Word Sense Disambiguation of Predicate using Sejong Electronic Dictionary and KorLex

Kang, Sang-Wook; Kim, Minho; Kwon, Hyuk‐Chul; Jeon, SungKyu; Oh, Juhyun

doi:10.5626/ktcp.2015.21.7.500

Cited by 3 publications

(3 citation statements)

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“…However, Korean WordNet is not developed as much as PWN, so its structure is not as deep as PWN and does not contain various forms in Korean words, especially verbs. Despite these limitations, [10] gained 96% precisions in WSD. It means Korean WordNet is still a fine resource for NLP.…”

Section: Wordnet Based Researchmentioning

confidence: 99%

Document Classification Using N-gram and Word Semantic Similarity

Ren¹,

Kang²

2015

IJUNESST

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Section: Wordnet Based Researchmentioning

confidence: 99%

Document Classification Using N-gram and Word Semantic Similarity

Ren¹,

Kang²

2015

IJUNESST

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“…Even modern approaches such as deep learning [34] or embedded word space [35] face the lack of data due to the limited training dataset. The knowledge-based approach [36,37] can solve this issue, but it requires a large and precise lexical network. This approach can use Korean WordNet KorLex [38], which was constructed by translating English WordNet to Korean as the knowledge base.…”

Section: Knowledge-based Approach For Wsdmentioning

confidence: 99%

UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study

Nguyen

Shin

et al. 2020

Applied Sciences

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Machine translation (MT) has recently attracted much research on various advanced techniques (i.e., statistical-based and deep learning-based) and achieved great results for popular languages. However, the research on it involving low-resource languages such as Korean often suffer from the lack of openly available bilingual language resources. In this research, we built the open extensive parallel corpora for training MT models, named Ulsan parallel corpora (UPC). Currently, UPC contains two parallel corpora consisting of Korean-English and Korean-Vietnamese datasets. The Korean-English dataset has over 969 thousand sentence pairs, and the Korean-Vietnamese parallel corpus consists of over 412 thousand sentence pairs. Furthermore, the high rate of homographs of Korean causes an ambiguous word issue in MT. To address this problem, we developed a powerful word-sense annotation system based on a combination of sub-word conditional probability and knowledge-based methods, named UTagger. We applied UTagger to UPC and used these corpora to train both statistical-based and deep learning-based neural MT systems. The experimental results demonstrated that using UPC, high-quality MT systems (in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score) can be built. Both UPC and UTagger are available for free download and usage.

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“…Knowledge-based approaches can overcome this problem, but they require an accurate and large lexical network [41], [42]. Korean WordNet KorLex [21], which was constructed by translating English WordNet to Korean, is either used as a knowledge base [43] or combined with the Korean monolingual dictionary [44]. However, because of the limited lexicons of KorLex (refer to Table 3) and the difference between characteristics of English and those of Korean (i.e., ''to take medicine'' in English but ''to eat medicine'' in Korean), the accuracy is insufficient.…”

Section: Sub-word Conditional Probabilitymentioning

confidence: 99%

Korean-Vietnamese Neural Machine Translation System With Korean Morphological Analysis and Word Sense Disambiguation

Nguyen

Shin

et al. 2019

IEEE Access

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Although deep neural networks have recently led to great achievements in machine translation (MT), various challenges are still encountered during the development of Korean-Vietnamese MT systems. Because Korean is a morphologically rich language and Vietnamese is an analytic language, neither have clear word boundaries. The high rate of homographs in Korean causes word ambiguities, which causes problems in neural MT (NMT). In addition, as a low-resource language pair, there is no freely available, adequate Korean-Vietnamese parallel corpus that can be used to train translation models. In this paper, we manually established a lexical semantic network for the special characteristics of Korean as a knowledge base that was used for developing our Korean morphological analysis and word-sense disambiguation system: UTagger. We also constructed a large Korean-Vietnamese parallel corpus, in which we applied the state-of-the-art Vietnamese word segmentation method RDRsegmenter to Vietnamese texts and UTagger to Korean texts. Finally, we built a bi-directional Korean-Vietnamese NMT system based on the attentionbased encoder-decoder architecture. The experimental results indicated that UTagger and RDRsegmenter could significantly improve the performance of the Korean-Vietnamese NMT system, achieving remarkable results by 27.79 BLEU points and 58.77 TER points in Korean-to-Vietnamese direction and 25.44 BLEU points and 58.72 TER points in the reverse direction. INDEX TERMS Korean-Vietnamese machine translation, Korean-Vietnamese parallel corpus, lexical semantic network, morphological analysis, neural machine translation, word sense disambiguation. I. INTRODUCTION Neural machine translation based on the attention-based encoder-decoder model [1], [2] has emerged as the dominant paradigm in MT. It has achieved state-of-the-art performance in the translation of language pairs that have large amounts of training parallel corpora, such as English-French [3] and English-German [4]. However, it has shown poor translation quality in low-resource language pairs where training parallel corpora are scarce [5], [6]. Korean-Vietnamese is a low-resource language pair, and Korean-Vietnamese MT systems need to be built to serve The associate editor coordinating the review of this manuscript and approving it for publication was Yang Zhen.

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Word Sense Disambiguation of Predicate using Sejong Electronic Dictionary and KorLex

Cited by 3 publications

References 1 publication

Document Classification Using N-gram and Word Semantic Similarity

Document Classification Using N-gram and Word Semantic Similarity

UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study

Korean-Vietnamese Neural Machine Translation System With Korean Morphological Analysis and Word Sense Disambiguation

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