• Journal of KIISE
• /
• v.43 no.7
• /
• pp.773-780
• /
• 2016

Semantic information and features are very important for Semantic Role Labeling(SRL) though many SRL systems based on machine learning mainly adopt lexical and syntactic features. Previous SRL research based on semantic information is very few because using semantic information is very restricted. We proposed the SRL system which adopts semantic information, such as named entity, word sense disambiguation, filtering adjunct role based on sense, synonym cluster, frame extension based on synonym dictionary and joint rule of syntactic-semantic information, and modified verb-specific numbered roles, etc. According to our experimentations, the proposed present method outperforms those of lexical-syntactic based research works by about 3.77 (Korean Propbank) to 8.05 (Exobrain Corpus) F1-scores.

• Annual Conference on Human and Language Technology
• /
• 2002.10e
• /
• pp.184-188
• /
• 2002

본 논문에서는 단순히 주변에 위치하는 어휘들간의 문맥 공기 정보를 이용하는 방식과는 달리 국소 구문 관계 및 의미 공기 정보에 기반한 명사 의미 모호성 해소 방안을 제안한다. 기존의 WSD 방법은 구조 분석의 어려움으로 인하여 문장의 구문 관계를 충분히 고려하지 못하고 주변 어휘들과의 공기 관계로 그 의미를 파악하려 했다. 그러나 본 논문에서는 동사구의 논항 의미 관계뿐만 아니라 명사구내에서의 의미 관계도 고려한 국소 구문관계를 고려한 명사 의미 모호성 해소 방법을 제안한다. 이 때, 명사들의 의미는 자동번역 시스템의 목적에 맞게 공기(co-occurrence)하는 동사들에 따라 분류하였다. 그리고 한중 자동 번역 지식으로 사용되는 명사 의미 코드가 부착된 74,880 의미 격틀의 의미 공기정보를 이용하였으며 형태소 태깅된 말뭉치로부터 의미모호성이 발생하지 않게 의미 공기정보 및 명사구 의미 공기 정보를 자동으로 추출하였다. 실험 결과, 의미 모호성이 발생하는 명사들에 대해서 83.9%의 의미 모호성 해소 정확률을 보였다.

• The KIPS Transactions:PartB
• /
• v.10B no.2
• /
• pp.219-228
• /
• 2003

This paper presents an efficient construction method of determination rules of thematic roles from syntactic relations in Korean language processing. This process is one of the main core of semantic analysis and an important issue to be solved in natural language processing. It is problematic to describe rules for determining thematic roles by only using general linguistic knowledge and experience, since the final result may be different according to the subjective views of researchers, and it is impossible to construct rules to cover all cases. However, our method is objective and efficient by considering large corpora, which contain practical osages of Korean language, and case frames in the Sejong Electronic Lexicon of Korean, which is being developed by dozens of Korean linguistic researchers. To determine thematic roles more correctly, our system uses syntactic relations, semantic classes, morpheme information, position of double subject. Especially by using semantic classes, we can increase the applicability of the rules.

• Journal of Korea Society of Industrial Information Systems
• /
• v.8 no.1
• /
• pp.33-42
• /
• 2003

This paper presents an efficient determination method of thematic roles from syntactic relations using rules and statistical model in Korean language processing. This process is one of the main core of semantic analysis and an important issue to be solved in natural language processing. It is problematic to describe rules for determining thematic roles by only using general linguistic knowledge and experience, since the final result may be different according to the subjective views of researchers, and it is impossible to construct rules to cover all cases. However, our hybrid method is objective and efficient by considering large corpora, which contain practical usages of Korean language, and case frames in the Sejong Electronic Lexicon of Korean, which is being developed by dozens of Korean linguistic researchers. To determine thematic roles more correctly, our system uses syntactic relations, semantic classes, morpheme information, position of double subject. Especially by using semantic classes, we can increase the applicability of our system.

• Journal of KIISE
• /
• v.44 no.9
• /
• pp.946-953
• /
• 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 KIPS Transactions:PartB
• /
• v.19B no.1
• /
• pp.43-52
• /
• 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.

• The KIPS Transactions:PartB
• /
• v.18B no.6
• /
• pp.405-414
• /
• 2011

Korean grammar checkers typically detect context-dependent errors by employing heuristic rules that are manually formulated by a language expert. These rules are appended each time a new error pattern is detected. However, such grammar checkers are not consistent. In order to resolve this shortcoming, we propose new method for generalizing error decision rules to detect the above errors. For this purpose, we use an existing thesaurus KorLex, which is the Korean version of Princeton WordNet. KorLex has hierarchical word senses for nouns, but does not contain any information about the relationships between cases in a sentence. Through the Tree Cut Model and the MDL(minimum description length) model based on information theory, we extract noun classes from KorLex and generalize error decision rules from these noun classes. In order to verify the accuracy of the new method in an experiment, we extracted nouns used as an object of the four predicates usually confused from a large corpus, and subsequently extracted noun classes from these nouns. We found that the number of error decision rules generalized from these noun classes has decreased to about 64.8%. In conclusion, the precision of our grammar checker exceeds that of conventional ones by 6.2%.