• Annual Conference on Human and Language Technology
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• 2003.10d
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• pp.63-68
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• 2003

본 논문에서는 형태소 분석 대상 어절의 좌우 어절내의 대표 형태소 어휘 문맥 정보에 기반한 형태소 오류 정정 방안을 제안한다. 현재까지 주변의 품사열 문맥 정보에만 의존하는 기존의 품사 태깅 모델과 달리 주변 어휘를 반영할 수 있는 좌우 어절 문맥을 이용해 형태소 태거의 성능을 향상시킬 수 있는 방법들이 제시되었다. 그러나 이러한 어절 문맥에 의한 지속적인 성능 향상을 위해서는 대량의 품사 태킹 문맥 정보를 필요로 한다. 따라서 본 논문에서는 이러한 자료 부족 문제를 해결하기 위하여 기존의 분석 대상 어절 좌우의 어절 단위의 어휘 문맥 정보가 아닌 좌우 어절내의 대표 형태소 단위의 형태소 어휘 문맥을 이용한 품사 태깅 오류 정정 방안을 제안한다. 실험을 통해, 형태소 어휘 단위의 문맥 정보의 적용성(Coverage)의 높고 기존의 품사 문맥 정보 기반의 형태소 분석기의 태깅 오류를 정정하여 그 정확성을 크게 향상시킬 수 있음을 보인다.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.365-374
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• 2008

In this paper, we propose a model to enrich an ontology by incrementally extending the relations through variations of patterns. In order to generalize initial patterns, combinations of features are considered as candidate patterns. The candidate patterns are used to extract relations from Wikipedia, which are sorted out according to reliability based on corpus frequency. Selected patterns then are used to extract relations, while extracted relations are again used to extend the patterns of the relation. Through making variations of patterns in incremental enrichment process, the range of pattern selection is broaden and refined, which can increase coverage and accuracy of relations extracted. In the experiments with single-feature based pattern models, we observe that the features of lexical, headword, and hypernym provide reliable information, while POS and syntactic features provide general information that is useful for enrichment of relations. Based on observations on the feature types that are appropriate for each syntactic unit type, we propose a pattern model based on the composition of features as our ongoing work.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.6
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• pp.427-431
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• 2007

In this paper we describe a Korean part-of-speech tagging approach using resolution rules for individual ambiguous word and statistical information. Our tagging approach resolves lexical ambiguities by common rules, rules for individual ambiguous word, and statistical approach. Common rules are ones for idioms and phrases of common use including phrases composed of main and auxiliary verbs. We built resolution rules for each word which has several distinct morphological analysis results to enhance tagging accuracy. Each rule may have morphemes, morphological tags, and/or word senses of not only an ambiguous word itself but also words around it. Statistical approach based on HMM is then applied for ambiguous words which are not resolved by rules. Experiment shows that the part-of-speech tagging approach has high accuracy and broad coverage.

• Korean Journal of Cognitive Science
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• v.22 no.1
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• pp.57-78
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• 2011

Paraphrasing is the act of writing a text using other words without altering the meaning. Paraphrases can be used in many fields of natural language processing. In particular, paraphrases can be incorporated in machine translation in order to improve the coverage and the quality of translation. Recently, the approaches on paraphrase extraction utilize bilingual parallel corpora, which consist of aligned sentence pairs. In these approaches, paraphrases are identified, from the word alignment result, by pivot phrases which are the phrases in one language to which two or more phrases are connected in the other language. However, the word alignment is itself a very difficult task, so there can be many alignment errors. Moreover, the alignment errors can lead to the problem of selecting incorrect pivot phrases. In this study, we propose a method in paraphrase extraction that discriminates good pivot phrases from bad pivot phrases. Each pivot phrase is weighted according to its reliability, which is scored by considering the lexical and part-of-speech information. The experimental result shows that the proposed method achieves higher precision and recall of the paraphrase extraction than the baseline. Also, we show that the extracted paraphrases can increase the coverage of the Korean-English machine translation.

• Korean Journal of Culture and Social Issue
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• v.29 no.1
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• pp.53-74
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• 2023

The purpose of this study was to identify the priming effects of disaster-related news. Specifically, this study aimed to examine the priming effects about trauma words and neutral words according to the geographical and psychological distance from the disaster. The participants in this study were 75 college students who had not experienced the Daegu subway fire accident, and whom were not ruled out after completing the screening measure completing the screening measure the completion of questionnaires for screening. All participants conducted a lexical decision task after watching a news video about the Daegu subway fire accident. The design was a 2 (Geographical distance: close vs. distant) × 2 (Psychological distance: close vs. distant) × 2 (Priming type: trauma priming vs. neutral priming) mixed-design. The results of this study are summarized as follows: First, the group which was geographically closer to the disaster tended to revealed a largerbigger priming effect than the distant group-a trend toward significance. Second, within the psychologically closer to the disaster, group, for those who were geographically closer too, the priming effect was larger for those who were geographically closer geographically closer than for the geographically distant group. Third, the geographically closer group had a largerbigger neutral priming effect than the distant group. Fourth, the psychologically distant group had a largerbigger neutral priming effect than the close group. To sum up, this study identified that disaster news coverage may have an implicit effect on people, and this influence can change according to the geographical and psychological distance. Finally, this study discussed the limitations of the study and recommendations for further research.

• Korean Journal of Cognitive Science
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• v.21 no.1
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• pp.95-126
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• 2010

This study proposes a bottom-up and inductive manual mapping methodology for integrating two heterogenous fine-grained ontologies which were built by a top-down and deductive methodology, namely the Sejong semantic classes (SJSC) and the upper nodes in KorLexNoun 1.5 (KLN), for HLP applications. It also discusses various problematics in the mapping processes of two language resources caused by their heterogeneity and proposes the solutions. The mapping methodology of heterogeneous fine-grained ontologies uses terminal nodes of SJSC and Least Upper Bounds (LUB) of KLN as basic mapping units. Mapping procedures are as follows: first, the mapping candidate groups are decided by the lexfollocorrelation between the synsets of KLN and the noun senses of Sejong Noun Dfotionaeci(SJND) which are classified according to SJSC. Secondly, the meanings of the candidate groups are precisely disambiguated by linguistic information provided by the two ontologies, i.e. the hierarchicllostructures, the definitions, and the exae les. Thirdly, the level of LUB is determined by applying the appropriate predicates and definitions of SJSC to the upper-lower and sister nodes of the candidate LUB. Fourthly, the mapping possibility ic inthe terminal node of SJSC is judged by che aring hierarchicllorelations of the two ontologies. Finally, the ituorrect synsets of KLN and terminologiollocandidate groups are excluded in the mapping. This study positively uses various language information described in each ontology for establishing the mapping criteria, and it is indeed the advantage of the fine-grained manual mapping. The result using the proposed methodology shows that 6,487 LUBs are mapped with 474 terminal and non-terminal nodes of SJSC, excluding the multiple mapped nodes, and that 88,255 nodes of KLN are mapped including all lower-level nodes of the mapped LUBs. The total mapping coverage is 97.91% of KLN synsets. This result can be applied in many elaborate syntactic and semantic analyses for Korean language processing.