• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.11 no.5
• /
• pp.1677-1684
• /
• 2010

Recently, due to the development of mobile phone and supply of smart phone, many contents have been developed. Especially, since the small-sized cameras are equiped in mobile devices, people are interested in the image based contents development, and it also becomes important part in their practical use. Among them, the character recognition system can be widely used in the applications such as blind people guidance systems, automatic robot navigation systems, automatic video retrieval and indexing systems, automatic text translation systems. Therefore, this paper proposes a system that is able to extract text area from the natural images captured by smart phone camera. The individual characters are recognized and result is output in voice. Text areas are extracted using Adaboost algorithm and individual characters are recognized using error back propagated neural network.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.1
• /
• pp.191-203
• /
• 2009

To provide dynamic services for users in ubiquitous computing environments by considering context in real-time, in our previous work we proposed Metaservice concept, the description specification and the process for building a Metaservice library. However, our previous process generates separated models - UML, OWL, OWL-S based models - from each step, so it did not provide the established method for translation between models. Moreover, it premises aid of experts in various ontology languages, ontology editing tools and the proposed Metaservice specification. In this paper, we design the translation process from domain ontology in OWL to Metaservice Library in OWL-S and develop a visual tool in order to enable non-experts to generate consistent models and to construct a Metaservice library. The purpose of the Metaservice Library translation process is to maintain consistency in all models and to automatically generate OWL-S code for Metaservice library by integrating existing OWL model and Metaservice model.

• Journal of KIISE:Software and Applications
• /
• v.32 no.5
• /
• pp.385-395
• /
• 2005

Long sentence analysis has been a critical problem in machine translation because of high complexity. The methods of intra-sentence segmentation have been proposed to reduce parsing complexity. This paper presents the intra-sentence segmentation method based on maximum entropy probability model to increase the coverage and accuracy of the segmentation. We construct the rules for choosing candidate segmentation positions by a teaming method using the lexical context of the words tagged as segmentation position. We also generate the model that gives probability value to each candidate segmentation positions. The lexical contexts are extracted from the corpus tagged with segmentation positions and are incorporated into the probability model. We construct training data using the sentences from Wall Street Journal and experiment the intra-sentence segmentation on the sentences from four different domains. The experiments show about $88\%$ accuracy and about $98\%$ coverage of the segmentation. Also, the proposed method results in parsing efficiency improvement by 4.8 times in speed and 3.6 times in space.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2002.06a
• /
• pp.148-155
• /
• 2002

Multithreaded models improve the efficiency of parallel systems by combining inner parallelism, asynchronous data availability and the locality of von Neumann model. This model executes thread code which is generated by compiler and of which quality is given by the method of generation. But multithreaded models have the demerit that execution model is restricted to a specific platform. On the contrary, Java has the platform independency, so if we can translate from threads code to Java bytecode, we can use the advantages of multithreaded models in many platforms. Java executes Java bytecode which is intermediate language format for Java virtual machine. Java bytecode plays a role of an intermediate language in translator and Java virtual machine work as back-end in translator. But, Java bytecode which is translated from multithreaded models have the demerit that it is not secure. This paper, multhithread code whose feature of platform independent can execute in java virtual machine. We design and implement translator which translate from thread code of multithreaded code to Java bytecode and which check secure problems from Java bytecode.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.8
• /
• pp.341-354
• /
• 2023

The title of a document is the brief summarization of the document. Readers can easily understand a document if we provide them with its title in their preferred styles and the languages. In this research, we propose a cross-lingual and style-based title generation model using multiple adapters. To train the model, we need a parallel corpus in several languages with different styles. It is quite difficult to construct this kind of parallel corpus; however, a monolingual title generation corpus of the same style can be built easily. Therefore, we apply a zero-shot strategy to generate a title in a different language and with a different style for an input document. A baseline model is Transformer consisting of an encoder and a decoder, pre-trained by several languages. The model is then equipped with multiple adapters for translation, languages, and styles. After the model learns a translation task from parallel corpus, it learns a title generation task from monolingual title generation corpus. When training the model with a task, we only activate an adapter that corresponds to the task. When generating a cross-lingual and style-based title, we only activate adapters that correspond to a target language and a target style. An experimental result shows that our proposed model is only as good as a pipeline model that first translates into a target language and then generates a title. There have been significant changes in natural language generation due to the emergence of large-scale language models. However, research to improve the performance of natural language generation using limited resources and limited data needs to continue. In this regard, this study seeks to explore the significance of such research.

