• Journal of Korean Medical classics
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• v.24 no.6
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• pp.111-124
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• 2011

The objectives of this study are to look into the grammatical characteristics and find misinterpretations on the translation books. 1. Sentences characteristics 1) Lots of ellipses of grammatical parts can be found such as conjunction, postposition, particle, Coverb, and focus on the parts which has practical meaning such as noun, pronoun, verb, adjective in the sentences. 2) Some predicates are skipped in the later phrases which has contradictive concepts against them of former phrases. 3) Pure Korean word order is exposed especially in complement. 2. Translation fallacy 1) There is fallacy in the sentences omitted paratactic conjunction as follows (1) mistranslation based on the wrong concept of the context between equal relation and subordinate relation. (2) failure on setting up the period, (3) misunderstanding equal relation as cause relation. 2) Some singular phrases, which are condition relation, were analyzed as plural phrases in the sentences omitted connection conjunction. 3) Ellipses of postposition obstruct understanding the difference between modifier and modificand in some sentences. 4) Some cause relation phrases were translated as equality relation due to lack of recognition of ellipsis of coverbs.

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

• ETRI Journal
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• v.32 no.5
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• pp.740-749
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• 2010

In a machine translation system, word sense disambiguation has an essential role in the proper translation of words when the target word can be translated differently depending on the context. Previous research on sense identification has mostly focused on adjacent words as context information. Therefore, in the case of nominal compounds, sense tagging of unit nouns mainly depended on other nouns surrounding the target word. In this paper, we present a practical method for the sense tagging of Korean unit nouns in a nominal compound. To overcome the weakness of traditional methods regarding the data sparseness problem, the proposed method adopts complement-predicate relation knowledge that was constructed for machine translation systems. Our method is based on a sentential form recovery technique, which recognizes grammatical relationships between unit nouns. This technique makes use of the characteristics of Korean predicative nouns. To show that our method is effective on text in general domains, the experiments were performed on a test set randomly extracted from article titles in various newspaper sections.

• Journal of KIISE:Software and Applications
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• v.32 no.10
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• pp.984-989
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• 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 information and communication convergence engineering
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• v.20 no.2
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• pp.113-124
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• 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
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• v.2 no.1
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• pp.214-255
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• 1998

Close examination of previous constraints on bound anaphora that are designed to directly constrain the distribution of referentially dependent(RD) items shows that no universal structural relation may exist that relates RD items to their antecedents. As an alternative to these constraints, this paper proposes an axiom of semantic interpretation, called the Principle of Referential Autonomy, which dispenses with any pretheoretical notion of grammatical functions or configurational notion like c-command. Together with certain English-specific facts, this principle enables us to infer the ungrammaticality of core examples of strong and weak crossover.

• Annual Conference on Human and Language Technology
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• 2008.10a
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• pp.35-41
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• 2008

이 논문은 각 문법 관계(grammatical relation)에서 선호되는 체언 어휘를 파악하고, 이 어휘들의 의미적 유형 및 그 위계를 파악하는 것이 목적이다. 이를 위해 80만 어절의 21세기 세종계획 구문분석 말뭉치에서 그 분포를 추출하고, 통계적 검증을 통해 각 문법 관계에서 선호되는 체언 어휘를 선별한다. 이 연구에서 관찰하는 문법 관계는 주어, 목적어, 용언수식어로 하며, 이들 문법 관계에서 선호되는 어휘 추출 대상 품사는 대명사, 고유명사, 일반명사로 한다. 한정성의 강도에 따라 주어 분포 경향이 나타나며, 이에 따라 대명사 > 고유명사 > 일반명사 순으로 주어 분포 경향이 나타난다. 그러나 일반적 예측과 다르게 한정성의 강도가 더 강한 것으로 알려진 대명사가 고유명사보다 목적어와 용언수식어에서 분포 경향이 더 강하여, 일반명사 > 대명사 > 고유명사의 순으로 분포 경향이 나타난다. 대명사, 고유명사, 일반명사는 공통적으로 주어에서는 사람 지시어, 목적어에서는 사물과 장소 지시어, 그리고 용언수식어에서는 시공간 표현이 선호되어 분포한다. 특히 대명사는 각 문법기능에서 인칭대명사의 경우 인칭에 따라, 그리고 지시대명사의 경우 원근칭에 따라 선호도의 차이를 보인다. 이러한 체언 어휘의 의미적 분포 특성은 문법 관계에 통사적 기능 외에도 의미적 경향이 반영된 것으로 고려될 수 있다.

• Journal of architectural history
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• v.8 no.3 s.20
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• pp.39-51
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• 1999

The purpose of this study is to establish the theory of the spatial composition that is 'The Structure of Relation' in Korean traditional houses. This study has been focused on the spatial composition of Hyangdan. The form composition is an element defining primitive functions and space and producing aesthetical effects, where 'quality as a substance' is not important but 'quality as a relation' is essential. Quality as a meaningful relationship comes from realization and regulation of visual attributes among form elements. Each form compositional element establishes the hierarchical structure logically with the entire order, In order for the quality of all form compositional elements to be clarified as a meaningful relationship logically, 'compositional concept' which combines a series of form elements into the grammatical dependence to a specific direction, has to be assumed. If the intrinsic relationship among a series of form compositional elements fails to confront with the 'contextual concept' which eventually indicates unique conditions for a place according to the refined compositional concept, the result of form composition never leads to a specific solution

• English Language & Literature Teaching
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• v.13 no.2
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• pp.47-61
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• 2007

The purpose of this paper is to survey the major elements that have changed English from a synthetic language to an analytic one. Therefore, this paper has looked at the differences between synthetic languages and analytic ones. In synthetic languages, the relation of words in a sentence is synthetically determined by means of inflections, while in analytic languages, the functions of words in a sentence are analytically determined by means of word order and function words. Thus, Old English with full inflectional systems shows the synthetic nature. However, in the course of time, Old English inflections came to be lost by phonetic changes and operation, which made English dependent on word order and function words to signal the relation of words in a sentence. The major phonetic changes that have shifted English are the change of final /m/ to /n/, the leveling of unstressed vowels, the loss of final /n/, and the decay of schwa in final syllables. These changes led to reduction of inflections of English as well as the loss of grammatical gender. The operation of analogy, the tendency of language to follow certain patterns and to adapt a less common form to a more familiar one, has also played an important role in changing English.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.205-213
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• 2010

We introduce a natural language sentence generation (NLG) method based on learning of word-association patterns. Existing NLG methods assume the inherent grammar rules or use template based method. Contrary to the existing NLG methods, the presented method learns the words-association patterns using only the co-occurrence of words without additional information such as tagging. We employ the hypernetwork method to analyze and represent the words-association patterns. As training going on, the model complexity is increased. After completing each training phase, natural language sentences are generated using the learned hyperedges. The number of grammatically plausible sentences increases after each training phase. We confirm that the proposed method has a potential for learning grammatical properties of training corpuses by comparing the diversity of grammatical rules of training corpuses and the generated sentences.