• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.192-201
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• 2007

This paper is a study on constructing a natural language interface to database, concentrating on generating textual answers. TGEN, a system that generates textual answer from query result tables is presented. The TGEN architecture guarantees its portability across domains. A combination of a frame-based approach and natural language generation techniques in the TGEN provides text fluency and text flexibility. The implementation result shows that this approach is feasible while a deep NLG approach is still far to be reached.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.205-213
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• 2010

We introduce a natural language sentence generation (NLG) method based on learning of word-association patterns. Existing NLG methods assume the inherent grammar rules or use template based method. Contrary to the existing NLG methods, the presented method learns the words-association patterns using only the co-occurrence of words without additional information such as tagging. We employ the hypernetwork method to analyze and represent the words-association patterns. As training going on, the model complexity is increased. After completing each training phase, natural language sentences are generated using the learned hyperedges. The number of grammatically plausible sentences increases after each training phase. We confirm that the proposed method has a potential for learning grammatical properties of training corpuses by comparing the diversity of grammatical rules of training corpuses and the generated sentences.

• Journal of Digital Convergence
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• v.18 no.11
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• pp.215-226
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• 2020

Chatbot is emerging as an important interface window for business. This change is due to the continued development of chatbot-related research from NLP to NLU and NLG. However, the reality is that the methodological study of drawing domain knowledge and developing it into a user-friendly interactive interface is weak in the process of developing chatbot. In this paper, in order to present the process criteria of chatbot development, we applied it to the actual project based on the methodology presented in the previous paper and improved the development methodology. In conclusion, the productivity of the test phase, which is the most important step, was improved by 33.3%, and the number of iterations was reduced to 37.5%. Based on these results, the "3 Phase and 17 Tasks Development Methodology" was presented, which is expected to dramatically improve the trial and error of the chatbot development.

• Journal of the Korean Society for Library and Information Science
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• v.51 no.4
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• pp.99-120
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• 2017

In order to develop a semiautomatic support system that allows researchers concerned to efficiently analyze the technical trends for the ever-growing industry and market. This paper introduces a couple of Korean sentence generation models that can automatically generate definitional statements as well as descriptions of technical terms and concepts. The proposed models are based on a deep learning model called LSTM (Long Sort-Term Memory) capable of effectively labeling textual sequences by taking into account the contextual relations of each item in the sequences. Our models take technical terms as inputs and can generate a broad range of heterogeneous textual descriptions that explain the concept of the terms. In the experiments using large-scale training collections, we confirmed that more accurate and reasonable sentences can be generated by CHAR-CNN-LSTM model that is a word-based LSTM exploiting character embeddings based on convolutional neural networks (CNN). The results of this study can be a force for developing an extension model that can generate a set of sentences covering the same subjects, and furthermore, we can implement an artificial intelligence model that automatically creates technical literature.