• Journal of rehabilitation welfare engineering & assistive technology
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• v.6 no.1
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• pp.29-35
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• 2012

Most Augmentative and Alternative Communication(AAC) use exclusive equipment or studied desktop, tablet PC based windows. Besides, the preceding study offers proper noun dictionary so, henceforward study has problem to innumerable proper noun processing. This paper suggests a method of proper noun processing using a mobile smart equipment. And via the button with virtual keyboard input method and the errors that can occur is also proposing a complementary way. AAC system to check availability for application on Android has been implemented. Experimental results, depending on user location and selection of proper nouns in the around could produce a sentence is derived.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1254-1271
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• 2018

Keyphrase extraction is one of fundamental natural language processing (NLP) tools to improve many text-mining applications such as document summarization and clustering. In this paper, we propose to use two novel techniques on the top of the state-of-the-art keyphrase extraction methods. First is the anti-patterns that aim to recognize non-keyphrase candidates. The state-of-the-art methods often used the rich feature set to identify keyphrases while those rich feature set cover only some of all keyphrases because keyphrases share very few similar patterns and stylistic features while non-keyphrase candidates often share many similar patterns and stylistic features. Second one is to use the dependency graph instead of the word co-occurrence graph that could not connect two words that are syntactically related and placed far from each other in a sentence while the dependency graph can do so. In experiments, we have compared the performances with different settings of the graphs (co-occurrence and dependency), and with the existing method results. Finally, we discovered that the combination method of dependency graph and anti-patterns outperform the state-of-the-art performances.

• The Transactions of the Korea Information Processing Society
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• v.4 no.8
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• pp.2025-2037
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• 1997

The concept-based translation system, which is a part of the Korean spoken language translation system, translates spoken utterances from Korean speech recognizer into one of English, Japanese and Korean in a travel planning task. Our system regulates semantic rather than the syntactic category in order to process the spontaneous speech which tends to be regarded as the one ungrammatical and subject to recognition errors. Utterances are parsed into concept structures, and the generation module produces the sentence of the specified target language. We have developed a token-separator using base-words and an automobile grammar corrector for Korean processing. We have also developed postprocessors for each target language in order to improve the readability of the generation results.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.744-751
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• 2022

Construction is among the most dangerous industries with numerous accidents occurring at job sites. Following an accident, an investigation report is issued, containing all of the specifics. Analyzing the text information in construction accident reports can help enhance our understanding of historical data and be utilized for accident prevention. However, the conventional method requires a significant amount of time and effort to read and identify crucial information. The previous studies primarily focused on analyzing related objects and causes of accidents rather than the construction activities. This study aims to extract construction activities taken by workers associated with accidents by presenting an automated framework that adopts a deep learning-based approach and natural language processing (NLP) techniques to automatically classify sentences obtained from previous construction accident reports into predefined categories, namely TRADE (i.e., a construction activity before an accident), EVENT (i.e., an accident), and CONSEQUENCE (i.e., the outcome of an accident). The classification model was developed using Convolutional Neural Network (CNN) showed a robust accuracy of 88.7%, indicating that the proposed model is capable of investigating the occurrence of accidents with minimal manual involvement and sophisticated engineering. Also, this study is expected to support safety assessments and build risk management systems.

• Annual Conference on Human and Language Technology
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• 2000.10d
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• pp.323-328
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• 2000

국내에서 영한 자동번역 시스템을 1985 년부터 개발한 지 벌써 15년이 흐르고 있다. 15 년의 영한 자동번역 기술개발에도 불구하고 아직도 영한 자동번역 시스템의 번역품질은 40%를 넘지 못하고 있다. 이렇게 번역품질이 낮은 이유는 다음과 같이 요약할 수 있을 것이다. o 입력문에 대해 파싱할 때 오른쪽 경계를 잘못 인식함으로써 구조적 모호성의 발생문제: 예를 들어 등위 접속절에서 오른쪽 등위절이 등위 접속절에 포함되는 지의 모호성. o 번역 단위로써 전체 문장을 대상으로 한 번역패턴이 아닌 구나 절과 같은 부분적인 번역패턴으로 인한 문장 전체의 잘못된 번역 결과 발생. o 점차 증가하는 대용량 번역지식의 구축과 관련해 새로 구축되는 번역 지식과 기구축된 대용량 번역지식들 간의 상호 충돌로 인한 번역 품질의 저하. 이러한 심각한 원인들을 극복하기 위해 본 논문에서는 문틀에 기반한 새로운 영한 자동번역 방법론을 소개하고자 한다. 이 문틀에 기반한 영한 자동번역 방법론은 현재 CNN뉴스 방송 자막을 대상으로 한 영한 자동번역 시스템에서 실제 활용되고 있다. 이 방법론은 기본적으로 data-driven 방법론에 속하다. 문틀 기반 자동번역 방법론은 규칙기반 자동번역 방법론보다는 낮은 단계에서 예제 기반 자동번역 방법론보다는 높은 단계에서 번역을 하는 번역방법론이다. 이 방법론은 영한 자동번역에 뿐만 아니라 다른 언어쌍에서의 번역에도 적용할 수 있을 것이다.

