• Journal of Korea Multimedia Society
• /
• v.8 no.9
• /
• pp.1248-1257
• /
• 2005

This paper designs and implements a sentence generation for an augmentive and alternative communication system(AAC). The AAC system is assistive communication device to help the mute language disorder communicate more freely and the system have an objected to reduce time and keystrokes for sentence generating. The paper of sentence generation make up for merits and demerits in the existing sentence generation method and in order to sentence generation. One aspect of Korean language that confines nouns defending on the verbs or postpositional words is used for sentence generation. The distinctive feature of this paper is to connect verbs to nouns using domain knowledge. We utilize the lexical information that exploits characteristics of Korean language for sentence generation. A comparison with other approaches is also presented. This sentence generation is based on lexical information by extracting characteristics of sentences.

• The KIPS Transactions:PartB
• /
• v.12B no.3 s.99
• /
• pp.323-328
• /
• 2005

This study is sentence generation for Augmentative and Alternative Communication. The object of sentence generation is to use in augmentative and alternative communication which is designed for those who are nonspeaking disorders. AAC generates human voice with using a sentence which is made up by the users. In order to construct a sentence, lexical information was adapted for a concept of augmentative and alternative communication. The lexical informations consist of noun types which can be connected to verbs, auxiliary words, conjugation of verbs and verb types. The system was made using lexical information and the usefulness of the sentence generation was measured by the system. The system constructed has functions of generation and saving right sentences, searching and inputting vocabularies.

• Journal of the HCI Society of Korea
• /
• v.5 no.1
• /
• pp.25-35
• /
• 2010

This paper proposes a method of automatically identifying sentence types in Korean and improving naturalness in sign language generation and speech synthesis using the identified sentence type information. In Korean, sentences are usually categorized into five types: declarative, imperative, propositive, interrogative, and exclamatory. However, it is also known that these types are quite ambiguous to identify in dialogues. In this paper, we present additional morphological and syntactic clues for the sentence type and propose a rule-based procedure for identifying the sentence type using these clues. The experimental results show that our method gives a reasonable performance. We also describe how the sentence type is used to generate non-manual signals in Korean-Korean sign language translation and appropriate intonation in Korean speech synthesis. Since the method of using sentence type information in speech synthesis and sign language generation is not much studied previously, it is anticipated that our method will contribute to research on generating more natural speech and sign language expressions.

• Korean Journal of Cognitive Science
• /
• v.20 no.4
• /
• pp.421-437
• /
• 2009

A chatting system is a computer program that simulates conversations between a human and a computer using natural language. In this paper, we propose a statistical model to generate natural chatting sentences when keywords and speech acts are input. The proposed model first finds Eojeols (Korean spacing units) including input keywords from a corpus, and generate sentence candidates by using appearance information and syntactic information of Eojeols surrounding the found Eojeols. Then, the proposed model selects one among the sentence candidates by using a language model based on speech act information, co-occurrence information between Eojeols, and syntactic information of each Eojeol. In the experiment, the proposed model showed the better correct sentence generation rate of 86.2% than a previous conventional model based on a simple language model.

• Smart Media Journal
• /
• v.10 no.1
• /
• pp.63-69
• /
• 2021

When a person sees a sentence and understands the sentence, the person understands the sentence by reminiscent of the main word in the sentence as an image. Text-to-image is what allows computers to do this associative process. The previous deep learning-based text-to-image model extracts text features using Convolutional Neural Network (CNN)-Long Short Term Memory (LSTM) and bi-directional LSTM, and generates an image by inputting it to the GAN. The previous text-to-image model uses basic embedding in text feature extraction, and it takes a long time to train because images are generated using several modules. Therefore, in this research, we propose a method of extracting features by using the attention mechanism, which has improved performance in the natural language processing field, for sentence embedding, and generating an image by inputting the extracted features into the GAN. As a result of the experiment, the inception score was higher than that of the model used in the previous study, and when judged with the naked eye, an image that expresses the features well in the input sentence was created. In addition, even when a long sentence is input, an image that expresses the sentence well was created.

