• Korean Journal of English Language and Linguistics
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• v.1 no.1
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• pp.39-57
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• 2001

In English we find two aspectual menaings, progressiveness and habits, which do not seem to fit in any of the classical four situation types of state, activity, accomplishment and achievement, established by Vendler in 1967. This paper analyzes the aspectual features of progressiveness and habits to find out their similarities to and differences from Vendler's four types. It turns out that the progressiveness has the same features as those of activity, and that the habits has independent combination of aspectual features distinguished from any of the four types.

• ETRI Journal
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• v.18 no.1
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• pp.29-41
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• 1996

This paper describes a method to select a suitable feature for speech recognition using information theoretic measure. Conventional speech recognition systems heuristically choose a portion of frequency components, cepstrum, mel-cepstrum, energy, and their time differences of speech waveforms as their speech features. However, these systems never have good performance if the selected features are not suitable for speech recognition. Since the recognition rate is the only performance measure of speech recognition system, it is hard to judge how suitable the selected feature is. To solve this problem, it is essential to analyze the feature itself, and measure how good the feature itself is. Good speech features should contain all of the class-related information and as small amount of the class-irrelevant variation as possible. In this paper, we suggest a method to measure the class-related information and the amount of the class-irrelevant variation based on the Shannon's information theory. Using this method, we compare the mel-scaled FFT, cepstrum, mel-cepstrum, and wavelet features of the TIMIT speech data. The result shows that, among these features, the mel-scaled FFT is the best feature for speech recognition based on the proposed measure.

• Asia pacific journal of information systems
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• v.4 no.2
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• pp.3-34
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• 1994

In this paper we describe a new data modeling language we call $O^{2}LDM$. $O^{2}LDM$ incorporates features from object-oriented and logic approaches. In $O^{2}LDM$ there is a rich collection of objects organized in a type hierarchy. It is possible to compose queries that involve field selection, function application and other constructs which transcend the usual, strictly syntactic, matching of PROLOG. We give the features of $O^{2}LDM$ and motivate its utility for conceptual modeling. We have a prototype implementation for the language, which we have written in ML. In this paper we describe an executable semantics of the deductive process used in the language. We work some examples to illustrate the expressive power of the language, and compare $O^{2}LDM$ to PROLOG.

• Proceedings of the Korean Society for Language and Information Conference
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• 1996.02a
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• pp.317-326
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• 1996

• International Journal of Contents
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• v.15 no.1
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• pp.39-51
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• 2019

Spontaneous speech provides rich information defining the linguistic characteristics of individuals. As such, computational analysis of speech would enhance the efficiency involved in evaluating patients' speech. This study aims to provide a method to differentiate the persons with and without aphasia based on language usage. Ten aphasic patients and their counterpart normal controls participated, and they were all tasked to describe a set of given words. Their utterances were linguistically processed and compared to each other. Computational analyses from PCA (Principle Component Analysis) to machine learning were conducted to select the relevant linguistic features, and consequently to classify the two groups based on the features selected. It was found that functional words, not content words, were the main differentiator of the two groups. The most viable discriminators were demonstratives, function words, sentence final endings, and postpositions. The machine learning classification model was found to be quite accurate (90%), and to impressively be stable. This study is noteworthy as it is the first attempt that uses computational analysis to characterize the word usage patterns in Korean aphasic patients, thereby discriminating from the normal group.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.6
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• pp.1833-1848
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• 2022

Named entity recognition (NER) is an important basic task in the field of Natural Language Processing (NLP). Recently deep learning approaches by extracting word segmentation or character features have been proved to be effective for Chinese Named Entity Recognition (CNER). However, since this method of extracting features only focuses on extracting some of the features, it lacks textual information mining from multiple perspectives and dimensions, resulting in the model not being able to fully capture semantic features. To tackle this problem, we propose a novel Multi-view Semantic Feature Fusion Model (MSFM). The proposed model mainly consists of two core components, that is, Multi-view Semantic Feature Fusion Embedding Module (MFEM) and Multi-head Self-Attention Mechanism Module (MSAM). Specifically, the MFEM extracts character features, word boundary features, radical features, and pinyin features of Chinese characters. The acquired font shape, font sound, and font meaning features are fused to enhance the semantic information of Chinese characters with different granularities. Moreover, the MSAM is used to capture the dependencies between characters in a multi-dimensional subspace to better understand the semantic features of the context. Extensive experimental results on four benchmark datasets show that our method improves the overall performance of the CNER model.

• English Language & Literature Teaching
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• v.18 no.3
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• pp.53-75
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• 2012

The present study examined the role of language proficiency in dyadic discourse in the organization of repairs and the distribution of linguistic features contained in repairs. One native speaker of English and five non-native speakers participated and formed three dyads: one same-proficiency NNS-NNS (non-native speaker), one different-proficiency NNS-NNS, and one NS (native speaker)-NNS dyads. Results showed that overall repair patterns in this type of interaction were more conversational than didactic, and that the degree of difference in proficiency between the participants in the dyad influenced repair patterns and the distribution of linguistic features in relation to repair patterns. Also, discussed in the present paper are some implications of the results and other issues related to language learning.

• Journal of English Language & Literature
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• v.56 no.6
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• pp.1163-1190
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• 2010

Korean EFL learners' stylistic sensitivity was examined through the two types of letter writing, professional and personal. The base of comparison with the English native speakers' stylistic sensitivity was the linguistic style markers that were statistically found by Biber's (1988) multi-dimensional model of variation of English language. The main finding was that Korean university students were sensitive to stylistic difference in the correct direction, though their linguistic repertoire was limited to the easy and simple linguistic features. Also, the learners were skewed in the involved style in both types of the letters unlike the native speakers and it was interpreted as due to the general developmental direction from informal to formal linguistic style. Learners were also skewed in the explicit style in both types of letters unlike the native speakers and it was interpreted as due to the learners' heavy reliance on one particular linguistic feature. As a whole, the learners' stylistic sensitivity heavily relied on the small number of linguistic features that they have already acquired, which happen to be simple and basic linguistic features.

• Language and Information
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• v.14 no.2
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• pp.33-46
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• 2010

This paper introduces a new linguistic-focused approach for sentiment analysis (SA) of Korean. In order to overcome shortcomings of previous works that focused mainly on statistical methods, we made effective use of various linguistic features reflecting the nature of Korean. These features include contextual shifters, modal affixes, and the morphological dependency of chunk structures. Moreover, in order to eschew possible confusion caused by ambiguous words and to improve the results of SA, we also proposed simple adjustment methods of word senses using KOLON ontology mapping information. Through experiments we contend that effective use of linguistic features and ontological information can improve the results of sentiment analysis of Korean.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.27-32
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• 2008

Sign languages, commonly used in aurally challenged communities, are a kind of visual language expressing sign words with motion. Spatiality and motility of a sign language are conveyed mainly via sign words as predicates. A predicate is modified by an adverbial phrase with an accompanying change in its semantics so that the adverbial phrase can also affect the overall spatiality and motility of expressions of a sign language. In this paper, we analyze the semantic features of adverbial phrases which may affect the motion-related semantics of a predicate in converting expressions in Korean into those in a sign language and propose a system that generates corresponding animation by utilizing these features.