• 문화기술의 융합
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• 제6권4호
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• pp.225-230
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• 2020

인공지능, 딥러닝, 머신러닝 등이 괄목할만한 발전을 이루면서 2016년경부터 100여개의 언어를 비롯하여 가장 보편적으로 사용되어 온 Google Translate (구글기계번역기)는 자연언어처리(NLP) 분야와 외국어 학습 등 언어활용 분야에 독보적인 역할을 하고 있다. 본 논문은 구글기계번역기, Google Translate에 있어서, 영어모국어화자가 가진 통사문법적-범주적 지식의 중요성과 그 영향력에 대해 살펴보고자 한다. Jackendoff (1999)는 맹인을 위한 독서기계(Reading Machine)등을 구축하려면 통사구조적 지식과 문법적 분해력(parsing)이 매우 중요하고, 적어도 현재의 컴퓨터는 엄청난 발전을 이루기는 하였으나, 인간의 두뇌를 따라갈 수 없다는 결론을 내렸다. Jackendoff가 논의했던 몇 가지 어휘항목과 통사구조적 중의성을 활용하여, Google Translate 기계발음번역기를 통해 그의 주장을 확인하는 실험을 실시하고, 그 결과를 분석하는 것이 이 논문의 목표이다. 이 연구는 Jackendoff의 주장처럼 L1 화자가 내재화한 통사문법적, 범주-구조적 지식은 NLP, 혹은 "독서기계"등의 구축에서 중요하며, 이는 Chomsky (1986, 2005)등에서 논의된 내재적언어 (I-language)의 핵심이라는 점을 시사한다.

• The Journal of Asian Finance, Economics and Business
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• 제9권5호
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• pp.273-283
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• 2022

Banking and finance is a broad term that incorporates a variety of smaller, more specialized subjects such as corporate finance, tax finance, and insurance finance. A virtual assistant that assists users in searching for information about banking and finance terms might be an extremely beneficial tool for users. In this study, we explored the process of searching for information, seeking opportunities, and developing a virtual assistant in the first stages of starting learning and understanding Vietnamese to increase effectiveness and save time, which is also an innovative business practice in Use-case Vietnam. We built the FIBA2020 dataset and proposed a pipeline that used Natural Language Processing (NLP) inclusive of Natural Language Understanding (NLU) algorithms to build chatbot applications. The open-source framework RASA is used to implement the system in our study. We aim to improve our model performance by replacing parts of RASA's default tokenizers with Vietnamese tokenizers and experimenting with various language models. The best accuracy we achieved is 86.48% and 70.04% in the ideal condition and worst condition, respectively. Finally, we put our findings into practice by creating an Android virtual assistant application using the model trained using Whitespace tokenizer and the pre-trained language m-BERT.

• ETRI Journal
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• 제22권2호
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• pp.20-31
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• 2000

Much research on natural language processing (NLP), computational linguistics and lexicography has relied and depended on linguistic corpora. In recent years, many organizations around the world have been constructing their own large corporal to achieve corpus representativeness and/or linguistic comprehensiveness. However, there is no reliable guideline as to how large machine readable corpus resources should be compiled to develop practical NLP software and/or complete dictionaries for humans and computational use. In order to shed some new light on this issue, we shall reveal the flaws of several previous researches aiming to predict corpus size, especially those using pure regression or curve-fitting methods. To overcome these flaws, we shall contrive a new mathematical tool: a piecewise curve-fitting algorithm, and next, suggest how to determine the tolerance error of the algorithm for good prediction, using a specific corpus. Finally, we shall illustrate experimentally that the algorithm presented is valid, accurate and very reliable. We are confident that this study can contribute to solving some inherent problems of corpus linguistics, such as corpus predictability, compiling methodology, corpus representativeness and linguistic comprehensiveness.