• Proceedings of the IEEK Conference
• /
• 2001.06c
• /
• pp.203-206
• /
• 2001

In the EKMT system, the part of Korea generation makes Korea sentence by using information obtained in the part of transfer. In the case of Korea generation, the conventional EKMT system don't arrange hierarchical word order and performs word order in the only modifier word. This paper proposes Korean adverb odering rule in English-Korean Machine Translation system which generates Korean sentence.

• ETRI Journal
• /
• v.39 no.4
• /
• pp.455-466
• /
• 2017

This study focuses on a method for sequential data augmentation in order to alleviate data sparseness problems. Specifically, we present corpus expansion techniques for enhancing the coverage of a language model. Recent recurrent neural network studies show that a seq2seq model can be applied for addressing language generation issues; it has the ability to generate new sentences from given input sentences. We present a method of corpus expansion using a sentence-chain based seq2seq model. For training the seq2seq model, sentence chains are used as triples. The first two sentences in a triple are used for the encoder of the seq2seq model, while the last sentence becomes a target sequence for the decoder. Using only internal resources, evaluation results show an improvement of approximately 7.6% relative perplexity over a baseline language model of Korean text. Additionally, from a comparison with a previous study, the sentence chain approach reduces the size of the training data by 38.4% while generating 1.4-times the number of n-grams with superior performance for English text.

• Journal of KIISE:Computing Practices and Letters
• /
• v.12 no.2
• /
• pp.119-128
• /
• 2006

As dialogue systems are widely required, the research on natural language generation in dialogue has raised attention. Contrary to conventional dialogue systems that reply to the user with a set of predefined answers, a newly developed dialogue system generates them dynamically and trains the answers to support more flexible and customized dialogues with humans. This paper proposes an evolutionary method for generating sentences using interactive genetic programming. Sentence plan trees, which stand for the sentence structures, are adopted as the representation of genetic programming. With interactive evolution process with the user, a set of customized sentence structures is obtained. The proposed method applies to a dialogue-based train booking agent and the usability test demonstrates the usefulness of the proposed method.

• Annual Conference on Human and Language Technology
• /
• 1993.10a
• /
• pp.559-570
• /
• 1993

In this article we present a rapid development of a Korean to English translation system, by the help of general English generator, PENMAN. PENMAN is an English sentence generation system, of which input language is a language specially devised for sentence generation, named Sentence Planning Language(SPL). The language SPL has various features that are necessary for generating sentences, covering both syntactic and semantic features. In this development we integrated a Korean language parser based on dependency grammar and the English sentence generator PENMAN, bridging two systems through a converting module, which converts dependency structures produced by Korean parser into SPL for PENMAN.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.10
• /
• pp.2718-2731
• /
• 2023

This paper presents a method of using syntax and shallow semantic analysis for Vietnamese question generation (QG). Specifically, our proposed technique concentrates on investigating both the syntactic and shallow semantic structure of each sentence. The main goal of our method is to generate questions from a single sentence. These generated questions are known as factoid questions which require short, fact-based answers. In general, syntax-based analysis is one of the most popular approaches within the QG field, but it requires linguistic expert knowledge as well as a deep understanding of syntax rules in the Vietnamese language. It is thus considered a high-cost and inefficient solution due to the requirement of significant human effort to achieve qualified syntax rules. To deal with this problem, we collected the syntax rules in Vietnamese from a Vietnamese language textbook. Moreover, we also used different natural language processing (NLP) techniques to analyze Vietnamese shallow syntax and semantics for the QG task. These techniques include: sentence segmentation, word segmentation, part of speech, chunking, dependency parsing, and named entity recognition. We used human evaluation to assess the credibility of our model, which means we manually generated questions from the corpus, and then compared them with the generated questions. The empirical evidence demonstrates that our proposed technique has significant performance, in which the generated questions are very similar to those which are created by humans.