• Journal of the Korean Society for information Management
• /
• v.24 no.1 s.63
• /
• pp.321-342
• /
• 2007

This study presents an effective word sense disambiguation model that does not require manual sense tagging Process by automatically tagging the right sense using a machine-readable and the collocation co-occurrence-based methods. The dictionary information-based method that applied multiple feature selection showed the tagging accuracy of 70.06%, and the collocation co-occurrence-based method 56.33%. The sense classifier using the dictionary information-based tagging method showed the classification accuracy of 68.11%, and that using the collocation co-occurrence-based tagging method 62.09% The combined 1a99ing method applying data fusion technique achieved a greater performance of 76.09% resulting in the classification accuracy of 76.16%.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.6
• /
• pp.747-751
• /
• 2010

This paper proposes the method that increases the accuracy for tagging word meaning by creating sense tagged data automatically using machine readable dictionaries. The concept of meaning group is applied here, where the meaning group for each meaning of a target word consists of neighbor words of the target word. To enhance the tagging accuracy, the notion of concentration is used for the weight of each word in a meaning group. The tagging result in SENSEVAL-2 data shows that accuracy of the proposed method is better than that of existing ones.

• Proceedings of the Korean Information Science Society Conference
• /
• 2000.10b
• /
• pp.179-181
• /
• 2000

한국어의 통사적, 의미적 중의성 해결을 위해 하위범주화 사전을 구축하였다. 용언에 따라 제한될 수 있는 문형 패턴과 의미역(semantic roles) 정보의 표준을 정하여 이를 부가하였고 구축한 하위범주화 사전이 명사에 대한 의미를 갖고 있는 계층 시소러스 의미사전과 연동하도록 용언과 명사와의 의미적 연어 관계에 따라 의미마커를 부여했다. 논문에서 구현된 하위범주화 사전이 구문과 어휘의 중의성을 어느 정도 해소하는지 확인하기 위해 반자동적으로 의미 태깅(Sense Tagging)된 말뭉치와 구문분석된 말뭉치를 통해 검증 작업을 수행했다. 이 과정에서 자동으로 하위범주 패턴에 대한 빈도 정보나, 연어정보, 각 의미역과 용언의 통계적 공기 정보 등을 추출하여 하위범주화사전에 추가시켰다. 또한 여기서 얻은 정보를 기준으로 하위범주화 사전을 자동으로 확장하는 알고리즘을 적용하여 확장시켰다.

• KIPS Transactions on Software and Data Engineering
• /
• v.8 no.5
• /
• pp.213-222
• /
• 2019

Determining the meaning of a keyword in a speech dialogue system is an important technology for the future implementation of an intelligent speech dialogue interface. After extracting keywords to grasp intention from user's utterance, the intention of utterance is determined by using the semantic mark of keyword. One keyword can have several semantic marks, and we regard the task of attaching the correct semantic mark to the user's intentions on these keyword as a problem of word sense disambiguation. In this study, about 23% of all keywords in the corpus is manually tagged to build a semantic mark dictionary, a synonym dictionary, and a context vector dictionary, and then the remaining 77% of all keywords is automatically tagged. The semantic mark of a keyword is determined by calculating the context vector similarity from the context vector dictionary. For an unregistered keyword, the semantic mark of the most similar keyword is attached using a synonym dictionary. We compare the performance of the system with manually constructed training set and semi-automatically expanded training set by selecting 3 high-frequency keywords and 3 low-frequency keywords in the corpus. In experiments, we obtained accuracy of 54.4% with manually constructed training set and 50.0% with semi-automatically expanded training set.

• The Journal of the Korea Contents Association
• /
• v.12 no.7
• /
• pp.463-472
• /
• 2012

For last decade, the amount of information has been increased rapidly because of the internet and computing technology development, mobile devices and sensors, and social networks like facebook or twitter. People who want to gain important knowledge from database have been frustrated with large database. Many studies for automatic knowledge extracting meaningful knowledge from large database have been fulfilled. In that sense, automatic knowledge extracting with computing technology has been highly significant in information technology field, but still has many challenges to go further. In order to improve the effectives and efficiency of knowledge extracting system, test collection is strongly necessary. In this research, we introduce a test collection for automatic knwoledge extracting. We name the test collection KEEC/KREC(KISTI Entity Extraction Collection/KISTI Relation Extraction Collection) and present the process and guideline for building as well as the features of. The main feature is to tag by experts to guarantee the quality of collection. The experts read documents and tag entities and relation between entities with a tool for tagging. KEEC/KREC is being used for a research to evaluate system performance and will continue to contribute to next researches.