• English Language & Literature Teaching
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• v.12 no.2
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• pp.61-85
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• 2006

The purpose of the present study is to compare Korean EFL learners' speech corpus (i.e. oral productions) with their composition corpus (i.e. written productions). Four college students participated in the study. The composition corpus was collected through a writing assignment, and the speech corpus was gathered by audio-taping their oral presentations. The results of the data analysis indicate that (i) As for error frequency, young adult low-intermediate Korean EFL learners showed high frequency in determiners (mostly, indefinite articles), vocabulary (mostly, semantic errors), and prepositions. The frequency order did not show much difference between the speech corpus and the composition corpus; and (ii) When comparing the oral productions with the written productions, there were not many differences between them in terms of the contents, a style (i.e., colloquial vs. literary), vocabulary selection, and error types and frequency. Therefore, it is assumed that the proficiency in oral presentation of EFL learners at this learning stage heavily depends on how much/how well they are able to write. In other words, EFL learners' writing and speaking skills are closely co-related. It implies that the teacher does not need to separate teaching how to speak from teaching how to write. The teacher may use the same methods or strategies to help the learners improve their English speaking and writing skills. Furthermore, it will be more effective to teach writing before speaking since they have more opportunities to write than speak in the EFL contexts.

• Smart Media Journal
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• v.9 no.4
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• pp.144-151
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• 2020

In a mobile environment, communication takes place via SMS text messages. Vocabularies used in SMS texts can be expected to use vocabularies of different classes from those used in general Korean literary style sentence. For example, in the case of a typical literary style, the sentence is correctly initiated or terminated and the sentence is well constructed, while SMS text corpus often replaces the component with an omission and a brief representation. To analyze these vocabulary usage characteristics, the existing colloquial style corpus and the literary style corpus are used. The experiment compares and analyzes the vocabulary use characteristics of the colloquial corpus SMS text corpus and the Naver Sentiment Movie Corpus, and the written Korean written corpus. For the comparison and analysis of vocabulary for each corpus, the part of speech tag adjective (VA) was used as a standard, and a distinctive collexeme analysis method was used to measure collostructural strength. As a result, it was confirmed that adjectives related to emotional expression such as'good-','sorry-', and'joy-' were preferred in the SMS text corpus, while adjectives related to evaluation expressions were preferred in the Naver Sentiment Movie Corpus. The word embedding was used to automatically construct a sentiment lexicon based on the extracted adjectives with high collostructural strength, and a total of 343,603 sentiment representations were automatically built.

• The Journal of the Convergence on Culture Technology
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• v.8 no.5
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• pp.489-495
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• 2022

It makes difference to LSTM D/L(Deep Learning) results for language model construction as the corpus preprocess changes. An LSTM model was trained with a famouse literaure poems(Ki Hyung-do's work) for training corpus in the study. You get the two wordvector sets for two corpus sets of the original text and eraised word ending text each once D/L training completed. It's been inspected of the similarity/analogy operation results, the positions of the wordvectors in 2D plane and the generated texts by the language models for the two different corpus sets. The suggested words by the silmilarity/analogy operations are changed for the corpus sets but they are related well considering the corpus characteristics as a literature work. The positions of the wordvectors are different for each corpus sets but the words sustained the basic meanings and the generated texts are different for each corpus sets also but they have the taste of the original style. It's supposed that the D/L language model can be a useful tool to enjoy the literature in object and in diverse with the analysis results shown in the study.

• The Journal of Korean Medical History
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• v.28 no.1
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• pp.135-141
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• 2015

The purpose of this study is to analyze vocabulary found in "Donguibogam", one of the medical books in mid-Chosun, through Corpus-based analysis, one of the text analysis methods. According to it, Donguibogam has total 871,000 words in it, and Chinese characters used in it are total 5,130. Among them, 2,430 characters form 99% of the entire text. The most frequently appearing 20 Chinese characters are mainly function words, and with this, we can see that "Donguibogam" is a book equipped with complete forms of sentences just like other books. Examining the chapters of "Donguibogam" by comparison, Remedies and Acupuncture indicated lower frequencies of function words than Internal Medicine, External Medicine, and Miscellaneous Diseases. "Yixuerumen (Introduction to Medicine)" which influenced "Donguibogam" very much has lower frequencies of function words than "Donguibogam" in its most frequently appearing words. This may be because "Yixuerumen" maintains the form of Chileonjeolgu (a quatrain with seven Chinese characters in each line with seven-word lines) and adds footnotes below it. Corpus-based analysis helps us to see the words mainly used by measuring their frequencies in the book of medicine. Therefore, this researcher suggests that the results of this analysis can be used for education of Chinese characters at the college of Korean Medicine.

