• ETRI Journal
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• v.28 no.6
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• pp.803-806
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• 2006

This letter addresses a structural disambiguation method for Korean adverbs based on the correlative relation constraints between adverbs and modifiees, and the morphological context information of sentences. Using the proposed method, we improved the dependency parsing accuracy of adverbs from 79.2 to 89%. The experimental result shows that the proposed method is especially expert in parsing adverbs which can modify multiple word classes or have a long distance dependency relation to their modifiees.

• Korean Journal of Cognitive Science
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• v.4 no.1
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• pp.153-176
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• 1993

Structual ambiguity is one of those problem that arise in the analysis of natural language sentences.It has been considered very difficult to solve the problem.Structural ambiguity,however,should be resolved no matter how difficult it may be.Otherwise natural language processing could be virtually impossible.A statistical approach to structural disambiguation is proposed in this dissertation.The information-theoretic concept of mutual information has been empolyed in resolving structural ambiguity Mutual information can be acquired in an automatic way.from text corpora. If a structural disambiguation subsystem had the capability of self-evaluating whether the results of structural disambiguation are correct or not.it would be possible to develop a more intelligent natural language proessing system.In this paper,the concept of confidence measure is also proposed to endow the disambiguation subsystem with such intelligence.Confidence measure is a numeric value calculated after structural disambiguation. Some experiments were performed in order to show the validity of the approach.Mutual information was auto matically acquired from a corpus of 1.6milion words that were collected from scientific abstracts.The accuracy of structural disambiguation was 80%when performed over 1,639 test sentences.Notice that there was no manual tuning in advance for the experiments.The task of detecting and correcting errors in structural disambiguation will be performed very effectively if the concept of confidence measure is employed in the process.

• Journal of Information Science Theory and Practice
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• v.1 no.3
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• pp.33-46
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• 2013

Despite limited success, today's information retrieval (IR) systems are not intelligent or reliable. IR systems return poor search results when users formulate their information needs into incomplete or ambiguous queries (i.e., weak queries). Therefore, one of the main challenges in modern IR research is to provide consistent results across all queries by improving the performance on weak queries. However, existing IR approaches such as query expansion are not overly effective because they make little effort to analyze and exploit the meanings of the queries. Furthermore, word sense disambiguation approaches, which rely on textual context, are ineffective against weak queries that are typically short. Motivated by the demand for a robust IR system that can consistently provide highly accurate results, the proposed study implemented a novel topic detection that leveraged both the language model and structural knowledge of Wikipedia and systematically evaluated the effect of query disambiguation and topic-based retrieval approaches on TREC collections. The results not only confirm the effectiveness of the proposed topic detection and topic-based retrieval approaches but also demonstrate that query disambiguation does not improve IR as expected.

• Korean Journal of Cognitive Science
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• v.7 no.4
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• pp.141-162
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• 1996

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2000.06a
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• pp.110-115
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• 2000

자연 언어 처리의 구문 구조 분석에서는 중의성 있는 결과가 많이 생성된다. 이러한 중의성을 해소하는데 어휘정보가 유용하다는 것은 잘 알려져 있으며, 이러한 어휘정보와 이를 이용한 중의성 해소에 관한 연구가 많이 이루어지고 있다. 본 논문은 한국어의 구문 구조 분석 시 부사에 의해 발생되는 중의성을 해소하기 위해 수식어 사전을 이용하여 구문 분석에서의 구조 중의성을 해소하였다. 수식어 사전의 어휘정보와 대상 말뭉치를 통해 각각의 부사에 대한 문법을 구성하고, 이를 이용하여 한국어 구문 구조 분석에서 부사에 의해 발생되는 중의성을 줄일 수 있다.

• Journal of KIISE:Software and Applications
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• v.30 no.1_2
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• pp.81-92
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• 2003

Generalized LR parsing is one of the enhanced LR parsing methods so that it overcome the limit of one-way linear stack of the traditional LR parser using graph-structured stack, and it has been playing an important role of a firm starting point to generate other variations for NL parsing equipped with various mechanisms. In this paper, we propose a conditional Action Model that can solve the problems of conventional probabilistic GLR methods. Previous probabilistic GLR parsers have used relatively limited contextual information for disambiguation due to the high complexity of internal GLR stack. Our proposed model uses Surface Phrasal Types representing the structural characteristics of the parse for its additional contextual information, so that more specified structural preferences can be reflected into the parser. Experimental results show that our GLR parser with the proposed Conditional Action Model outperforms the previous methods by about 6-7% without any lexical information, and our model can utilize the rich stack information for syntactic disambiguation of probabilistic LR parser.

• Annual Conference on Human and Language Technology
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• 2000.10d
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• pp.110-115
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• 2000

자연 언어 처리의 구문 분석에서는 중의성 있는 결과가 많이 생성된다. 이러한 중의성을 해소하는데 어휘정보가 유용하다는 것은 잘 알려져 있으며, 이러한 어휘정보와 이를 이용한 중의성 해소에 관한 연구가 많이 이루어지고 있다. 본 논문은 한국어의 구문 구조 분석 시 부사에 의해 발생되는 중의성을 해소하기 위해 수식어 사전을 이용하여 구문 분석에서의 구조 중의성을 해소하였다. 수식어 사전의 어휘정보와 대상 말뭉치를 통해 각각의 부사에 대한 문법을 구성하고, 이를 이용하여 한국어 구문구조 분석에서 부사에 의해 발생되는 중의성을 줄일 수 있다.

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.809-816
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• 2002

Since a natural language has inherently structural ambiguities, one of the difficulties of parsing is resolving the structural ambiguities. Recently, a probabilistic approach to tackle this disambiguation problem has received considerable attention because it has some attractions such as automatic learning, wide-coverage, and robustness. In this paper, we focus on Korean probabilistic parsing model using head co-occurrence. We are apt to meet the data sparseness problem when we're using head co-occurrence because it is lexical. Therefore, how to handle this problem is more important than others. To lighten the problem, we have used the restricted and simplified phrase-structure grammar and back-off model as smoothing. The proposed model has showed that the accuracy is about 84%.

• Korean Journal of Cognitive Science
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• v.13 no.4
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• pp.42-42
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• 2002

An adverb has been generally defined as what modifies verbs or adjectives in Korean, but we can find that some adverbs can modify nouns. These kinds of adverbs lead a structural analysis complicated; therefore, they should be exceptionally processed by a syntactic parser. In this paper, we categorize a noun-modifying adverb and characterize that from a syntactic analysis standpoint. And also, we propose a method to handle noun-modifying adverbs for improving the accuracy of syntactic analysis. By using this proposed method, we can show that the parser increases it′s accuracy from 81.9 to 83.6% on testing corpus.

• Korean Journal of Cognitive Science
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• v.13 no.4
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• pp.43-53
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• 2002

An adverb has been generally defined as what modifies verbs or adjectives in Korean, but we can find that some adverbs can modify nouns. These kinds of adverbs lead a structural analysis complicated; therefore, they should be exceptionally processed by a syntactic parser. In this paper, we categorize a noun-modifying adverb and characterize that from a syntactic analysis standpoint. And also, we propose a method to handle noun-modifying adverbs for improving the accuracy of syntactic analysis. By using this proposed method, we can show that the parser increases it's accuracy from 81.9 to 83.6％ on testing corpus.