• Title/Summary/Keyword: Homographs

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A Study of the Prosodic Characteristics of Homographs with Context Cues by Subjects with Right and Left Hemisphere Damage (문맥 내에서 좌우반구 손상자의 동음어에 대한 운율 산출 비교)

  • Lee, Myoung-Soon
    • Phonetics and Speech Sciences
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    • v.2 no.1
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    • pp.13-21
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    • 2010
  • The purpose of this study was to examine the prosody characteristics of sentence-level utterances which contain homographs with context cues in patients with neurogenic communication disorders. Homographs which may be affected by prosody, especially tonic length features, were used to investigate this matter. The characteristics of tone, duration, pitch, and pitch peak were analyzed to examine the characteristics of prosody in patients with lesions in the left or right hemisphere and normal controls. The whole process was recorded using Praat 4.3.14 and for statistical analyses, three-way ANOVA and multiple comparative analyses, Chi-Square tests, and a one-way ANOVA were carried out using SPSS 12.0 for Windows. The conclusions of this study are as follows. First, the length of syllables and vowels in homographs in Korean was different depending on the meaning and was not significant between groups. Second, it was found that patients with lesions in the right hemisphere had significant difference on pitch. Third, it was found that frequency of pitch peak and tone in 'short' tone syllables were different between groups. The conclusion of this study found that the prosody of homographs between groups absolutely was not differentiated. Accordingly, more detailed studies of acoustic parameters and other parameters which the prosody characteristic between groups could be found are needed in the future.

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An Evaluation of Translation Quality by Homograph Disambiguation in Korean-X Neural Machine Translation Systems (한-X 신경기계번역시스템에서 동형이의어 분별에 따른 변역질 평가)

  • Nguyen, Quang-Phuoc;Shin, Joon-Choul;Ock, Cheol-Young
    • Annual Conference on Human and Language Technology
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    • 2018.10a
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    • pp.504-509
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    • 2018
  • Neural machine translation (NMT) has recently achieved the state-of-the-art performance. However, it is reported failing in the word sense disambiguation (WSD) for several popular language pairs. In this paper, we explore the extent to which NMT systems are able to disambiguate the Korean homographs. Homographs, words with different meanings but the same written form, cause the word choice problems for NMT systems. Consistent with the popular language pairs, we discover that NMT systems fail to translate Korean homographs correctly. We provide a Korean word sense disambiguation tool-UTagger to use for improvement of NMT's translation quality. We conducted translation experiments using Korean-English and Korean-Vietnamese language pairs. The experimental results show that UTagger can significantly improve the translation quality of NMT in terms of the BLEU, TER, and DLRATIO evaluation metrics.

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A Study on the Description of Relationships and Homographs in Terms of Creator and Work in the Korean Thesaurus (한글 시소러스에서 저자와 저작에 대한 관계 설정과 동형 이의어의 기술)

  • Han, Sang-Kil;Choi, Suk-Doo
    • Journal of the Korean Society for Library and Information Science
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    • v.45 no.4
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    • pp.139-155
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    • 2011
  • The failure of distinguishing homographs in describing relations of individual authors and relations of authorship(i.e. distinction of persons with the same name or persons of the same literary author's name) will cause difficulties of retrieving exact information. It is because relations of automorphism cannot be formed between the two sets mentioned above. Therefore, it is ultimately necessary to set up the criteria or tools to distinguish homographs in order to retrieve more exact information. In the past, some efforts were made to develop authority data in order to solve the homograph problems by individual libraries, documents and portal sites in Korea. It is well understood that developing authority data by an individual institution was very difficult with no criteria or no rules to clarify the homograph problems at the national level. This study is to develop ways of recognizing individual names including subject words and proper nouns. The results of the study will present methods of distinguishing and describing homographs between individual author sets, and authorship sets particularly focused on the areas of arts and popular culture.

Verb Sense Disambiguation using Subordinating Case Information (종속격 정보를 적용한 동사 의미 중의성 해소)

  • Park, Yo-Sep;Shin, Joon-Choul;Ock, Cheol-Young;Park, Hyuk-Ro
    • The KIPS Transactions:PartB
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    • v.18B no.4
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    • pp.241-248
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    • 2011
  • Homographs can have multiple senses. In order to understand the meaning of a sentence, it is necessary to identify which sense isused for each word in the sentence. Previous researches on this problem heavily relied on the word co-occurrence information. However, we noticed that in case of verbs, information about subordinating cases of verbs can be utilized to further improve the performance of word sense disambiguation. Different senses require different sets of subordinating cases. In this paper, we propose the verb sense disambiguation using subordinating case information. The case information acquire postposition features in Standard Korean Dictionary. Our experiment on 12 high-frequency verb homographs shows that adding case information can improve the performance of word sense disambiguation by 1.34%, from 97.3% to 98.7%. The amount of improvement may seem marginal, we think it is meaningful because the error ratio reduced to less than a half, from 2.7% to 1.3%.

Improvement of Korean Homograph Disambiguation using Korean Lexical Semantic Network (UWordMap) (한국어 어휘의미망(UWordMap)을 이용한 동형이의어 분별 개선)

  • Shin, Joon-Choul;Ock, Cheol-Young
    • Journal of KIISE
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    • v.43 no.1
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    • pp.71-79
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    • 2016
  • Disambiguation of homographs is an important job in Korean semantic processing and has been researched for long time. Recently, machine learning approaches have demonstrated good results in accuracy and speed. Other knowledge-based approaches are being researched for untrained words. This paper proposes a hybrid method based on the machine learning approach that uses a lexical semantic network. The use of a hybrid approach creates an additional corpus from subcategorization information and trains this additional corpus. A homograph tagging phase uses the hypernym of the homograph and an additional corpus. Experimentation with the Sejong Corpus and UWordMap demonstrates the hybrid method is to be effective with an increase in accuracy from 96.51% to 96.52%.

Translation of Korean Object Case Markers to Mongolian's Suffixes (한국어 목적격조사의 몽골어 격 어미 번역)

  • Setgelkhuu, Khulan;Shin, Joon Choul;Ock, Cheol Young
    • KIPS Transactions on Software and Data Engineering
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    • v.8 no.2
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    • pp.79-88
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    • 2019
  • Machine translation (MT) system, especially Korean-Mongolian MT system, has recently attracted much attention due to its necessary for the globalization generation. Korean and Mongolian have the same sentence structure SOV and the arbitrarily changing of their words order does not change the meaning of sentences due to postpositional particles. The particles that are attached behind words to indicate their grammatical relationship to the clause or make them more specific in meaning. Hence, the particles play an important role in the translation between Korean and Mongolian. However, one Korean particle can be translated into several Mongolian particles. This is a major issue of the Korean-Mongolian MT systems. In this paper, to address this issue, we propose a method to use the combination of UTagger and a Korean-Mongolian particles table. UTagger is a system that can analyze morphologies, tag POS, and disambiguate homographs for Korean texts. The Korean-Mongolian particles table was manually constructed for matching Korean particles with those of Mongolian. The experiment on the test set extracted from the National Institute of Korean Language's Korean-Mongolian Learner's Dictionary shows that our method achieved the accuracy of 88.38% and it improved the result of using only UTagger by 41.48%.