• Journal of Digital Contents Society
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• v.15 no.5
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• pp.579-586
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• 2014

This paper analyzes the recognition of driver's behavior in lane change using context-free grammar. In contrast to conventional pattern recognition techniques, context-free grammars are capable of describing features effectively that are not easily represented by finite symbols. Instead of coordinate data processing that should handle features in multiple concurrent events respectively, effective syntactic analysis was applied for patterning of symbolic sequence. The findings proposed the effective and intuitive method for drivers and researchers in driving safety field. Probabilistic parsing for the improving this research will be the future work to achieve a robust recognition.

• Journal of Multimedia Information System
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• v.9 no.2
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• pp.93-102
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• 2022

Dependency parsing is a decision problem of the syntactic relation between words in a sentence. Recently, deep learning models are used for dependency parsing based on the word representations in a continuous vector space. However, it causes a mislabeled tagging problem for the proper nouns that rarely appear in the training corpus because it is difficult to express out-of-vocabulary (OOV) words in a continuous vector space. To solve the OOV problem in dependency parsing, we explored the proper noun embedding method according to the embedding unit. Before representing words in a continuous vector space, we replace the proper nouns with a special token and train them for the contextual features by using the multi-layer bidirectional LSTM. Two models of the syllable-based and morpheme-based unit are proposed for proper noun embedding and the performance of the dependency parsing is more improved in the ensemble model than each syllable and morpheme embedding model. The experimental results showed that our ensemble model improved 1.69%p in UAS and 2.17%p in LAS than the same arc-eager approach-based Malt parser.

• ETRI Journal
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• v.46 no.1
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• pp.35-47
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• 2024

This study introduces CR-M-SpanBERT, a coreference resolution (CR) model that utilizes multiple embedding-based span bidirectional encoder representations from transformers, for antecedent recognition in natural language (NL) text. Information extraction studies aimed to extract knowledge from NL text autonomously and cost-effectively. However, the extracted information may not represent knowledge accurately owing to the presence of ambiguous entities. Therefore, we propose a CR model that identifies mentions referring to the same entity in NL text. In the case of CR, it is necessary to understand both the syntax and semantics of the NL text simultaneously. Therefore, multiple embeddings are generated for CR, which can include syntactic and semantic information for each word. We evaluate the effectiveness of CR-M-SpanBERT by comparing it to a model that uses SpanBERT as the language model in CR studies. The results demonstrate that our proposed deep neural network model achieves high-recognition accuracy for extracting antecedents from NL text. Additionally, it requires fewer epochs to achieve an average F1 accuracy greater than 75% compared with the conventional SpanBERT approach.

• Proceedings of the Korean Information Science Society Conference
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• 2003.04a
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• pp.539-541
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• 2003

본 논문에서는 컨텐츠 사이트에서 디지털 컨텐츠를 보호하기 위하여 사용자 행동 패턴을 분석을 이용해 특이한 성향을 보이는 사용자를 탐지하기 위한 모델을 제시하였다. 사용자의 행동 패턴을 분석하기 위한 탐지 규칙(detection rule)으로 Syntactic Rule과 Semantic Rule을 정의하였다. 사용자 로그 분석 결과 탐지 규칙에 대한 위반 정도가 일정 범위를 벗어나는 사용자를 비정상적인 사용자로 추정하였다. 또한 제안 모델은 eCRM 시스템에서 이탈 가능성이 있는 고객 집단을 사전에 탐지하여 고객으로 유지하기 위한 promotion 전략 수립에 응용될 수 있다.

• The Journal of the Acoustical Society of Korea
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• v.18 no.7
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• pp.17-22
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• 1999

In this paper we propose a new mixed method of LDA and tri-tone model to predict Korean prosodic break indices(PBI) for a given utterance. PBI can be used as an important cue of syntactic discontinuity in continuous speech recognition(CSR). The model consists of three steps. At the first step, PBI was predicted with the information of syllable and pause duration through the linear discriminant analysis (LDA) method. At the second step, syllable tone information was used to estimate PBI. In this step we used vector quantization (VQ) for coding the syllable tones and PBI is estimated by tri-tone model. In the last step, two PBI predictors were integrated by a weight factor. The proposed method was tested on 200 literal style spoken sentences. The experimental results showed 72% accuracy.

