• Journal of Computing Science and Engineering
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• v.10 no.3
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• pp.95-101
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• 2016

In this study, we propose a new system for constructing parallel corpora for sign languages, which are generally under-resourced in comparison to spoken languages. In order to achieve scalability and accessibility regarding data collection and corpus construction, our system utilizes deep learning-based techniques and predicts depth information to perform pose estimation on hand information obtainable from video recordings by a single RGB camera. These estimated poses are then transcribed into expressions in SignWriting. We evaluate the accuracy of hand tracking and hand pose estimation modules of our system quantitatively, using the American Sign Language Image Dataset and the American Sign Language Lexicon Video Dataset. The evaluation results show that our transcription system has a high potential to be successfully employed in constructing a sizable sign language corpus using various types of video resources.

• The KIPS Transactions:PartB
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• v.19B no.1
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• pp.43-52
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• 2012

Semantic role labeling is the research area analyzing the semantic relationship between elements in a sentence and it is considered as one of the most important semantic analysis research areas in natural language processing, such as word sense disambiguation. However, due to the lack of the relative linguistic resources, Korean semantic role labeling research has not been sufficiently developed. We, in this paper, propose an automatic predicate-argument analyzer to begin constructing the Korean PropBank which has been widely utilized in the semantic role labeling. The analyzer has mainly two components: the semantic lexical dictionary and the automatic predicate-argument extractor. The dictionary has the case frame information of verbs and the extractor is a module to decide the semantic class of the argument for a specific predicate existing in the syntactically annotated corpus. The analyzer developed in this research will help the construction of Korean PropBank and will finally play a big role in Korean semantic role labeling.

• The Journal of Korean Association of Computer Education
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• v.6 no.2
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• pp.9-21
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• 2003

This paper proposes an automatic construction system for transliteration dictionary from English-Korean parallel corpus. The system works in 3 steps: it extracts all nouns from Korean documents as the first step, filters transliterated foreign word nouns out of them with the language identification method as the second step, and extracts the corresponding English words by using a probabilistic alignment method as the final step. Specially, the fact that there is a corresponding English word in most cases, is utilized to extract the purely transliterated part from a Koreans word phrase, which is usually used in combined forms with Korean endings(Eomi) or particles(Josa). Moreover, the direct phonetic comparison is done to the words in two different alphabet systems without converting them to the same alphabet system. The experiment showed that the performance was influenced by the first and the second preprocessing steps; the most efficient model among manually preprocessed ones showed 85.4% recall, 91.0% precision and the most efficient model among fully automated ones got 68.3% recall, 89.2% precision.

• The KIPS Transactions:PartB
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• v.14B no.3 s.113
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• pp.231-240
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• 2007

In this paper, we propose an automatic extraction model for unknown translations and implement an unknown translation extraction system using the proposed model. The proposed model as a phrase-alignment model is incorporated with three models: a phrase-boundary model, a language model, and a translation model. Using the proposed model we implement the system for extracting unknown translations, which consists of three parts: construction of parallel corpora, alignment of Korean and English words, extraction of unknown translations. To evaluate the performance of the proposed system we have established the reference corpus for extracting unknown translation, which comprises of 2,220 parallel sentences including about 1,500 unknown translations. Through several experiments, we have observed that the proposed model is very useful for extracting unknown translations. In the future, researches on objective evaluation and establishment of parallel corpora with good quality should be performed and studies on improving the performance of unknown translation extraction should be kept up.

• Journal of the Korea Convergence Society
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• v.12 no.11
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• pp.109-117
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• 2021

Recently, the use of speech-based interfaces is increasing as a means for human-computer interaction (HCI). Accordingly, interest in post-processors for correcting errors in speech recognition results is also increasing. However, a lot of human-labor is required for data construction. in order to manufacture a sequence to sequence (S2S) based speech recognition post-processor. To this end, to alleviate the limitations of the existing construction methodology, a new data construction method called Back TranScription (BTS) was proposed. BTS refers to a technology that combines TTS and STT technology to create a pseudo parallel corpus. This methodology eliminates the role of a phonetic transcriptor and can automatically generate vast amounts of training data, saving the cost. This paper verified through experiments that data should be constructed in consideration of text style and domain rather than constructing data without any criteria by extending the existing BTS research.

