• Annual Conference on Human and Language Technology
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• 2019.10a
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• pp.533-536
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• 2019

의존 구문 분석은 문장 구성 성분 간의 의존 관계를 분석하는 태스크로, 자연어 이해의 대표적인 과제 중 하나이다. 본 논문에서는 한국어 의존 구문 분석의 성능 향상을 위해 Deep Bi-Affine Network와 Left to Right Dependency Parser를 적용하고, 새롭게 한국어의 언어적 특징을 반영한 Right to Left Dependency Parser 모델을 제안한다. 3개의 의존 구문 분석 모델에 단어 표현을 생성하는 방법으로 ELMo, BERT 임베딩 방법을 적용하고 여러 종류의 모델을 앙상블하여 세종 의존 구문 분석 데이터에 대해 UAS 94.50, LAS 92.46 성능을 얻을 수 있었다.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.

• Proceedings of the Korean Society for Language and Information Conference
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• 2005.06a
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• pp.1-16
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• 2005

• Annual Conference on Human and Language Technology
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• 2018.10a
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• pp.19-22
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• 2018

본 논문에서는 기존 Stack Pointer Network의 의존 파싱 모델을 확장한 Bi-Stack Pointer Network를 제안한다. Stack Pointer Network는 기존의 Pointer Network에 내부 stack을 만들어 전체 문장을 읽어 dependency tree를 구성한다. stack은 tree의 깊이 우선 탐색을 통해 선정되고 Pointer Network는 stack의 top 단어(head)의 자식(child)을 선택한다. 제안한 모델은 기존의 Stack Pointer Network가 지배소(head)정보로 의존소(child)를 예측하는 부분에 Biaffine attention을 통해 의존소(child)에서 지배소(head)를 예측하는 방향을 추가하여 양방향 예측이 가능하게 한 모델이다. 실험 결과, 제안 Bi-Stack Pointer Network모델은 UAS 91.53%, LAS 90.93%의 성능을 보여주어 기존 최고 성능을 개선시켰다.

• The Journal of the Acoustical Society of Korea
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• v.21 no.6
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• pp.543-551
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• 2002

In this paper, we present an efficient decoding method that performs in real time for 20k word continuous speech recognition task. Basic search method is a one-pass Viterbi decoder on the search space constructed from the novel language model network. With the consistent search space representation derived from various language models by the LM network, we incorporate basic pruning strategies, from which tokens alive constitute a dynamic search space. To facilitate post-processing, it produces a word graph and a N-best list subsequently. The decoder is tested on the database of 20k words and evaluated with respect to accuracy and RTF.

• Journal of Korean society of Dental Hygiene
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• v.23 no.6
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• pp.451-458
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• 2023

Objectives: The purpose of this study is to explore the research trends of elderly oral health through a language network analysis. Methods: A total of 354 published studies with 668 keywords were collected from the Research Information Sharing Service (RISS) between 2000 and 2022. Language network analysis was performed using Textom 6.0, Ucinet 6.774, and NetDraw 2.183. Results: The most frequent keywords were 'elderly', 'oral health', 'quality of life', and 'OHIP-14'. The result of frequency-inverse document frequent keywords showed similar results to the most frequent keywords. The N-gram of keywords shows that 'elderly', 'oral health' (18 times) and 'elderly', 'depression' (7 times). As a results of the analysis of degree centrality and between centrality, 'elderly', 'oral health', and 'quality of life' were found to be high. The CONCOR analysis identified the main clusters of 'quality of life', 'oral health behavior', 'health', and 'oral function disorder'. Conclusions: The results of the current study could be available to know research trends in elderly oral health and it is necessary to improve more comprehensive study in follow-up study.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.37-40
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• 2021

This paper presents a design of a speech to sign language converter for deaf and hard of hearing people. The device is low-cost, low-power consumption, and it can be able to work entirely offline. The speech recognition is implemented using an open-source API, Pocketsphinx library. In this work, we proposed a context-oriented language model, which measures the similarity between the recognized speech and the predefined speech to decide the output. The output speech is selected from the recommended speech stored in the database, which is the best match to the recognized speech. The proposed context-oriented language model can improve the speech recognition rate by 21% for working entirely offline. A decision module based on determining the similarity between the two texts using Levenshtein distance decides the output sign language. The output sign language corresponding to the recognized speech is generated as a set of sequential images. The speech to sign language converter is deployed on a Raspberry Pi Zero board for low-cost deaf assistive devices.

• International Journal of Computer Science & Network Security
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• v.22 no.8
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• pp.55-60
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• 2022

Specific Language Impairment is one of the serious disorders that interferes with spontaneous communication skills in children. Children suffering from this disorder may have reading, speaking, or listening impairments, and such type of disorders are also termed Autism Speech Disorder (ASD) in medical terminology. The aim of the article is to define specific language impairment in children and the problems it can cause. The different methods adopted by speech pathologists to diagnose language impairment. Finally implementing machine learning models to automate the process and help speech pathologists and pediatricians/ in diagnosing the specific language impairment.

• International Journal of Computer Science & Network Security
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• v.23 no.3
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• pp.144-150
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• 2023

With progressive advancements in Man-made consciousness (computer based intelligence) and Profound Learning (DL), contributing altogether to Normal Language Handling (NLP), the precision and nature of Machine Interpretation (MT) has worked on complex. There is a discussion, but that its no time like the present the human interpretation became immaterial or excess. All things considered, human flaws are consistently dealt with by its own creations. With the utilization of brain networks in machine interpretation, its been as of late guaranteed that keen frameworks can now decipher at standard with human interpreters. In any case, simulated intelligence is as yet not without any trace of issues related with handling of a language, let be the intricacies and complexities common of interpretation. Then, at that point, comes the innate predispositions while planning smart frameworks. How we plan these frameworks relies upon what our identity is, subsequently setting in a one-sided perspective and social encounters. Given the variety of language designs and societies they address, their taking care of by keen machines, even with profound learning abilities, with human proficiency looks exceptionally far-fetched, at any rate, for the time being.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.89-94
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• 2024

The main purpose of the study is to determine the main elements of the use of digital technologies for learning a foreign language in educational institutions. The era of digital technologies is a transition from the traditional format of working with information to a digital format. This is the era of the total domination of digital technologies. Digital technologies have gained an unprecedented rapid and general distribution. In recent years, all spheres of human life have already undergone the intervention of digital technologies. Therefore, it is precisely the educational industry that faces a difficult task - to move to a new level of education, where digital technologies will be actively used, allowing you to conveniently and quickly work in the information field for more effective learning and development. The study has limitations and they relate to the fact that the practical activities of the process of using digital technologies in the system of preparing the study of a foreign language were not taken into account.