• Proceedings of the KSPS conference
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• 2003.10a
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• pp.37-40
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• 2003

This paper presents the preliminary result of the automatic pronunciation scoring for non-native English speakers, and shows the developmental process for an English speech recognizer for the educational and evaluational purposes. The proposed speech recognizer, featuring two refined acoustic model sets, implements the noise-robust data compensation, phonetic alignment, highly reliable rejection, key-word and phrase detection, easy-to-use language modeling toolkit, etc., The developed speech recognizer achieves 0.725 as the average correlation between the human raters and the machine scores, based on the speech database YOUTH for training and K-SEC for test.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2023.05a
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• pp.154-155
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• 2023

The International Convention on Standards for Training, Certification, and Watchkeeping of Seafarers (STCW) outlines the qualifications that maritime cadets must meet in order to serve as merchant marine officers. Maritime English is one of the most essential qualifications for STCW, and each national authority is implementing Maritime English education that complies with national and international regulations. In this study, an English proficiency background survey was conducted to investigate the factors related to the Maritime English skills and competencies. In line with this, maritime cadets utilized the Standard Maritime English Communication Phrases (SMCP) learning platform to track their learning progress and its efficacy. This study examined the applicability of the automatic scoring platform for Maritime English education, as well as its future potential for widespread use in the maritime education field.

• Journal of the Korea Society of Computer and Information
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• v.25 no.4
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• pp.141-148
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• 2020

Automatic term extraction is to recognize domain-specific terms given a collection of domain-specific text. Previous term extraction methods operate effectively in unsupervised manners which include extracting candidate terms, and assigning importance scores to candidate terms. Regarding the calculation of term importance scores, the study focuses on utilizing sets of inner and outer terms of a candidate term. For a candidate term, its inner terms are shorter terms which belong to the candidate term as components, and its outer terms are longer terms which include the candidate term as their component. This work presents various functions that compute, for a candidate term, term strength from either set of its inner or outer terms. In addition, a scoring method of a term importance is devised based on C-value score and the term strength values obtained from the sets of inner and outer terms. Experimental evaluations using GENIA and ACL RD-TEC 2.0 datasets compare and analyze the effectiveness of the proposed term extraction methods for English. The proposed method performed better than the baseline method by up to 1% and 3% respectively for GENIA and ACL datasets.

• Journal of KIISE
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• v.42 no.12
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• pp.1522-1534
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• 2015

This work presents a new method that can predict, without the use of training data, whether an input essay is written on a given topic. ConceptNet is a common-sense knowledge base that is generated automatically from sentences that are extracted from a variety of document types. An essay prompt is the topic that an essay should be written about. The method that is proposed in this paper uses ConceptNet and an essay prompt to decide whether or not an input essay is off-topic. We introduce a way to find the shortest path between two nodes on ConceptNet, as well as a way to calculate the semantic similarity between two nodes. Not only an essay prompt but also a student's essay can be represented by concept nodes in ConceptNet. The semantic similarity between the concepts that represent an essay prompt and the other concepts that represent a student's essay can be used for a calculation to rank "on-topicness" ; if a low ranking is derived, an essay is regarded as off-topic. We used eight different essay prompts and a student-essay collection for the performance evaluation, whereby our proposed method shows a performance that is better than those of the previous studies. As ConceptNet enables the conduction of a simple text inference, our new method looks very promising with respect to the design of an essay prompt for which a simple inference is required.