• Proceedings of the Korean Society for Language and Information Conference
• /
• 1992.02a
• /
• pp.111-120
• /
• 1992

• BMB Reports
• /
• v.37 no.6
• /
• pp.648-656
• /
• 2004

This paper deals with the development of a predictive probabilistic model, a composite dependency-reflecting model (CDRM), which was designed to detect core promoter regions and transcription start sites (TSS) in vertebrate genomic DNA sequences, an issue of some importance for genome annotation. The model actually represents a combination of first-, second-, third- and much higher order or long-range dependencies obtained using the expanded maximal dependency decomposition (EMDD) procedure, which iteratively decomposes data sets into subsets on the basis of dependency degree and patterns inherent in the target promoter region to be modeled. In addition, decomposed subsets are modeled by using a first-order Markov model, allowing the predictive model to reflect dependency between adjacent positions explicitly. In this way, the CDRM allows for potentially complex dependencies between positions in the core promoter region. Such complex dependencies may be closely related to the biological and structural contexts since promoter elements are present in various combinations separated by various distances in the sequence. Thus, CDRM may be appropriate for recognizing core promoter regions and TSSs in vertebrate genomic contig. To demonstrate the effectiveness of our algorithm, we tested it using standardized data and real core promoters, and compared it with some current representative promoter-finding algorithms. The developed algorithm showed better accuracy in terms of specificity and sensitivity than the promoter-finding ones used in performance comparison.

• The Journal of the Korea Contents Association
• /
• v.5 no.3
• /
• pp.175-181
• /
• 2005

Recent measurements of local-area and wide-area traffic have shown that network traffic exhibits at a wide range of scales-Self-similarity. Self-similarity is expressed by long term dependency, this is contradictory concept with Poisson model that have relativity short term dependency. Therefore, first of all for design and dimensioning of next generation communication network, traffic model that are reflected burstness and self-similarity is required. Here self-similarity can be characterized by Hurst parameter. In this paper, when different many data traffic being integrated under various environments is arrived to communication network, Hurst Parameter's change is analyzed and compared with simulation results.

• ETRI Journal
• /
• v.28 no.6
• /
• pp.803-806
• /
• 2006

This letter addresses a structural disambiguation method for Korean adverbs based on the correlative relation constraints between adverbs and modifiees, and the morphological context information of sentences. Using the proposed method, we improved the dependency parsing accuracy of adverbs from 79.2 to 89%. The experimental result shows that the proposed method is especially expert in parsing adverbs which can modify multiple word classes or have a long distance dependency relation to their modifiees.

• Journal of Korean Association for Spatial Structures
• /
• v.15 no.4
• /
• pp.73-80
• /
• 2015

The new rotary friction damper was developed using several two-nodal rotary frictional components with different clamping forces. Because of these components, the rotary friction damper can be activated by building movements due to lateral forces such as a wind and earthquake. In this paper, various dependency tests such as displacement amplitude, forcing frequency and long term cyclic loading were carried out to evaluate on the structural performance and the multi-slip mechanism of the new damper. Test results show that the multi-slip mechanism is verified and friction coefficients are dependent on displacement amplitute and forcing frequency except long term cyclic loading.

• ETRI Journal
• /
• v.44 no.3
• /
• pp.438-449
• /
• 2022

This study proposes a dependency grammar-based self-attention multilayered bidirectional long short-term memory (DG-M-Bi-LSTM) model for subject-predicate-object (SPO) tuple recognition from natural language (NL) sentences. To add recent knowledge to the knowledge base autonomously, it is essential to extract knowledge from numerous NL data. Therefore, this study proposes a high-accuracy SPO tuple recognition model that requires a small amount of learning data to extract knowledge from NL sentences. The accuracy of SPO tuple recognition using DG-M-Bi-LSTM is compared with that using NL-based self-attention multilayered bidirectional LSTM, DG-based bidirectional encoder representations from transformers (BERT), and NL-based BERT to evaluate its effectiveness. The DG-M-Bi-LSTM model achieves the best results in terms of recognition accuracy for extracting SPO tuples from NL sentences even if it has fewer deep neural network (DNN) parameters than BERT. In particular, its accuracy is better than that of BERT when the learning data are limited. Additionally, its pretrained DNN parameters can be applied to other domains because it learns the structural relations in NL sentences.

