• Information Systems Review
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• v.25 no.3
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• pp.43-57
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• 2023

In this study, we developed an AI-based system to generate sentences that assist in creating Korean poetry. Instead of replacing the creative aspect of composition, which is considered a unique domain of humans, the focus was on generating foundational sentences to enhance human imagination efficiently. By conducting interviews with poets, the researchers extracted sentences from eight distinct datasets, enabling the generation of poetry across eight different genres. This study stands out for its innovation in developing a method for crafting literary works in Korean. Its significance lies in its potential to facilitate the creation of diverse literary forms such as essays, prose, or novels.

• 한국게임학회지
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• v.8 no.1
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• pp.32-38
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• 2011

The help or guide sentences of educational math games which use mathematical statements need to represent graphical forms for the learners of the game generation whose cognitive style is graphic first. In this paper, we proposed an object-based visual representation method for mathematical statements. It has object-based description rules to use graphical symbols and mathematical symbols with text words. It is easy to describe or to understand accurately mathematical meaning and is also fast for learners to read for understanding. The proposed method is good for learners of the game generation to get the help as scaffolding for learning math by educational games.

• Proceedings of the Korean Society of Computer Information Conference
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• 2016.07a
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• pp.17-20
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• 2016

딥러닝 기법이 발달함에 따라 텍스트에 내재된 의미 및 구문을 어떠한 벡터 공간 상에 표현하기 위한 언어 모델이 활발히 연구되어 왔다. 이를 통해 자연어 처리를 기반으로 하는 감성 분석 및 문서 분류, 기계 번역 등의 분야가 진보되었다. 그러나 대부분의 언어 모델들은 텍스트에 나타나는 단어들의 일반적인 패턴을 학습하는 것을 기반으로 하기 때문에, 문서 요약이나 스토리텔링, 의역된 문장 판별 등과 같이 보다 고도화된 자연어의 이해를 필요로 하는 연구들의 경우 주어진 텍스트의 주제 및 의미를 고려하기에 한계점이 있다. 이와 같은 한계점을 고려하기 위하여, 본 연구에서는 기존의 LSTM 모델을 변형하여 문서 주제와 해당 주제에서 단어가 가지는 문맥적인 의미를 단어 벡터 표현에 반영할 수 있는 새로운 언어 학습 모델을 제안하고, 본 제안 모델이 문서의 주제를 고려하여 문장을 자동으로 생성할 수 있음을 보이고자 한다.

• Proceedings of the Korean Society for Cognitive Science Conference
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• 2010.05a
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• pp.60-65
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• 2010

Language learning involves linguistic environments around the learner. So the variation in training input to which the learner is exposed has been linked to their language learning. We explore how linguistic experiences can cause differences in learning linguistic structural features, as investigate in a probabilistic graphical model. We manipulate the amounts of training input, composed of natural linguistic data from animation videos for children, from holistic (one-word expression) to compositional (two- to six-word one) gradually. The recognition and generation of sentences are a "probabilistic" constraint satisfaction process which is based on massively parallel DNA chemistry. Random sentence generation tasks succeed when networks begin with limited sentential lengths and vocabulary sizes and gradually expand with larger ones, like children's cognitive development in learning. This model supports the suggestion that variations in early linguistic environments with developmental steps may be useful for facilitating language acquisition.

• The Transactions of the Korea Information Processing Society
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• v.7 no.5
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• pp.1317-1329
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• 2000

The advanced techniques for video processing and computer graphics enables a sign language education system to appear. the system is capable of showing a sign language motion for an arbitrary sentence using the captured video clips of sign language words. In this paper, a method is suggested which generates the frames between the last frame of a word and the first frame of its following word in order to animate hand motion. In our method, we find hand locations and angles which are required for in between frame generation, capture and store the hand images at those locations and angles. The inbetween frames generation is simply a task of finding a sequence of hand angles and locations. Our method is computationally simple and requires a relatively small amount of disk space. However, our experiments show that inbetween frames for the presentation at about 15fps (frame per second) are achieved so tat the smooth animation of hand motion is possible. Our method improves on previous works in which computation cost is relativey high or unnecessary images are generated.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.12
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• pp.91-99
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• 1994

