• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2001년도 학술대회 논문집
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• pp.237-257
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• 2001

This paper investigates the idiosyncratic understanding of universal quantifiers such as every, each or all by young children at the ages of 4 to 7, and argues that the phenomenon is explicable in terms of the maturation of both the cognitive system and the linguistic system. Evidence for this dual explanation comes from the fact that the visual input, a picture, plays a key role in determining the children’s conceptual representation, suggesting the need for the central integration of visual and linguistic elements; and from the fact that a quantifier in the linguistic input has an intrinsic property, i.e. a <+focus> feature. I have tried to explain the nature of the cognitive factors in terms of the function of the central system, suggesting a modified form of Smith & Tsimpli’s (1995) version of Fodor’s (1983) modularity hypothesis. Conceptual representations of two kinds are in competition with each other and they are integrated into a neutral LOT (Language of Thought) representation at some point . In the process of this integration, the representations from the visual input predominate over those from the auditory input, though the quantize. (treated as new information provided by the latter) is salient in the final representations. When visual conceptual representations predominate over purely linguistic ones, quantifier spreading errors occur. By contrast, when the relevant grammatical knowledge has developed sufficiently to counteract the conceptual representations, this peculiar behaviour by children should disappear. It is argued that children have to learn two kinds of grammatical fact with regard to universal quantification: (i) they have to learn the status of the quantifier as a functional head of DP so that it has to be positioned inside DP; and (ii) they have to learn the Left-Branch Condition which specifies that movement of an element in the left-branch position is possible only by pied-piping the entire phrase.

• 대한전자공학회논문지
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• 제26권12호
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• pp.1975-1983
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• 1989

Implementing the linguistic theories on computer, we resolve the problems for restrictions of computer and increase processing efficiency for systemization not for linguistic theory itself. Thus, we modify the grammatical theory to be applied to systems. This paper reports the various problems about constructing dictionaies, defining rules, and appling universal principles and metarules, which is caused to implement the systems based on GPSG. In semantic interpretations, logical expressions which correspond Montague grammar are acquired, and we make a rule connect with several logical expressions. And we show the efficiency of the this method through implementing parser.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권3호
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• pp.205-213
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• 2010

본 논문에서는 단어간 관계 패턴을 학습한 후 이에 기반하여 자연 언어 문장을 생성하는 방법을 소개한다. 기존의 문장 생성 방법론에서는 내재된 문법 규칙의 존재를 가정하거나 템플릿을 사용하고 있으나, 본 논문에서 소개하는 방법론에서는 태깅 등의 부가 정보 없이 단어의 동시 등장 빈도만을 활용하여 단어간 관계 패턴을 학습한다. 단어간 관계 패턴은 하이퍼네트워크 방법론에 기반하여 학습되었다. 학습이 진행됨에 따라 하이퍼네트워크의 복잡도가 높아지며, 학습 모델에 축적되는 언어 관계 패턴의 수가 증가한다. 학습된 모텔의 유효성은 학습 패턴에 기반한 자연 언어 문장 생성을 통해 확인하였다. 실험 결과 학습이 진행됨에 따라 문법적으로 성립하는 문장의 비율이 향상하였다. 파서를 이용하여 생성된 문장을 구성하는 문법 규칙을 분석한 후 문법 규칙의 분포를 학습에 사용한 코퍼스의 문법 규칙 분포와 비교한 결과 학습에 사용된 코퍼스의 문법적 특성을 학습할 수 있는 잠재력을 갖고 있음을 확인하였다.

• International Journal of Contents
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• 제1권2호
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• pp.39-43
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• 2005

In this paper, we describe a generation mechanism of semantic-based queries for high accuracy information retrieval and question answering. It is difficult to offer the correct retrieval result because general information retrieval systems do not analyze the semantic of user's natural language question. We analyze user's question semantically and extract semantic features, and we .generate semantic-based queries using them. These queries are generated using the se-mantic-based question analysis grammar and the query generation rule. They are represented as semantic features and grammatical morphemes that consider semantic and syntactic structure of user's questions. We evaluated our mechanism using 100 questions whose answer type is a person in the TREC-9 corpus and Web. There was a 0.28 improvement in the precision at 10 documents when semantic-based queries were used for information retrieval.

• 대한음성학회지:말소리
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• 제38호
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• pp.1-24
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• 1999

There is an indisputable connection between prosody and focus. The focal prominence in Korean, a prosodic realization of pitch prominence in an utterance, defines a focused constituent, the domain of which is identified by the Focus Identification Principle. To this is added the Basic Focus Rule which makes it possible to capture and interpret the focal domain, which can then be tested against the available context. The focal domain can be contextually made available by setting it off with information structure boundaries(I/S) identified by the Information Structure Identification Principle. The fragment of the utterance enclosed within the IS boundaries can be recognized as 'new' information with the help of the Focus Domain Identification Rule. Since information structures are pragmatically tied to semantic levels of grammatical systems, the Basic Focus Rule is now replaced by the Focal Prominence Principle ensuring the focal prominence within the focal domain. Close relationships exist between patterns of intonation and their expressiveness in terms of giving a pragmatically-oriented description of focus. This is particularly manifested in Korean sentences containing contrastiveness.