• Journal of Korea Society of Industrial Information Systems
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• v.12 no.4
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• pp.25-31
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• 2007

An automatic term-network construction system is proposed in this paper. This system uses the statistical values of the terms appeared in a document corpus. The 186 oral history documents collected from the Saemangeum area of Chollapuk-do, Korea, are used for the research. The term relationships presented in the term-network are decided by the cosine similarities of the term vectors. The number of the terms extracted from the documents is about 1700. The system is able to show the term relationships from the term-network as quickly as like a real-time system. The way of this term-network construction is expected as one of the methods to construct the ontology system and to support the semantic retrieval system in the near future.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.467-474
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• 2013

Construction contactors must continuously seek to improve their cash flows, which reside at the heart of their financial success. They require careful planning, analysis, and optimization to avoid the risk of bankruptcy, remain profitable, and secure long-term growth. Sources of cash include bank loans and retained earnings, which are conceptually similar in that they both incur a cost of capital. Financial management therefore requires accurate yet customizable modeling capabilities that can quantify all expenses, including said cost of capital. However, currently existing cash flow models in construction engineering and management have strongly simplified the manner in which interest is assessed, which may even lead to overstating it at a disadvantage to contractors. The variable nature of cash balances, especially in the early phases of construction projects, contribute to this challenging issue. This research therefore extends a new cash flow model with an accurate interest calculation. It utilizes singularity functions, so called because of their ability to flexibly model changes across any number of different ranges. The interest function is continuous for activity costs of any duration and allows the realistic case that activities may begin between integer time periods, which are often calendar months. Such fractional interest calculation has hitherto been lacking from the literature. It also provides insights into the self-referential behavior of compound interest for variable cash balances. The contribution of this study is twofold; augmenting the corpus of financial analysis theory with a new interest formula, whose strengths include its generic nature and that it can be evaluated at any fractional value of time, and providing construction managers with a tool to help improve and fine-tune the financial performance of their projects.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.254-262
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• 2009

As the trend of e-business activities develop, customers come into contact with products through on-line shopping sites and lots of customers refer product reviews before the purchasing on-line. However, as the volume of product reviews grow, it takes a great deal of time and effort for customers to read and evaluate voluminous product reviews. Lately, attention is being paid to Opinion Mining(OM) as one of the effective solutions to this problem. In this paper, we propose an efficient method for opinion sentiment classification of product reviews using product specific context information of words occurred in the reviews. We define the context information of words and propose the application of context for sentiment classification and we show the performance of our method through the experiments. Additionally, in case of word corpus construction, we propose the method to construct word corpus automatically using the review texts and review scores in order to prevent traditional manual process. In consequence, we can easily get exact sentiment polarities of opinion words in product reviews.

• Journal of the Korea Society of Computer and Information
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• v.13 no.7
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• pp.11-18
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• 2008

Korean morphological analyzer generally generates multiple candidates, and then selects the most likely one among multiple candidates. As the number of candidates increases, the chance that the correctly analyzed candidate is included in the candidate list also grows. This process, however, increases ambiguity and then deteriorates the performance. In this paper, we propose a new rule-based model that produces one best analysis. The analysis rules are automatically extracted from large amount of Part-of-Speech tagged corpus, and the proposed model does not require any manual construction cost of analysis rules, and has shown high success rate of analysis. Futhermore, the proposed model can reduce the ambiguities and computational complexities in the candidate selection phase because the model produces one analysis when it can successfully analyze the given word. By combining the conventional probability-based model. the model can also improve the performance of analysis when it does not produce a successful analysis.

• MALSORI
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• v.55
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• pp.77-91
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• 2005

In this paper, we propose an XML based meta-data specification for industrial speech databases. Building speech databases is very time-consuming and expensive. Recently, by the government supports, huge amount of speech corpus has been collected as speech databases. However, the formats and meta-data for speech databases are different depending on the constructing institutions. In order to advance the reusability and portability of speech databases, a standard representation scheme should be adopted by all speech database construction institutions. ETRI proposed a XML based annotation scheme [51 for speech databases, but the scheme has too simple and flat modeling structure, and may cause duplicated information. In order to overcome such disadvantages in this previous scheme, we first define the speech database more formally and then identify object appearing in speech databases. We then design the data model for speech databases in an object-oriented way. Based on the designed data model, we develop the meta-data specification for industrial speech databases.