• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.491-500
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• 2004

This paper presents an ontology construction method using various computational language resources, and an ontology-based word sense disambiguation method. In order to acquire a reasonably practical ontology the Kadokawa thesaurus is extended by inserting additional semantic relations into its hierarchy, which are classified as case relations and other semantic relations. To apply the ontology to disambiguate word senses, we apply the previously-secured dictionary information to select the correct senses of some ambiguous words with high precision, and then use the ontology to disambiguate the remaining ambiguous words. The mutual information between concepts in the ontology was calculated before using the ontology as knowledge for disambiguating word senses. If mutual information is regarded as a weight between ontology concepts, the ontology can be treated as a graph with weighted edges, and then we locate the weighted path from one concept to the other concept. In our practical machine translation system, our word sense disambiguation method achieved a 9% improvement over methods which do not use ontology for Korean translation.

• KIISE Transactions on Computing Practices
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• v.22 no.2
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• pp.107-112
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• 2016

The Sejong Electronic(machine-readable) Dictionary, developed by the 21st century Sejong Plan, contains systematically organized information on Korean words. It helps to solve problems encountered in the electronic formatting of the still-commonly-used hard-copy dictionary. The Sejong Electronic Dictionary, however has a limitation relate to sentence structure and selection-restricted nouns. This paper discuses the limitations of word-sense disambiguation(WSD) that uses subcategorization information suggested by the Sejong Electronic Dictionary and generalized selection-restricted nouns from the Korean Lexico-semantic network. An alternative method that utilized semi-supervised learning, the chi-square test and some other means to make WSD decisions is presented herein.

• The Transactions of the Korea Information Processing Society
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• v.1 no.1
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• pp.129-139
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• 1994

This paper presents a morphological analysis method for the Korean language. The characteristics and adjacency information of the words can be obtained from sentences in a large corpus. Generally a word can be analyzed to a result by applying the adjacency attributes and rules. However, we have to choose one from the several results for the ambiguous words. The collected morpheme's adjacency attributes and relations with neighbor words are recorded in a well designed dictionaries. With this information, abbreviated words as well as ambiguous words can be almost analyzed successfully. Efficiency of morphological analyzer depends on the information in the dictionaries. A morpheme dictionary and a phrase dictionary have been designed with lexical database, and necessary information extracted from the corpus is stored in the dictionaries.

• The KIPS Transactions:PartB
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• v.18B no.4
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• pp.241-248
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• 2011

Homographs can have multiple senses. In order to understand the meaning of a sentence, it is necessary to identify which sense isused for each word in the sentence. Previous researches on this problem heavily relied on the word co-occurrence information. However, we noticed that in case of verbs, information about subordinating cases of verbs can be utilized to further improve the performance of word sense disambiguation. Different senses require different sets of subordinating cases. In this paper, we propose the verb sense disambiguation using subordinating case information. The case information acquire postposition features in Standard Korean Dictionary. Our experiment on 12 high-frequency verb homographs shows that adding case information can improve the performance of word sense disambiguation by 1.34%, from 97.3% to 98.7%. The amount of improvement may seem marginal, we think it is meaningful because the error ratio reduced to less than a half, from 2.7% to 1.3%.

• Annual Conference on Human and Language Technology
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• 2002.10e
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• pp.16-23
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• 2002

