• Korean Journal of English Language and Linguistics
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• v.3 no.2
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• pp.255-280
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• 2003

The present paper will argue what can be considered to be principled elements of the initial state S/sub 0/ of the Faculty of Language, which are called the Interface Condition (IC), and how far we can take the strongest minimalist thesis (SMT), which aims to offer principled explanation of language in terms of IC and the principle of efficient computation, to linguistic analysis. We will discuss implications of label-free phrase structures, required by the strong version of the Inclusiveness Condition, and possibilities of crash-free syntax, required by the condition of efficient computation. I will point out problems of Chomsky's assumption that an externally Merged expletive there is a head, which, as a probe, undergoes agreement with the goal T. I will present several advantages we obtain if we maintain A and A' distinction, and assume that wh-movement to the outer [SPEC, υ] is an A'-movement like wh-movement to [SPEC, C].

• Language and Information
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• v.17 no.2
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• pp.67-91
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• 2013

This paper describes Korean Nominal Bank, a project that provides argument structure for instances of the predicative nouns in the Sejong parsed Corpus. We use the language resources of the Sejong project, so that the same set of data is annotated with more and more levels of annotation, since a new type of a language resource building project could bring new information of separate and isolated processing. We have based on the annotation scheme based on the Sejong electronic dictionary, semantically tagged corpus, and syntactically analyzed corpus. Our work also involves the deep linguistic knowledge of syntaxsemantic interface in general. We consider the semantic theories including the Frame Semantics of Fillmore (1976), argument structure of Grimshaw (1990) and argument alternation of Levin (1993), and Levin and Rappaport Hovav (2005). Various syntactic theories should be needed in explaining various sentence types, including empty categories, raising, left (or right dislocation). We also need an explanation on the idiosyncratic lexical feature, such as collocation and etc.

• Korean Journal of English Language and Linguistics
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• v.2 no.2
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• pp.207-226
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• 2002

In this paper we provide an alternative explanation for there construction, by assuming that the expletive there (EXPL) has independently uninterpretable Case feature as well as u[person], not as being pied-pipied with $\varphi$-feature. If EXPL has only u[person], we could analyse incorrectly some there constructions, including an embedded infinitive clause: by Chomsky (1998, 1999) in the construction, EXPL is ‘frozen’ and cannot participate in the computation of higher. As a result, we could predict incorrectly that the derivation is crashed. But if EXPL has two uninterpretable features, u[person] and u[Case], we could predict correctly that the derivation is converged: the u[person] of EXPL is deleted under Matching/Agree with $T_{def}$; still, the undeleted u[Case] of EXPL is activating; so EXPL can be raised to [SPEC, T] to satisfy the EPP-feature of matrix T.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3230-3255
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• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• Korean Journal of Cognitive Science
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• v.21 no.4
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• pp.669-696
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• 2010

Assuming that the whole meaning of a document is a composition of the meanings of each part, this paper proposes to study the automatic grading of movie reviews which contain sentimental expressions. This will be accomplished by calculating the values of semantic segments and performing data classification for each review. The ARSSA(The Automatic Rating System for Sentiment analysis using an Appraisal dictionary) system is an effort to model decision making processes in a manner similar to that of the human mind. This aims to resolve the discontinuity between the numerical ranking and textual rationalization present in the binary structure of the current review rating system: {rate: review}. This model can be realized by performing analysis on the abstract menas extracted from each review. The performance of this system was experimentally calculated by performing a 10-fold Cross-Validation test of 1000 reviews obtained from the Naver Movie site. The system achieved an 85% F1 Score when compared to predefined values using a predefined appraisal dictionary.