• Journal of information and communication convergence engineering
• /
• v.21 no.1
• /
• pp.9-16
• /
• 2023

The natural language on social network platforms has a certain front-to-back dependency in structure, and the direct conversion of Chinese text into a vector makes the dimensionality very high, thereby resulting in the low accuracy of existing text classification methods. To this end, this study establishes a deep learning model that combines a big data ultra-deep convolutional neural network (UDCNN) and long short-term memory network (LSTM). The deep structure of UDCNN is used to extract the features of text vector classification. The LSTM stores historical information to extract the context dependency of long texts, and word embedding is introduced to convert the text into low-dimensional vectors. Experiments are conducted on the social network platforms Sogou corpus and the University HowNet Chinese corpus. The research results show that compared with CNN + rand, LSTM, and other models, the neural network deep learning hybrid model can effectively improve the accuracy of text classification.

• Journal of KIISE
• /
• v.41 no.9
• /
• pp.652-665
• /
• 2014

Many important terminologies in biomedical text are expressed as abbreviations or acronyms. We newly suggest a semantic link topic model based on the concepts of topic and dependency link to disambiguate biomedical abbreviations and cluster long form variants of abbreviations which refer to the same senses. This model is a generative model inspired by the latent Dirichlet allocation (LDA) topic model, in which each document is viewed as a mixture of topics, with each topic characterized by a distribution over words. Thus, words of a document are generated from a hidden topic structure of a document and the topic structure is inferred from observable word sequences of document collections. In this study, we allow two distinct word generation to incorporate semantic dependencies between words, particularly between expansions (long forms) of abbreviations and their sentential co-occurring words. Besides topic information, the semantic dependency between words is defined as a link and a new random parameter for the link presence is assigned to each word. As a result, the most probable expansions with respect to abbreviations of a given abstract are decided by word-topic distribution, document-topic distribution, and word-link distribution estimated from document collection though the semantic dependency link topic model. The abstracts retrieved from the MEDLINE Entrez interface by the query relating 22 abbreviations and their 186 expansions were used as a data set. The link topic model correctly predicted expansions of abbreviations with the accuracy of 98.30%.

• Annual Conference on Human and Language Technology
• /
• 2018.10a
• /
• pp.323-328
• /
• 2018

한국어는 교착어적 특성으로 인하여 어미와 조사가 매우 발달되어 있다. 그러므로 영어와 같은 굴절어를 중심으로 설계된 UD를 한국어에 적용하는 것에는 많은 어려움이 있다. 이를 해결하기 위해서 형태통사적 특성이 유사한 일본의 UD 적용 사례를 살펴보고 한국어의 UD 적용 양상과의 비교 분석을 통해서 한국어의 UD 적용 및 개선 방안을 고찰해 보고자 한다. 한국어와 일본어는 동일한 교착어로서 비슷한 특성을 지니고 있으나, 주석의 기본 단위 설정에서 차이를 보이면서 UD를 적용하는 양상이 달라졌음을 확인하였다. 일본어의 UD 주석에서 형태 분석 기본 단위인 단단위(Short unit word, 長單位)를 기본 구문 주석 단위로 하되 장단위(Long unit word, 短單位)와 문절 정보를 이용하는 것처럼, 한국어에서도 형태 분석 단위를 기준으로 의존 관계를 주석하는 방안에 대해서도 고려할 필요가 있다.

• Proceedings of the Safety Management and Science Conference
• /
• 2011.04a
• /
• pp.329-338
• /
• 2011

Business processes are often of long duration, and include internal worker's decision making, which makes business processes to be exposed to many exceptional situations. These properties of business processes makes it difficult to design processes to support uncertainties from internal or external environments. The behavioral properties of business processes mainly depends on the data aspects of business processes. To formalize the data aspect of process modeling, this paper proposes a graph-based model, called Data Dependency Graph (DDG), constructed from dependency relationships specified between business data. The paper also defines a mechanism of describing a set of mapping rules that generates a process model semantically equivalent to a DDG, which is accomplished by allocating data dependencies to component activities.