• Proceedings of the Korean Society for Language and Information Conference
• /
• 1985.02a
• /
• pp.125-152
• /
• 1985

• Proceedings of the KSPS conference
• /
• 1996.10a
• /
• pp.224-228
• /
• 1996

This paper is intended as a study on how an utterance is divided into rhythmic units in Standard Korean with respect to its syntactic structure. With respect to the data in this study I used 150 sentences which contained similar number of words and various syntactic structures. Those sentences were read by 7 speakers of Seoul dialect in a conversation style. Each sentence was read twice in a normal speed and twice in a fast speed. As a total, 4200 sentences were recorded. Then listening to them, the author marked the sentences with two kinds of boundaries i.e. strong and weak. To explore the relationship between rhythmic units and syntactic structure I devised a framework of grammatical symbols. Each symbol is designed to have both syntactic and morphological information at the same time. So I assigned those grammatical symbols to the sentences. Having sentences marked with grammatical symbols on the one hand, and with the rhythmic boundaries on the other hand, 1 could show the relationship between rhythmic units and syntactic structure; which syntactic structures are likely to be pronounced as one rhythmic unit, and which are on the rhythmic boundaries.

• Journal of information and communication convergence engineering
• /
• v.20 no.2
• /
• pp.113-124
• /
• 2022

News in the form of web data generates increasingly large amounts of information as unstructured text. The capability of understanding the meaning of news is limited to humans; thus, it causes information overload. This hinders the effective use of embedded knowledge in such texts. Therefore, Automatic Knowledge Extraction (AKE) has now become an integral part of Semantic web and Natural Language Processing (NLP). Although recent literature shows that AKE has progressed, the results are still behind the expectations. This study proposes a method to auto-extract surface knowledge from English news into a machine-interpretable semantic format (triple). The proposed technique was designed using the grammatical structure of the sentence, and 11 original rules were discovered. The initial experiment extracted triples from the Sri Lankan news corpus, of which 83.5% were meaningful. The experiment was extended to the British Broadcasting Corporation (BBC) news dataset to prove its generic nature. This demonstrated a higher meaningful triple extraction rate of 92.6%. These results were validated using the inter-rater agreement method, which guaranteed the high reliability.

• Language and Information
• /
• v.20 no.1
• /
• pp.1-24
• /
• 2016

Korean is well-known for its topic-prominent properties. In this paper, we look into several subtypes of topic constructions whose grammatical complexities have received much attention in generative grammar. From a semantic/pragmatic view, topics in Korean can be classified into three different types: aboutness, contrastive, and scene-setting. Meanwhile, syntax can classify topic constructions into two types, depending on whether or not the comment clause following topic has a syntactic gap linked to the topic. In this paper, we review some key properties of these topic constructions in Korean, and suggest that each type is licensed by tight interactions between syntactic and semantic constraints. In particular, the paper tries to offer a Construction Grammar analysis where each grammatical component is interacting in non-modular ways and in which the multiple inheritance network of constructions plays an important role in capturing cross-cutting generalizations of the topic constructions.

• Genomics & Informatics
• /
• v.9 no.1
• /
• pp.5-11
• /
• 2011

Treating genomes just as languages raises the possibility of producing concise generalizations about information in biological sequences. Grammars used in this way would constitute a model of underlying biological processes or structures, and that grammars may, in fact, serve as an appropriate tool for theory formation. The increasing number of biological sequences that have been yielded further highlights a growing need for developing grammatical systems in bioinformatics. The intent of this review is therefore to list some bibliographic references regarding the recent progresses in the field of grammatical modeling of biological sequences. This review will also contain some sections to briefly introduce basic knowledge about formal language theory, such as the Chomsky hierarchy, for non-experts in computational linguistics, and to provide some helpful pointers to start a deeper investigation into this field.

