• Journal of the Korean Society of Earth Science Education
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• v.5 no.2
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• pp.158-165
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• 2012

With five gifted and nine average elementary school students, this study attempted to make a comparative analysis on the characteristics of their scientific writings for earth science-related topics. The analysis found that all of the gifted students showed higher scores than the average in the writing sections of scientific nature, logical nature and creativity. Compared to the average scores, their creativity scores were far higher. By comparing and analyzing the predicates in the writings two groups wrote, I found that the gifted students used more sentences per topic than the average students. Both groups wrote the most numbers of sentences for Volcano-related topics. In the meantime, the gifted children used the least numbers of sentences for the related topics to atmospheric pollution and the average students did so for the related topics to fossils. By the analysis on the patterns of predicate, it was observed that both groups used material predicates most and verbal predicates least. As far as the second most used predicates are concerned, the gifted children used relational predicates and the average students used mental predicates.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.24-29
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• 2022

Semantic role labeling task used in various natural language processing fields, such as information extraction and question answering systems, is the task of identifying the arugments for a given sentence and predicate. Predicate used as semantic role labeling input are extracted using lexical analysis results such as POS-tagging, but the problem is that predicate can't extract all linguistic patterns because predicate in korean language has various patterns, depending on the meaning of sentence. In this paper, we propose a korean predicate recognition method using neural network model with pre-trained embedding models and lexical features. The experiments compare the performance on the hyper parameters of models and with or without the use of embedding models and lexical features. As a result, we confirm that the performance of the proposed neural network model was 92.63%.

• The KIPS Transactions:PartB
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• v.19B no.2
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• pp.135-148
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• 2012

This paper proposes a model for recognizing sentential paraphrases through Predicate-Argument Tuple (PAT)-based approximate alignment between two texts. We cast the paraphrase recognition problem as a binary classification by defining and applying various alignment features which could effectively express the semantic relatedness between two sentences. Experiment confirmed the potential of our approach and error analysis revealed various paraphrase patterns not being solved by our system, which can help us devise methods for further performance improvement.

• Annual Conference of KIPS
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• 2010.11a
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• pp.748-750
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• 2010

문서 내에 존재하는 개체들 간의 관계를 자동으로 추출할 때 다양한 형태의 문서 분석 결과를 활용할 수 있는데, 본 논문에서는 문장 내에 존재하는 각 단어의 predicate-argument 관계를 분석하여 자질로 활용하는 PAS 패턴 기반 관계 추출 시스템을 제안한다. 관계 종류별로 구축된 PAS 패턴 집합을 활용하여 관계 식별기를 개발하였고, 실험을 통하여 개발된 관계 식별기의 성능을 측정하였다. 실험 결과 개체 간의 유의미한 관계를 표현해주는 PAS 패턴이 관계 추출 작업에 유용한 정보임을 알 수 있었다.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.6
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• pp.219-230
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• 2014

An analysis of dependency relation is a job that determines the governor and the dependent between words in sentence. The dependency relation of predicate is established by patterns and selectional restriction of subcategorization of the predicate. This paper proposes a method of analysis of Korean dependency relation using homograph predicate disambiguated in morphology analysis phase. The disambiguated homograph predicates has each different pattern. Especially reusing a stage transition training dictionary used during tagging POS and homograph, we propose a method of fixing the dependency relation of {noun+postposition, predicate}, and we analyze the accuracy and an effect of homograph for analysis of dependency relation. We used the Sejong Phrase Structured Corpus for experiment. We transformed the phrase structured corpus to dependency relation structure and tagged homograph. From the experiment, the accuracy of dependency relation by disambiguating homograph is 80.38%, the accuracy is increased by 0.42% compared with one of undisambiguated homograph. The Z-values in statistical hypothesis testing with significance level 1% is ${\mid}Z{\mid}=4.63{\geq}z_{0.01}=2.33$. So we can conclude that the homograph affects on analysis of dependency relation, and the stage transition training dictionary used in tagging POS and homograph affects 7.14% on the accuracy of dependency relation.

• Journal of KIISE:Computing Practices and Letters
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• v.6 no.3
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• pp.364-372
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• 2000

Subcategorization, defining dependency relation between predicates and their complements, is an important source of knowledge for resolving syntactic and semantic ambiguities arising in analyzing sentences. This paper describes a Korean subcategorization dictionary, particularly annotated with semantic roles of complements coupled with thesaural semantic hierarchy as well as syntactic dependencies. For annotating roles, we defined 25 semantic roles associated with surface case markers that can be used to derive semantic structures directly from syntactic ones. In addition, we used more than 120,000 entries of thesaurus to specify concept markers of noun complements, and also used 47 and 17 predicate patterns for verbs and adjectives, respectively, to express dependency relation between predicates and their complements. Using a full-fledged thesaurus for specifying concept markers makes it possible to build an effective selectional restriction mechanism coupled with the subcategorization dictionary, and using the standard predicate patterns for specifying dependency relations makes it possible to avoid inconsistency in the results and to reduce the costs for constructing the dictionary. On the bases of these, we built a Korean subcategorization dictionary for frequently used 13,000 predicates found in corpora with the aid of a tool specially designed to support this task. An experimental result shows that this dictionary can provide 72.7% of predicates in corpora with appropriate subcategorization information.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.73-85
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• 2011

Lots of valuable textual information is used to extract relations between named entities from literature. Composite kernel approach is proposed in this paper. The composite kernel approach calculates similarities based on the following information:(1) Phrase structure in convolution parse tree kernel that has shown encouraging results. (2) Predicate-argument structure patterns. In other words, the approach deals with syntactic structure as well as semantic structure using a reciprocal method. The proposed approach was evaluated using various types of test collections and it showed the better performance compared with those of previous approach using only information from syntactic structures. In addition, it showed the better performance than those of the state of the art approach.

• 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.

• Journal of English Language & Literature
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• v.56 no.6
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• pp.1135-1161
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• 2010

The interpretations of degree expressions require the postulation of new entities to represent degrees. Diverse entities such as degrees, intervals, and vectors are adopted for degree expressions. Positive degree sentences and questions are properly construed with the introduction of these entities, but their negative counterparts need more consideration. Negative degree sentences show dual patterns of entailments depending on contexts, and negative degree questions are unacceptable, making weak islands. To explicate the distinct nature of negative degree sentences and questions, Fox & Hackl (2006) provide an analysis based on degrees while Abrusan & Spector (2010) suggest a proposal in interval readings of degree expressions. I have pointed out the theoretical problems of these analyses and proposed an alternative in the framework of the vector space semantics, following Winter (2005). Bi-directional scales in vector space fit well with the dual patterns of negative degree sentences, and the notion of a reference vector is useful to accommodate the contextual influence in negative degree sentences and to deal with the unacceptability of negative degree questions.

• MALSORI
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• v.68
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• pp.17-32
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• 2008

The purpose of the present study is to investigate syntactic development of Korean by analysing the spontaneous speech data. Thirty children(3, 5, and 7-year-old and 10 per each age group) and 10 adults are employed as subjects for this study. Speech data were recorded and transcribed in orthography. Transcribed data are analysed syntactically: sentence(simple vs complex) patterns and clause patterns(4 basic types according to the predicate) etc. The results are as follows: 1) simple sentences show higher frequency for the upper age groups, 2) complex sentences with conjunctive and embedded clauses show higher frequency for the upper age groups.