This paper proposes the Korean generator which is used in a Japanese to Korean translation system. The established Korean generator for japanese to Korean translation uses the entire sentence structure to generate Korean, or directly translates each morpheme. But in this paper, the generator generates Korean, based on a short clause. Japanese and Korean have similarities in short clauses, including the word order. But in the Korean generation, they also have many differences within short clauses. The analysed features in Japanese and Korean are in the wrong order. Therefore the Korean generator which takes it into consideration is needed. We propose a annotational algorithm for the Korean generation and apply it to the translation of postpositions, declinable words and auxiliary verbs. We also consider Korean phonological phenomenon that can't be extracted from the analyed Japanese informations. In the end, we can translate the Japanese conjugation that can't be easily dealt in the procedural method.

• Journal of the Korean Society for information Management
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• v.9 no.2
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• pp.43-79
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• 1992

This study proposes a Korean aut.omatic abstracting system in microbiology by applying Case Grammar, Concept Dependency Grammar, and Unification-Based Grammar(PATR- I[. DCG). The sample abstracts are analyzesd to clarify the ideal structure of abstract-a purpose sentence as first sentcnce, 2-3 method and result sentences as middle sentences, and a conclusion sentence as last sentences. To extract and refine the representative sentences constructing an automated abstract requires tht. rules giving the role features to nouns. And t.he rules rearranging the extracted sentences and the rules generating the abstract sentences arc also required. Evaluat.ing the effic~ency of this system. the method used in this automatic abstracting system needs thc more precise role features and the rules of sentence generation to reach the level of the author abstracts.

• Korean Journal of Cognitive Science
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• v.24 no.4
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• pp.301-321
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• 2013

For the task of sentence ordering, this paper attempts to utilize the Entity-Grid model, a type of entity-based modeling approach, as well as Latent Semantic analysis, which is based on vector space modeling, The task is well known as one of the fundamental tools used to measure text coherence and to enhance text generation processes. For the implementation of the Entity-Grid model, we attempt to use the syntactic roles of the nouns in the Korean text for the ordering task, and measure its impact on the result, since its contribution has been discussed in previous research. Contrary to the case of German, it shows a positive result. In order to obtain the information on the syntactic roles, we use a strategy of using Korean case-markers for the nouns. As a result, it is revealed that the cues can be helpful to measure text coherence. In addition, we compare the results with the ones of the LSA-based model, discussing the advantages and disadvantages of the models, and options for future studies.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.983-987
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• 2009

One of the critical themes in the language acquisition is its exposure to linguistic environments. Linguistic environments, which interact with infants, include not only human beings such as its parents but also artificially crafted linguistic media as their functioning elements. An infant learns a language by exploring these extensive language environments around it. Based on such large linguistic data exposure, we propose a machine learning based method on the cognitive mechanism that simulate flexibly and appropriately infant's language learning. The infant's initial stage of language learning comes with sentence learning and creation, which can be simulated by exposing it to a language corpus. The core of the simulation is a memory-based learning model which has language hypernetwork structure. The language hypernetwork simulates developmental and progressive language learning using the structure of new data stream through making it representing of high level connection between language components possible. In this paper, we simulates an infant's gradual and developmental learning progress by training language hypernetwork gradually using 32,744 sentences extracted from video scripts of commercial animation movies for children.

• Journal of KIISE
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• v.45 no.2
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• pp.157-164
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• 2018

Due to the revolutionary advances in deep learning, performance of pattern recognition has increased significantly in many applications like speech recognition and image recognition, and some systems outperform human-level intelligence in specific domains. Unlike pattern recognition, in this paper, we focus on generating Korean sentences based on a few Korean sentences. We apply variational recurrent auto-encoder (VRAE) and modify the model considering some characteristics of Korean sentences. To reduce the number of words in the model, we apply a word spacing model. Also, there are many Korean sentences which have the same meaning but different word order, even without subjects or objects; therefore we change the unidirectional encoder of VRAE into a bidirectional encoder. In addition, we apply an interpolation method on the encoded vectors from the given sentences, so that we can generate new sentences which are similar to the given sentences. In experiments, we confirm that our proposed method generates better sentences which are semantically more similar to the given sentences.