• Korean Journal of Cognitive Science
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• v.23 no.4
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• pp.487-515
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• 2012

The purpose of this study was to investigate the effect of the individual differences in cognitive processes on paragraph comprehension. To do so, the lexical decision task and the pattern comparison task were used to measure the low-level cognitive processes. Digit span task was used to test the phonological loop capacity. The individual differences of the central executive processing capacity were measured by operational span task. Reading span task was used to test the working memory capacity related with the sentence processing. Reading times and accuracies of the logically valid inferences and logically void inferences were tested to measure the high-level cognitive processes. Reading times and accuracies for the target sentences with and without prior explicit causal sentence were measured to test individuals' paragraph comprehension abilities. The results showed that the speed of the low-level cognitive processes was related with the speed of the high-level cognitive processes. Also, the accuracy of the low-level cognitive processes was related with the accuracy of the high-level cognitive processes while there was no significant correlation between the speed and the accuracy in any measures of the cognitive processes. Working memory capacity was related with the accuracy of the cognitive processes while it was not significantly correlated with the speed of the cognitive processes. Most importantly, the speed of low-level cognitive processes significantly affected the speed of the paragraph comprehension while the working memory capacity and the high-level cognitive processes had influences on the accuracies of the paragraph comprehension. The speed of the paragraph comprehension had no influence on the accuracies of the paragraph comprehension.

• Korean Journal of Cognitive Science
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• v.14 no.4
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• pp.21-31
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• 2003

This paper reports our experimental study with Korean-Chinese (=KC) bilinguals as compared with Korean monolinguals. We aim to find KC bilingual speakers' sentence processing strategies, and the interaction between the Ll and U2 grammars in bilingual development. To this end, we recruited 166 subjects of all age groups from age 3 to adult in the Korean autonomous community in Yanji, China, and did a classical subject/actor identification test, where subjects are supposed to pick out the subjects/actor of both sensical and nonsensical sentences (cf. Liu, Bates & Li, 1992). We compared our results with our previous work on monolingual Koreans, and found out that KC bilinguals rely on word order as well as anumacy; that KC bilinguals make use of morphology at age 10 as compared with age 5 for monolinguals; and that KC bilingual adults rely on animacy and word order as well as morphology, while monolingual Korean adults rely solely on morphology for sentence interpretation. Given that animacy and word order play an important role in the Chinese grammar, our finding lends support to the backward transfer which Liu, Bates & Li (1992) propose for early bilingualism.

• 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.

• Journal of Platform Technology
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• v.9 no.3
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• pp.52-62
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• 2021

Quality control is critical at manufacturing sites and is key to predicting the risk of quality defect before manufacturing. However, the reliability of manual quality control methods is affected by human and physical limitations because manufacturing processes vary across industries. These limitations become particularly obvious in domain areas with numerous manufacturing processes, such as the manufacture of major nuclear equipment. This study proposed a novel method for predicting the risk of quality defects by using natural language processing and machine learning. In this study, production data collected over 6 years at a factory that manufactures main equipment that is installed in nuclear power plants were used. In the preprocessing stage of text data, a mapping method was applied to the word dictionary so that domain knowledge could be appropriately reflected, and a hybrid algorithm, which combined n-gram, Term Frequency-Inverse Document Frequency, and Singular Value Decomposition, was constructed for sentence vectorization. Next, in the experiment to classify the risky processes resulting in poor quality, k-fold cross-validation was applied to categorize cases from Unigram to cumulative Trigram. Furthermore, for achieving objective experimental results, Naive Bayes and Support Vector Machine were used as classification algorithms and the maximum accuracy and F1-score of 0.7685 and 0.8641, respectively, were achieved. Thus, the proposed method is effective. The performance of the proposed method were compared and with votes of field engineers, and the results revealed that the proposed method outperformed field engineers. Thus, the method can be implemented for quality control at manufacturing sites.

• The Transactions of the Korea Information Processing Society
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• v.5 no.3
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• pp.851-861
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• 1998

Korean language is to be characterized by the rich vocal sound variation. In order to increase the probability of vocal sound recognition and to provide a natural vocal sound synthesis, a systematic and thorough research into the characteristics of Korean language including its vocal sound changing rules is required. This paper addresses an effective way of vocal sound recognition and synthesis by providing the design and implementation of the Korean vocal sound variation rule. The regulation we followed for the design of the vocal sound variation rule is the Phonetic Standard(Section 30. Chapter 7) of the Korean Orthographic Standards. We have first factor out rules for each regulations, then grouped them into 27 groups for eaeh final-consonant. The Phonological Change Processing System suggested in the paper provides a fast processing ability for vocal sound variation by a single application of the rule. The contents of the process for information augmented to words or the stem of innected words are included in the rules. We believe that the Phonological Change Processing System will facilitate the vocal sound recognition and synthesis by the sentence. Also, this system may be referred as an example for similar research areas.