• Journal of KIISE
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• v.44 no.9
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• pp.946-953
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• 2017

Semantic Role Decision defines the semantic relationship between the predicate and the arguments in natural language processing (NLP) tasks. The semantic role information and semantic category information should be used to make Semantic Role Decisions. The Sejong Electronic Dictionary contains frame information that is used to determine the semantic roles. In this paper, we propose a method to extend the Sejong electronic dictionary using word embedding and synonyms. The same experiment is performed using existing word-embedding and retrofitting vectors. The system performance of the semantic category assignment is 32.19%, and the system performance of the extended semantic category assignment is 51.14% for words that do not appear in the Sejong electronic dictionary of the word using the word embedding. The system performance of the semantic category assignment is 33.33%, and the system performance of the extended semantic category assignment is 53.88% for words that do not appear in the Sejong electronic dictionary of the vector using retrofitting. We also prove it is helpful to extend the semantic category word of the Sejong electronic dictionary by assigning the semantic categories to new words that do not have assigned semantic categories.

• The Journal of Society for e-Business Studies
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• v.7 no.1
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• pp.141-152
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• 2002

Data dictionary is a collection of meta-data, which describes data produced and consumed while performing business processes. Data dictionary is an essential element for business process standardization and automation, and has a fundamental role in ERP application management and customization. Also, data dictionary facilitates B2B processes by enabling painless integration of business processes between various enterprises. We implemented data dictionary support in SEA＋, a component- based scalable ERP system developed in ETRI, and found out that it's a plausible feature of business information system. We discovered that data dictionary promotes semantic, not syntactic, data management, which can make it possible to leverage viability of the tool in the coming age of more meta-data oriented computing world. We envision that business data dictionary is a firm foundation of adapting business knowledge, applications and processes into the semantic web based enterprise infra-structure.

• Language and Information
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• v.9 no.1
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• pp.19-34
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• 2005

Ontology is an indispensable component in intelligent and semantic processing of knowledge and information, such as in semantic web. However, ontology construction requires vast amount of data collection and arduous efforts in processing these un-structured data. This study proposed a methodology to automatically construct and generate ontologies from Sejong Electronic Dictionary. As Sejong Electronic Dictionary is structured in XML format, it can be processed automatically by computer programmed tools into an OWL(Web Ontology Language)-based ontologies as specified in W3C . This paper presents the process and concrete application of this methodology.

• Journal of IKEEE
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• v.13 no.2
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• pp.241-247
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• 2009

This paper suggests the expansion methods of semantic dictionary, taking Korean semantic features account. These methods will be used to extract a practical topic word in the automatic summarization. The first is the method which is constructed the synonym dictionary for improving the performance of semantic-marker analysis. The second is the method which is extracted the probabilistic information from the subcategorization dictionary for resolving the syntactic and semantic ambiguity. The third is the method which is predicted the subcategorization patterns of the unregistered predicate, for the resolution of an affix-derived predicate.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.63-76
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• 2012

We propose a Korean compound noun semantic tagging system using statistical compound noun decomposition and semantic relation information extracted from a lexical semantic network(U-WIN) and dictionary definitions. The system consists of three phases including compound noun decomposition, semantic constraint, and semantic tagging. In compound noun decomposition, best candidates are selected using noun location frequencies extracted from a Sejong corpus, and re-decomposes noun for semantic constraint and restores foreign nouns. The semantic constraints phase finds possible semantic combinations by using origin information in dictionary and Naive Bayes Classifier, in order to decrease the computation time and increase the accuracy of semantic tagging. The semantic tagging phase calculates the semantic similarity between decomposed nouns and decides the semantic tags. We have constructed 40,717 experimental compound nouns data set from Standard Korean Language Dictionary, which consists of more than 3 characters and is semantically tagged. From the experiments, the accuracy of compound noun decomposition is 99.26%, and the accuracy of semantic tagging is 95.38% respectively.

• Journal of KIISE
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• v.43 no.12
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• pp.1376-1384
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• 2016

Computers require analytic and processing capability for all possibilities of human expression in order to process sentences like human beings. Linguistic information processing thus forms the initial basis. When analyzing a sentence syntactically, it is necessary to divide the sentence into components, find obligatory arguments focusing on predicates, identify the sentence core, and understand semantic relations between the arguments and predicates. In this study, the method applied a case frame dictionary based on The Korean Standard Dictionary of The National Institute of the Korean Language; in addition, we used a CRF Model that constructed subcategorization of predicates as featured in Korean Lexical Semantic Network (UWordMap) for semantic role labeling. Automatically tagged semantic roles based on the CRF model, which established the information of words, predicates, the case-frame dictionary and hypernyms of words as features, were used. This method demonstrated higher performance in comparison with the existing method, with accuracy rate of 83.13% as compared to 81.2%, respectively.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.43-52
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• 2012

Semantic role labeling is the research area analyzing the semantic relationship between elements in a sentence and it is considered as one of the most important semantic analysis research areas in natural language processing, such as word sense disambiguation. However, due to the lack of the relative linguistic resources, Korean semantic role labeling research has not been sufficiently developed. We, in this paper, propose an automatic predicate-argument analyzer to begin constructing the Korean PropBank which has been widely utilized in the semantic role labeling. The analyzer has mainly two components: the semantic lexical dictionary and the automatic predicate-argument extractor. The dictionary has the case frame information of verbs and the extractor is a module to decide the semantic class of the argument for a specific predicate existing in the syntactically annotated corpus. The analyzer developed in this research will help the construction of Korean PropBank and will finally play a big role in Korean semantic role labeling.

• KIISE Transactions on Computing Practices
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• v.21 no.7
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• pp.500-505
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• 2015

The Sejong Electronic(machine readable) Dictionary, which was developed by the 21 century Sejong Plan, contains a systematic of immanence information of Korean words. It helps in solving the problem of electronical presentation of a general text dictionary commonly used. Word sense disambiguation problems can also be solved using the specific information available in the Sejong Electronic Dictionary. However, the Sejong Electronic Dictionary has a limitation of suggesting structure of sentences and selection-restricted nouns. In this paper, we discuss limitations of word sense disambiguation by using subcategorization information as suggested by the Sejong Electronic Dictionary and generalize selection-restricted noun of argument using Korean Lexico-semantic network.

• Journal of KIISE:Software and Applications
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• v.28 no.5
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• pp.448-448
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• 2001

The definitions in Korean dictionary not only describe meanings of title, but also include various semantic information such as hypernymy/hyponymy, meronymy/holonymy, polysemy, homonymy, synonymy, antonymy, and semantic features. This paper purposes to implement a parser as the basic tool to acquire automatically the semantic information from the definitions in Korean dictionary. For this purpose, first we constructed the part-of-speech tagged corpus and the tree tagged corpus from the definitions in Korean dictionary. And then we automatically extracted from the corpora the frequency of words which are ambiguous in part-of-speech tag and the grammar rules and their probability based on the statistical method. The parser is a kind of the probabilistic chart parser that uses the extracted data. The frequency of words which are ambiguous in part-of-speech tag and the grammar rules and their probability resolve the noun phrase's structural ambiguity during parsing. The parser uses a grammar factoring, Best-First search, and Viterbi search In order to reduce the number of nodes during parsing and to increase the performance. We experiment with grammar rule's probability, left-to-right parsing, and left-first search. By the experiments, when the parser uses grammar rule's probability and left-first search simultaneously, the result of parsing is most accurate and the recall is 51.74% and the precision is 87.47% on raw corpus.

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