• Journal of KIISE:Software and Applications
• /
• v.32 no.4
• /
• pp.312-326
• /
• 2005

There has been increasing interest in English-to-Korean transliteration recently. Previous ,works are related to a direct method like $\rightarrow$Korean graphemes> and a pivot method like $\rightarrow$English phoneme$\rightarrow$Korean graphemes>. Though most of the previous works focus on the direct method, transliteration, however, is a phonetic process rather than an orthographic one. In this point of view, we present an English-Korean transliteration model using grapheme and phoneme information. Unlike the previous works, our method uses phonetic information such as phonemes and their context. Moreover, we also use graphemes corresponding to phonemes. Our method shows about $60\%$ word accuracy.

• The KIPS Transactions:PartB
• /
• v.9B no.4
• /
• pp.445-452
• /
• 2002

In Korean-English mixed documents, translated technical words are usually used with the attached full words or original words enclosed with parenthesis. In this paper, a collective method is presented to recognize and extract the translation phrases with using a base translation dictionary. In order to process the unregistered title words and translation words in the dictionary, a phonetic similarity matching method, a translation partial matching method, and a compound word matching method are newly proposed. The experiment result of each method was measured in F-measure(the alpha is set to 0.4) ; exact matching of dictionary terms as a baseline method showed 23.8%, the hybrid method of translation partial matching and phonetic similarity matching 75.9%, and the compound word matching method including the hybrid method 77.3%, which is 3.25 times better than the baseline method.

• The KIPS Transactions:PartD
• /
• v.15D no.6
• /
• pp.825-840
• /
• 2008

Refactoring is a kind of software modification process that improves system qualities internally but maintains system functions externally. What should be improved on the existing source codes should take precedence over the others in such a modification process using this refactoring. Martin Fowler and Kent Beck proposed a method that identifies code smells for this purpose. Also, some studies on determining what refactoring will be applied to which targets through detecting code smells in codes were presented. However, these studies have a lot of disadvantages that show a lack of precise description for such code smells and detect limited code smells only. In addition, these studies showed other disadvantages that generate ambiguity in behavior preservation due to the fact that a description method of pre-conditions for the behavior preservation is included in a refactoring process or unformalized. Thus, our study represents a precise specification of code smells using OCL and proposes a framework that performs a refactoring process through the automatic detection of code smells using an OCL interpreter. Furthermore, we perform the automatic detection in which the code smells are be specified by using OCL to the java program and verify its applicability and effectivity through applying a refactoring process.

• Journal of Internet of Things and Convergence
• /
• v.4 no.1
• /
• pp.31-36
• /
• 2018

In this paper, we developed a chatbot system using the Django framework using the KakaoTalk API so that college students can easily search for important information in their university. Unlike existing chatbot systems that provide only specific information, the chatbot developed in this research automatically provides search results for various types of user queries such as weather, YouTube, Naver real-time ranking search and language translation as well as important information within their own university. We developed a module using Apache, Python and Django in AWS Ubuntu server and developed a chatbot system that automatically responds to user queries by communicating with KakaoTalk server using KakaoTalk API and BeautifulSoup. The system developed in this study is expected to be applicable to the future university entrance information promotion and election promotion system.

• Journal of KIISE:Computing Practices and Letters
• /
• v.16 no.10
• /
• pp.1000-1004
• /
• 2010

A large number of current language processing systems use a part-of-speech tagger for preprocessing. Most language processing systems required a tagger with the highest possible accuracy. Specially, the use of domain-specific advantages has become a hot issue in machine translation community to improve the translation quality. This paper addresses a method for customizing an HMM or LHMM based English tagger from general domain to specific domain. The proposed method is to semi-automatically customize the output and transition probabilities of HMM or LHMM using domain-specific raw corpus. Through the experiments customizing to Patent domain, our LHMM tagger adapted by the proposed method shows the word tagging accuracy of 98.87% and the sentence tagging accuracy of 78.5%. Also, compared with the general tagger, our tagger improved the word tagging accuracy of 2.24% (ERR: 66.4%) and the sentence tagging accuracy of 41.0% (ERR: 65.6%).