• Phonetics and Speech Sciences
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• v.7 no.4
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• pp.41-47
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• 2015

This paper examines whether the Global Vector model is applicable to Korean data as a universal learning algorithm. The main purpose of this study is to compare the global vector model (GloVe) with the word2vec models such as a continuous bag-of-words (CBOW) model and a skip-gram (SG) model. For this purpose, we conducted an experiment by employing an evaluation corpus consisting of 70 target words and 819 pairs of Korean words for word similarities and analogies, respectively. Results of the word similarity task indicated that the Pearson correlation coefficients of 0.3133 as compared with the human judgement in GloVe, 0.2637 in CBOW and 0.2177 in SG. The word analogy task showed that the overall accuracy rate of 67% in semantic and syntactic relations was obtained in GloVe, 66% in CBOW and 57% in SG.

• Speech Sciences
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• v.8 no.4
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• pp.185-191
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• 2001

This study was designed to determine what components of spoken language have been relatively neglected in the teaching of listening comprehension in Korea and to suggest a model of listening process. Two types of tests were undertaken using spoken and written forms of English with secondary school teachers of English and college students. Findings: Hearing power has been generally neglected in the teaching of listening comprehension. Hearing power which can be thought as an active process is defined as an ability to transfer the sequence of discrete phonetic segments without word boundary into the sequence of words in phonemic representations by using both nonlinguistic factors and linguistic factors including perception rules based on phonetics and phonology. Vocabularies, hearing-speaking power, syntactic structures and idiomatic expressions are to be taught for spoken English. A model of listening process was suggested and discussed.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.50-56
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• 2003

In the development of complex software system, it is important to use hierarchical use case model due to the complex scope of development procedure. The use case model is core factor of the OMG (Object Management Group)'s UML (Unified Modeling Language) diagrams. In this paper, we propose a novel method to check syntactic consistency automatically in use case models at the different level of abstraction. This method is a rule-based approach which utilizes actor tree, use case tree and use case description. The proposed method is simulated on ITS (Intelligent Transportation System) architecture for the verification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4174-4190
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• 2019

IEC 62304 is a standard for the medical device software lifecycle. Developers must develop software that complies with all specifications in the standard for licensing. However, because the standard contains not only a large number of specifications, but also domain-specific information and association relationships between specifications, it requires considerable effort and time for developers to understand and interpret the standard. To support developers, this paper presents a method for extracting the contents of the IEC 62304 standard as a goal model, which is the core methodologies of requirements engineering. The proposed method analyzes the grammar of the standard to robustly extract complex structures and various information from standard specifications and define rules that extract goals and links from syntactic element units. We validated the actual extraction process for the standard document experimentally. Based on the extracted goal model, developers can intuitively and efficiently comply with the standard and track specific information within the medical software and standard domains.

• Korean Journal of Cognitive Science
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• v.17 no.2
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• pp.103-118
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• 2006

The main purpose of this paper is to investigate the effects of discourse topic and morpho-syntactic verbal information on the resolution of null pronouns in the Korean narrative text within the framework of the construction-integration theory (Kintsch, 1988, Singer & Kintsch, 2001, Graesser, Gernsbacher, & Goldman. 2003). For the purpose of this paper, two conditions were designed: an explicit condition with both a consistently maintained discourse topic and the person-specific verb modals on one hand, and a neutral condition with no discourse topic or morpho-syntactic information provided, on the other. We measured the reading tines far the target sentence containing a null pronoun and the question response times for finding an antecedent, and the accuracy rates for finding an antecedent. During the experiments each passage was presented at a tine on a computer-controlled display. Each new sentence was presented on the screen at the moment the participant pressed the button on the computer keyboard. Main findings indicate that processing is facilitated by macro-structure (topicality) in conjunction with micro-structure (morpho-syntax) in pronoun interpretation. It is speculated that global processing alone may not be able to determine which potential antecedent is to be focused unless aided by lexical information. It is argued that the results largely support the resonance-based model, but not the minimalist hypothesis.