본 논문에서는 아라비안 숫자를 포함한 텍스트를 음성으로 합성하기 위하여, 숫자 형태와 분류사 그리고 숫자가 나오는 문맥에 따라 숫자를 자동으로 문자화할 수 있는 전처리 규칙을 설정하는데 목적을 둔다. 먼저 선행연구를 통해 숫자를 포함한 수사 및 수사표현의 읽기 규칙의 적용 범위 및 한계점을 살펴보고, 음성 합성을 위한 아라비안 숫자의 문자화 규칙을 설정하고자 한다. 현대 한국어에서 아라비안 숫자를 읽는 방식은 크게 고유어 방식과 한자어 방식이 있으며 단(單)단위에서는 영어가 사용되기도 한다. 또한 한자어 방식에서도 단위를 붙여 읽는 경우와 모든 수를 단 단위로 읽는 경우가 있으므로, 아라비안 숫자의 문자화를 단순한 규칙을 설정하여 자동화하기에는 중의성이 높다. 본 연구에서는 (1) 숫자 전 전치어(pre-numeral), (2) 기호를 포함한 숫자열의 표현 형식과 크기, (3) 단위 표현, (4) 숫자 후치어(post-numeral), (5) 분류사(classifier) (6) 분류사 후치어(post-classifier), (7) 수사표현 앞뒤 문맥에 따라, 아라비안 숫자표현이 문자화되는 방식을 살펴보았다. 분석 대상 말뭉치는 C 신문의 2000년 1월부터 2000년 4월까지 전체 기사 1,400건에서 숫자가 포함된 숫자표현 약 63,000개론 구성하였다. 패턴화된 구조 및 중의성이 없는 구조를 12가지로 밝히고 중의성이 있는 구조의 유형을 밝혔으며 분류사 후치어와의 결합 관계, 좌우 문맥정보를 통해 중의성 해결의 단서를 제시하고자 하였다.

• Journal of KIISE
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• v.44 no.8
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• pp.813-821
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• 2017

Entity Linking find the meaning of an entity mention, which indicate the entity using different expressions, in a user's query by linking the entity mention and the entity in the knowledge base. This task has four challenges, including the difficult knowledge base construction problem, multiple presentation of the entity mention, ambiguity of entity linking, and NIL entity recognition. In this paper, we first construct the entity name dictionary based on Wikipedia to build a knowledge base and solve the multiple presentation problem. We then propose various methods for NIL entity recognition and solve the ambiguity of entity linking by training the support vector machine based on several features, including the similarity of the context, semantic relevance, clue word score, named entity type similarity of the mansion, entity name matching score, and object popularity score. We sequentially use the proposed two methods based on the constructed knowledge base, to obtain the good performance in the entity linking. In the result of the experiment, our system achieved 83.66% and 90.81% F1 score, which is the performance of the NIL entity recognition to solve the ambiguity of the entity linking.

• Journal of KIISE:Software and Applications
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• v.33 no.11
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• pp.986-998
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• 2006

There has been an increasing necessity of Semantic Web-based metadata that helps computers efficiently understand and manage an information increased with the growth of Internet. However, it seems inevitable to face some semantically ambiguous information when metadata is generated. Therefore, we need a solution to this problem. This paper proposes a new method for automated metadata generation with the help of a concept of class, in which some ambiguous words imbedded in information such as documents are semantically more related to others, by using probability model of consequent words. We considers ambiguities among defined concepts in ontology and uses the Hidden Markov Model to be aware of part of a named entity. First of all, we constrict a Markov Models a better understanding of the named entity of each class defined in ontology. Next, we generate the appropriate context from a text to understand the meaning of a semantically ambiguous word and solve the problem of ambiguities during generating metadata by searching the optimized the Markov Model corresponding to the sequence of words included in the context. We experiment with seven semantically ambiguous words that are extracted from computer science thesis. The experimental result demonstrates successful performance, the accuracy improved by about 18%, compared with SemTag, which has been known as an effective application for assigning a specific meaning to an ambiguous word based on its context.

• Journal of Information Management
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• v.42 no.2
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• pp.193-210
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• 2011

The study was applied to context window sizes and weighting method to obtain the best performance of word sense disambiguation using support vector machine. The context window sizes were used to a 3-word, sentence, 50-bytes, and document window around the targeted word. The weighting methods were used to Binary, Term Frequency(TF), TF ${\times}$ Inverse Document Frequency(IDF), and Log TF ${\times}$ IDF. As a result, the performance of 50-bytes in the context window size was best. The Binary weighting method showed the best performance.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.8
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• pp.1705-1712
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• 2009

Recently, most Korean syntactic analysis systems use Dependency Grammar, because it is quite good to analysis of Korean language structures. But Dependency Grammar makes many ambiguities during syntax analysis of Korean. We implement a system which decreases many ambiguities in syntax analysis. To decrease ambiguities we suggest several methods. First, we use about 200 dependency rules, second, we suggest a new segmentation method and third, one predicate can not have more than one subject or object. Using these methods, we can reduce many ambiguities in Korean syntactic analysis.

• 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%.