• The KIPS Transactions:PartB
• /
• v.15B no.1
• /
• pp.69-72
• /
• 2008

This study aims to identify dependency structures in Korean sentences with the cascaded chunking. In the first stage of the cascade, we find chunks of NP and guess grammatical relations (GRs) using Support Vector Machine (SVM) classifiers for all possible modifier-head pairs of chunks in terms of GR categories as subject, object, complement, adverbial, etc. In the next stages, we filter out incorrect modifier-head relations in each cascade for its corresponding GR using the SVM classifiers and the characteristics of the Korean language such as distance between relations, no-crossing and case property. Through an experiment with a parsed and GR tagged corpus for training the proposed parser, we achieved an overall accuracy of 85.7%.

• Journal of KIISE:Software and Applications
• /
• v.32 no.10
• /
• pp.984-989
• /
• 2005

This study aims to identify grammatical relations (GRs) in Korean sentences. The key task is to find the GRs in sentences in terms of such GR categories as subject, object, and adverbial. To overcome this problem, we are fared with the many ambiguities. We propose a statistical model, which resolves the grammatical relational ambiguity first, and then finds correct noun phrases (NPs) arguments of given verb phrases (VP) by using the probabilities of the GRs given NPs and VPs in sentences. The proposed model uses the characteristics of the Korean language such as distance, no-crossing and case property. We attempt to estimate the probabilities of GR given an NP and a VP with Support Vector Machines (SVM) classifiers. Through an experiment with a tree and GR tagged corpus for training the model, we achieved an overall accuracy of $84.8\%,\;94.1\%,\;and\;84.8\%$ in identifying subject, object, and adverbial relations in sentences, respectively.

• Journal of Convergence for Information Technology
• /
• v.9 no.2
• /
• pp.43-48
• /
• 2019

This study examines the problem of incorrect usage of grammatical terms that are quite common in English grammar teaching process and suggests ways to revise and improve the errors. Parts of speech and sentence elements are indispensable for any grammatical explanation. These grammatical terms are a core part of the grammar, but they are frequently used without being verified correctly and interchangeably with no distinction. These terms refer to different things, and when they are used interchangeably, they cause confusion in the establishment of grammar cognition. In result, there is a crucial need to discuss and improve the definitions of the grammatical terms defined in the English teaching process for proper improvement in effective English education.

• Journal of KIISE:Software and Applications
• /
• v.35 no.5
• /
• pp.313-324
• /
• 2008

A sentence reduction is the information compression process which removes extraneous words and phrases and retains basic meaning of the original sentence. Most researches in the sentence reduction have required a large number of lexical and syntactic resources and focused on extracting or removing extraneous constituents such as words, phrases and clauses of the sentence via the complicated parsing process. However, these researches have some problems. First, the lexical resource which can be obtained in loaming data is very limited. Second, it is difficult to reduce the sentence to languages that have no method for reliable syntactic parsing because of an ambiguity and exceptional expression of the sentence. In order to solve these problems, we propose the sentence reduction method which uses templates and POS(part of speech) information without a parsing process. In our proposed method, we create a new sentence using both Sentence Reduction Templates that decide the reduction sentence form and Grammatical POS-based Reduction Rules that compose the grammatical sentence structure. In addition, We use Viterbi algorithms at HMM(Hidden Markov Models) to avoid the exponential calculation problem which occurs under applying to Sentence Reduction Templates. Finally, our experiments show that the proposed method achieves acceptable results in comparison to the previous sentence reduction methods.

• KIISE Transactions on Computing Practices
• /
• v.22 no.4
• /
• pp.184-188
• /
• 2016

The cleaning robot is popularly used as a home appliance. The state-of-the-art cleaning robot can clean more efficiently by using information gathered from its sensor, which is difficult for low-price cleaning robots due to limitation in this aspect. In this paper, we suggested a method for the moving pattern of cleaning robot based on grammatical evolution. Optimized program is generated by using moving pattern grammar, which is defined by Backus-Naur form. In addition, conditional probability is used between each of the grammar elements during the program creation process. The proposed method is evaluated by robot simulation in order to verify its performance and further compare it with existing algorithms. The experiment results showed that the proposed method is better than the compared algorithms.