the dictionaries.

• Journal of the Korea Convergence Society
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• v.12 no.7
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• pp.45-51
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• 2021

In accordance with the rapid non-face-to-face environment and mobile first strategy, the explosive increase and creation of many structured/unstructured data every year demands new decision making and services using big data in all fields. However, there have been few reference cases of using the Hadoop Ecosystem, which uses the rapidly increasing big data every year to collect and load big data into a standard platform that can be applied in a practical environment, and then store and process well-established big data in a relational database. Therefore, in this study, after collecting unstructured data searched by keywords from social network services based on Hadoop 2.0 through three virtual machine servers in the Spring Framework environment, the collected unstructured data is loaded into Hadoop Distributed File System and HBase based on the loaded unstructured data, it was designed and implemented to store standardized big data in a relational database using a morpheme analyzer. In the future, research on clustering and classification and analysis using machine learning using Hive or Mahout for deep data analysis should be continued.