• 한국언어정보학회지:언어와정보
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• 제19권1호
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• pp.19-36
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• 2015

Honorific agreement is one of the main properties in languages like Korean, playing a pivotal role in appropriate communication. This makes the deep processing of honorific information crucial in various computational applications such as spoken language translation and generation. This paper shows that departing from previous literature, an adequate analysis of Korean honorification needs to involve a system that has access not only to morpho-syntax but to semantics and pragmatics as well. Along these lines, this paper offers a constraint-based HPSG analysis of Korean honorification in which the enriched lexical information tightly interacts with syntactic, semantic, and pragmatic levels for the proper honorific system.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2007년도 한국음성과학회 공동학술대회 발표논문집
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• pp.135-137
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• 2007

This study has experimented and materialized a computational lexical processing model which hybridizes full model and decomposition model as applying lexical acquisition, one of early stages of human lexical processes, to Korean. As the result of the study, we could simulate the lexical acquisition process of linguistic input through experiments and studying, and suggest a theoretical foundation for the order of acquitting certain grammatical categories. Also, the model of this study has shown proofs with which we can infer the type of the mental lexicon of the human cerebrum through fu1l-list dictionary and decomposition dictionary which were automatically produced in the study.

• 한국언어정보학회지:언어와정보
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• 제14권1호
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• pp.1-24
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• 2010

The complexity of comparative constructions in each language has given challenges to both theoretical and computational analyses. This paper first identifies types of comparative constructions in Korean and discusses their main grammatical properties. It then builds a syntactic parser couched upon the typed feature structure grammar, HPSG and proposes a context-dependent interpretation for the comparison. To check the feasibility of the proposed analysis, we have implemented the grammar into the existing Korean Resource Grammar. The results show us that the grammar we have developed here is feasible enough to parse Korean comparative sentences and yield proper semantic representations though further development is needed for a finer model for contextual information.

• Journal of Multimedia Information System
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• 제5권3호
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• pp.147-154
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• 2018

Natural language processing (NLP) is an emerging research area in which we study how machines can be used to perceive and alter the text written in natural languages. We can perform different tasks on natural languages by analyzing them through various annotational tasks like parsing, chunking, part-of-speech tagging and lexical analysis etc. These annotational tasks depend on morphological structure of a particular natural language. The focus of this work is part-of-speech tagging (POS tagging) on Hindi language. Part-of-speech tagging also known as grammatical tagging is a process of assigning different grammatical categories to each word of a given text. These grammatical categories can be noun, verb, time, date, number etc. Hindi is the most widely used and official language of India. It is also among the top five most spoken languages of the world. For English and other languages, a diverse range of POS taggers are available, but these POS taggers can not be applied on the Hindi language as Hindi is one of the most morphologically rich language. Furthermore there is a significant difference between the morphological structures of these languages. Thus in this work, a POS tagger system is presented for the Hindi language. For Hindi POS tagging a hybrid approach is presented in this paper which combines "Probability-based and Rule-based" approaches. For known word tagging a Unigram model of probability class is used, whereas for tagging unknown words various lexical and contextual features are used. Various finite state machine automata are constructed for demonstrating different rules and then regular expressions are used to implement these rules. A tagset is also prepared for this task, which contains 29 standard part-of-speech tags. The tagset also includes two unique tags, i.e., date tag and time tag. These date and time tags support all possible formats. Regular expressions are used to implement all pattern based tags like time, date, number and special symbols. The aim of the presented approach is to increase the correctness of an automatic Hindi POS tagging while bounding the requirement of a large human-made corpus. This hybrid approach uses a probability-based model to increase automatic tagging and a rule-based model to bound the requirement of an already trained corpus. This approach is based on very small labeled training set (around 9,000 words) and yields 96.54% of best precision and 95.08% of average precision. The approach also yields best accuracy of 91.39% and an average accuracy of 88.15%.

• 한국멀티미디어학회논문지
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• 제23권9호
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• pp.1191-1200
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• 2020

In this paper, we proposed DEVS based programming education model that based on the cognitive information processing theory, not a grammatical programming education, and studied effectiveness analysis using computer thinking patterns. By creating a small range of patterns in the grammar which underlies the programming language and solving various examples through combinations, this paper shows an education method to develop problem-solving skills based on algorithmic thinking. The purpose of this study is to facilitate non-majors learn programming languages and understand patterned program structures when writing programs by patterning of control statements which the most important in learning programming.