http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
KOLON(the KOrean Lexicon mapped onto the Mikrokosmos ONtology): 한국어 어휘의 미크로코스모스 온톨로지로의 사상과 언어 자원의 결합
신효필(Hyopil Shin) 사단법인 한국언어학회 2010 언어학 Vol.0 No.56
The KOLON(KOrean Lexicon mapped onto the Mikrokosmos ONtology) is an output of our work of mapping Korean words onto the Mikrokosmos ontology with a view to building a Wordnet for Korean. Unlike other Wordnet-related resources, the KOLON aims at taking fully advantage of properties of a concept represented in a frame by inheriting them to lexicons. We mapped about 24,858 Korean words consisting of 7,386 nouns, 13,397 verbs and 4,075 adjectives so far. Since we keep adding lexical items and cleaning original mappings, the numbers are subject to change. Synonyms are grouped together for each concepts. The big difference between the KOLON and other Korean Wordnet-related resources in terms of synonyms comes from granularity. While other resources show a fine grained and very restricted set of synonyms, the work of mapping Korean words onto the Mikrokosmos ontology results in a wide coverage of synonym set, because a concept can cover many lexical items in a cognitive perspective. described the mapping procedure in line with parts-of-speech, and pointed out strengths and weaknesses of the work. And I compared the KOLON with another Korean Word net, KorLex, and showed the ideological differences between the two efforts. I contend that the work described here can be a useful resource for a natural language processing and theoretical Linguistic research. All the information and up-to-date lexical items can be checked on the website, http://word.snu.ac.kr/kolon.
한국어 감정분석 코퍼스를 활용한 양상정보 기반의 감정분석 연구
신효필(Hyopil Shin),김문형(Munhyong Kim),박수지(Suzi Park) 사단법인 한국언어학회 2016 언어학 Vol.0 No.74
This study develops a practical application of language resources from the Korean Sentiment Analysis Corpus (KOSAC) for sentiment analysis research. With this in mind, based on their sentiment properties and the probabilistic factors of annotated expressions from KOSAC, we extracted annotated expressions and refined them to be a sentiment analysis research resource. This study attempted to break away from simple calculation methods dependant on the distribution of lexical polarity items seen in previous research. Additionally, in order to perform more sophisticated sentiment analysis, we attempted to introduce pragmatic information which includes modality. In order to achieve this, we cataloged expressions that include pragmatic information related to the speaker"s attitude, based on their relative probability in KOSAC. After doing so, this study shows a practical application of this new language resource to subjectivity analysis research. When using this new resource, this research demonstrates an accuracy improvement of around 6%. This demonstrates very clearly that, in addition to polarity items, there exists a need to include a variety of aspects and lexical information when doing this type of research. Moreover, this extraction of sentiment expressions, depending on their semantic and pragmatic properties, not only shows an additional use of KOSAC, but also establishes a new resource in the field of sentiment analysis.
김영삼(Youngsam Kim),신효필(Hyopil Shin) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.12
Temporal-difference(TD) learning is a core algorithm of reinforcement learning, which employs models of Markov process. In the TD methods, rewards are always discounted by a discount factor and states receive these discounted values as their rewards. In this paper, we attempted to estimate a semantic orientation of words in texts using the TD-based methods and examined the effectiveness of the proposed methods by comparing them to existing feature selection methods (indirect approach) and Bayes probabilities (direct approach). The TD-based estimation would be useful for tasks of social opinion mining, since TD learning is inherently an on-line method. In order to show our approach is scalable to huge data, the estimation method is also evaluated using asynchronous parallel processing.
순차 모형과 언어 자질 벡터를 이용한 한국어 토론 데이터의 선형 논증 구조 분석
이상아(Sangah Lee),신효필(Hyopil Shin) Korean Institute of Information Scientists and Eng 2018 정보과학회논문지 Vol.45 No.12
Current studies on argument mining provide tree-structured argumentation structures based on relational nuclearities and discourse relations between sentences in each document. In this case, inconsistencies between related sentences may occur, constructing a full argumentation structure for a document by the bottom-up method. This paper introduces relations between the topic of texts and sentences to provide a frame of argumentation structure. Automatic analysis of argumentation structure uses contextual information from documents, as argument types defined for each sentence are applied to the sequential model. In this paper, we vectorized sentences using bag-of-words of morphemes, word embedding of morphemes, and some linguistic features extracted from the sentence respectively, and used those vectors as inputs of models to predict argument types in the document. As a result, the combination of linguistic features and the sequential model revealed the best result in the experiment, showing 0.68 as the f1-score.