• The KIPS Transactions:PartB
• /
• v.9B no.3
• /
• pp.383-388
• /
• 2002

We developed a spatial parser generator, SPG, which can automatically create a spatial parser if CMG(Constraint Multiset Grammars) grammars for a visual language are provided by the user with GUI(Graphical User Interface). SPG has the following features. (1) The user uses a visual editor to define the grammars of a virtual language and draw the visual language which should be parsed. (2) The user roughly defines CMG grammars in a visual wan at first. Then the user modifies them and defines final grammars. (3) Because SPG has a constraint solver, it maintains constraints in the parsed virtual language according to the grammars.

• The KIPS Transactions:PartD
• /
• v.10D no.3
• /
• pp.399-406
• /
• 2003

The widespreading of XML as a standard for semi-structured documents on the Web opens up challenging opportunities for Web query language. And UML is a graphical language to represent the result of object-oriented analysis and design. In this paper, we introduce an UML-based graphical query language for XML documents. The use of a visual formalism for representing the syntax and semantics of queries enables an intuitive expression of queries, even when they are rather complex. And, it is matched a series of processes to store and retrieve XML documents to OODBMS with the use of an uniform visualization for representing both the content of XML documents (and of their DTD) and the syntax and semantics of queries.

• The Journal of Korean Association of Computer Education
• /
• v.13 no.1
• /
• pp.37-44
• /
• 2010

Recently, the scientific approach to brain engineering is actively being made for effective foreign language learning and diagnosis. In order to supplement the problem of preexistence paper exam, the study aimed to develop a tool for foreign language fluency diagnosis which based on brain engineering. The proposed tools in the paper indirectly measure the aspects of brain information processing by testing learners' 3 abilities of linguistic memory, comprehension, and language production in 5 different ways.

• The KIPS Transactions:PartB
• /
• v.8B no.6
• /
• pp.675-684
• /
• 2001

This paper describes a method for stemming of Korean language by using unsupervised learning from raw corpus. This technique does not require a lexicon or any language-specific knowledge. Since we use unsupervised learning, the time and effort required for learning is negligible. Unlike heuristic approaches that are theoretically ungrounded, this method is based on widely accepted statistical methods, and therefore can be easily extended. The method is currently applied only to Korean language, but it can easily be adapted to other agglutinative languages, since it is not language-dependent.

• Journal of the Korea Convergence Society
• /
• v.12 no.1
• /
• pp.57-63
• /
• 2021

Spoken language understanding, which aims to understand utterance as naturally as human would, are mostly focused on English language. In this paper, we construct a Korean language dataset for spoken language understanding, which is based on a conversational corpus between reservation system and its user. The domain of conversation is limited to restaurant reservation. There are 7 types of slot tags and 5 types of intent tags in 6857 sentences. When a model proposed in English-based research is trained with our dataset, intent classification accuracy decreased a little, while slot filling F1 score decreased significantly.

• KNOM Review
• /
• v.24 no.2
• /
• pp.35-47
• /
• 2021

Various stress exists in a modern society, which requires constant competition and improvement. A person under stress often shows his pressure in his facial expression and language. Therefore, it is possible to measure the pressure using facial expression and language analysis. The paper proposes a stress measurement system using facial expression and language sensitivity analysis. The method analyzes the person's facial expression and language sensibility to derive the stress index based on the main emotional value and derives the integrated stress index based on the consistency of facial expression and language. The quantification and generalization of stress measurement enables many researchers to evaluate the stress index objectively in general.

• Communications of Mathematical Education
• /
• v.38 no.1
• /
• pp.49-67
• /
• 2024

Recent interest in the diverse applications of artificial intelligence (AI) language models has highlighted the need to explore didactical uses in mathematics education. AI language models, capable of natural language processing, show promise in solving mathematical word problems. This study tested the capability of ChatGPT, an AI language model, to solve word problems from elementary school textbooks, and analyzed both the solutions and errors made. The results showed that the AI language model achieved an accuracy rate of 81.08%, with errors in problem comprehension, equation formulation, and calculation. Based on this analysis of solution processes and error types, the study suggests implications for the didactical application of AI language models in education.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.8
• /
• pp.325-330
• /
• 2022

This paper conducts the collection and experiment of datasets for deep learning research on sign language such as sign language recognition, sign language translation, and sign language segmentation for Korean sign language. There exist difficulties for deep learning research of sign language. First, it is difficult to recognize sign languages since they contain multiple modalities including hand movements, hand directions, and facial expressions. Second, it is the absence of training data to conduct deep learning research. Currently, KETI dataset is the only known dataset for Korean sign language for deep learning. Sign language datasets for deep learning research are classified into two categories: Isolated sign language and Continuous sign language. Although several foreign sign language datasets have been collected over time. they are also insufficient for deep learning research of sign language. Therefore, we attempted to collect a large-scale Korean sign language dataset and evaluate it using a baseline model named TSPNet which has the performance of SOTA in the field of sign language translation. The collected dataset consists of a total of 11,402 image and text. Our experimental result with the baseline model using the dataset shows BLEU-4 score 3.63, which would be used as a basic performance of a baseline model for Korean sign language dataset. We hope that our experience of collecting Korean sign language dataset helps facilitate further research directions on Korean sign language.

• Journal of Internet Computing and Services
• /
• v.21 no.3
• /
• pp.123-131
• /
• 2020

Along with the rapid development of artificial intelligence technology, natural language processing, which deals with human language, is also actively studied. In particular, BERT, a language model recently proposed by Google, has been performing well in many areas of natural language processing by providing pre-trained model using a large number of corpus. Although BERT supports multilingual model, we should use the pre-trained model using large amounts of Korean corpus because there are limitations when we apply the original pre-trained BERT model directly to Korean. Also, text contains not only vocabulary, grammar, but contextual meanings such as the relation between the front and the rear, and situation. In the existing natural language processing field, research has been conducted mainly on vocabulary or grammatical meaning. Accurate identification of contextual information embedded in text plays an important role in understanding context. Knowledge graphs, which are linked using the relationship of words, have the advantage of being able to learn context easily from computer. In this paper, we propose a system to extract Korean contextual information using pre-trained BERT model with Korean language corpus and knowledge graph. We build models that can extract person, relationship, emotion, space, and time information that is important in the text and validate the proposed system through experiments.

• Proceedings of the IEEK Conference
• /
• 2002.07a
• /
• pp.556-559
• /
• 2002

The Call Processing Language (CPL, in short), recommended in RFC 2824 of IETF, is a service description language for the Internet Telephony. The CPL allows users to define their own services, which dramatically improves the choice and flexibility of the users. The syntax of the CPL is strictly defined by DTD (Document Type Definition). However, compliance with the DTD is not a sufficient condition for correctness of a CPL script. There are enough rooms for non-expert users to make semantical mistakes in the service logic, which could lead to serious system down. In this paper, we present six classes of semantic warnings for the CPL service description: MF, IS, CR, AS, US, OS. For each class, we give the definition and its effects with an example script. These warnings are not necessarily errors. However, these warnings will help users to find ambiguity, redundancy and inconsistency in their own service description.