• Asia Pacific Journal of Corpus Research
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• v.1 no.1
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• pp.95-125
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• 2020

This paper aims to inform people how to support each other on social media. It alludes to an architecture for social media discourse and proposes a novel theory of support in social media discourse. It makes a methodological contribution. It combines predominately artificial intelligence with corpus linguistics analysis. It is on a large-scale dataset of anonymised diabetes-related user's posts from the Facebook platform. Log-likelihood and precision measures help with validation. A multi-method approach with Discourse Analysis helps in understanding any potential patterns. People living with Diabetes are found to employ sophisticated high-frequency patterns of device-enabled categories of purpose and content. It is with, for example, linguistic forms of Advice with stance-taking and targets such as Diabetes amongst other interactional ways. There can be uncertainty and variation of effect displayed when sharing information for support. The implications of the new theory aim at healthcare communicators, corpus linguists and with preliminary work for AI support-bots. These bots may be programmed to utilise the language patterns to support people who need them automatically.

• English Language & Literature Teaching
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• v.17 no.3
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• pp.177-194
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• 2011

This study examines the use of discourse markers in cross-cultural communication between EFL learners in an e-learning environment. The study analyzes the use of discourse markers in a corpus of an interactive web with a bulletin board system through which college students of English at Japanese and Korean universities interacted with each other discussing the topics of local and global issues. It compares the use of discourse markers in the learners' corpus to that of a native English speakers' corpus. The results indicate that discourse markers are useful interactional devices to structure and organize discourse. EFL learners are found to display more frequent use of referentially and cognitively functional discourse markers and a relatively rare use of other markers. Native speakers are found to use a wider variety of discourse markers for different functions. Suggestions are made for using computer corpora in understanding EFL learners' language difficulties and helping them become more interactionally competent speakers.

• Korean Journal of English Language and Linguistics
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• v.4 no.1
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• pp.61-81
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• 2004

This paper will look at some grammatical phenomena which will illustrate some of the questions that can be addressed with a corpus-based approach. We will use this approach to investigate the following subjects in English grammar: number ambiguity, subject-verb concord, concord with measure expressions, and (reflexive) pronoun choice in coordinated noun phrases. We will emphasize the distinctive features of the corpus-based approach, particularly its strengths in investigating language use, as opposed to traditional descriptions or prescriptions of structure in English grammar. This paper will show that a corpus-based approach has made it possible to conduct new kinds of investigations into grammar in use and to expand the scope of earlier investigations. Native speakers rarely have accurate information about frequency of use. A large representative corpus (i.e., The British National Corpus) is one of the most reliable sources of frequency information. It is important to base an analysis of language on real data rather than intuition. Any description of grammar is more complete and accurate if it is based on a body of real data.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.87-90
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• 2002

This paper analyzes statistically the relationship between size and balance of text corpus by evaluation of the effect of interview sentences in language model for Korean broadcast news transcription system. Our Korean broadcast news transcription system's ultimate purpose is to recognize not interview speech, but the anchor's and reporter's speech in broadcast news show. But the gathered text corpus for constructing language model consists of interview sentences a portion of the whole, $15\%$ approximately. The characteristic of interview sentence is different from the anchor's and the reporter's in one thing or another. Therefore it disturbs the anchor and reporter oriented language modeling. In this paper, we evaluate the effect of interview sentences in language model for Korean broadcast news transcription system and analyze statistically the relationship between size and balance of text corpus by making an experiment as the same procedure according to varying the size of corpus.

• Asia Pacific Journal of Corpus Research
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• v.4 no.1
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• pp.25-42
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• 2023

The number of studies on data-driven learning (DDL) has increased in recent years, and DDL's overall effectiveness as an L2 (second language) teaching methodology has been reported to be high. However, the degree of its effectiveness in grammar instruction, particularly for the goal of correcting errors in L2 writing, is still unclear. To provide guidelines for focused grammar instruction with DDL in the Japanese classroom setting, we aimed to identify the typical grammatical errors made by Japanese learners in the Cambridge Learner Corpus First Certificate in English (CLC FCE) dataset. The results revealed that three error types (nouns, articles, and prepositions) should be addressed in DDL grammar instruction for Japanese English as a foreign language (EFL) learners. In light of the findings, pedagogical implications and suggestions for future DDL research and practice are discussed.

• Asia Pacific Journal of Corpus Research
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• v.4 no.2
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• pp.57-74
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• 2023

Having a firm grasp of technical terms is essential for learners of Japanese for Specific Purposes (JSP). This research aims to analyze Japanese nursing care vocabulary based on objective corpus-based frequency and subjectively rated word familiarity. For this purpose, we constructed a text corpus centered on the National Examination for Certified Care Workers to extract nursing care keywords. The Log-Likelihood Ratio (LLR) was used as the statistical criterion for keyword identification, giving a list of 300 keywords as target words for a further word recognition survey. The survey involved 115 participants of whom 51 were certified care workers (CW group) and 64 were individuals from the general public (GP group). These participants rated the familiarity of the target keywords through crowdsourcing. Given the limited sample size, Bayesian linear mixed models were utilized to determine word familiarity rates. Our study conducted a comparative analysis of word familiarity between the CW group and the GP group, revealing key terms that are crucial for professionals but potentially unfamiliar to the general public. By focusing on these terms, instructors can bridge the knowledge gap more efficiently.