• Genomics & Informatics
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• 제17권2호
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• pp.19.1-19.10
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• 2019

In this paper, we investigate cross-platform interoperability for natural language processing (NLP) and, in particular, annotation of textual resources, with an eye toward identifying the design elements of annotation models and processes that are particularly problematic for, or amenable to, enabling seamless communication across different platforms. The study is conducted in the context of a specific annotation methodology, namely machine-assisted interactive annotation (also known as human-in-the-loop annotation). This methodology requires the ability to freely combine resources from different document repositories, access a wide array of NLP tools that automatically annotate corpora for various linguistic phenomena, and use a sophisticated annotation editor that enables interactive manual annotation coupled with on-the-fly machine learning. We consider three independently developed platforms, each of which utilizes a different model for representing annotations over text, and each of which performs a different role in the process.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.986-1005
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• 2022

Text steganography is one of the most imminent and promising research interests in the information security field. With the unprecedented success of the neural network and natural language processing (NLP), the last years have seen a surge of research on generative linguistic steganography (GLS). This paper provides a thorough and comprehensive review to summarize the existing key contributions, and creates a novel taxonomy for GLS according to NLP techniques and steganographic encoding algorithm, then summarizes the characteristics of generative linguistic steganographic methods properly to analyze the relationship and difference between each type of them. Meanwhile, this paper also comprehensively introduces and analyzes several evaluation metrics to evaluate the performance of GLS from diverse perspective. Finally, this paper concludes the future research work, which is more conducive to the follow-up research and innovation of researchers.

• 한국정보시스템학회지:정보시스템연구
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• 제28권3호
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• pp.105-121
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• 2019

Purpose One of the reasons for the expansion of information systems in the enterprise is the increased efficiency of data analysis. In particular, the rapidly increasing data types which are complex and unstructured such as video, voice, images, and conversations in and out of social networks. The purpose of this study is the customer needs analysis from customer voices, ie, text data, in the web environment.. Design/methodology/approach As previous study results, the word frequency of the sentence is extracted as a word that interprets the sentence has better affects than frequency analysis. In this study, we applied the TF-IDF method, which extracts important keywords in real sentences, not the TF method, which is a word extraction technique that expresses sentences with simple frequency only, in Korean language research. We visualized the two techniques by cluster analysis and describe the difference. Findings TF technique and TF-IDF technique are applied for Korean natural language processing, the research showed the value from frequency analysis technique to semantic analysis and it is expected to change the technique by Korean language processing researcher.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.352-358
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• 2022

Text mining is an important process used for analyzing the data collected from different sources like videos, audio, social media, and so on. The tools like Natural Language Processing (NLP) are mostly used in real-time applications. In the earlier research, text mining approaches were implemented using long-short memory (LSTM) networks. In this paper, text mining is performed using average-stochastic gradient descent weight-dropped (AWD)-LSTM techniques to obtain better accuracy and performance. The proposed model is effectively demonstrated by considering the internet movie database (IMDB) reviews. To implement the proposed model Python language was used due to easy adaptability and flexibility while dealing with massive data sets/databases. From the results, it is seen that the proposed LSTM plus weight dropped plus embedding model demonstrated an accuracy of 88.36% as compared to the previous models of AWD LSTM as 85.64. This result proved to be far better when compared with the results obtained by just LSTM model (with 85.16%) accuracy. Finally, the loss function proved to decrease from 0.341 to 0.299 using the proposed model

• 문화기술의 융합
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• 제10권2호
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• pp.453-458
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• 2024

광학 문자 인식(Optical Character Recognition, OCR)은 이미지 내의 문자를 인식하여 디지털 포맷(Digital Format)의 텍스트로 변환하는 기술이다. 딥러닝(Deep Learning) 기반의 OCR이 높은 인식률을 보여줌에 따라 대량의 기록 자료를 보유한 많은 산업 분야에서 OCR을 활용하고 있다. 특히, 의료 산업 분야는 의료 서비스 향상을 위해 딥러닝 기반의 OCR을 적극 도입하였다. 본 논문에서는 딥러닝 기반 OCR 엔진(Engine) 및 의료 데이터에 특화된 OCR의 동향을 살펴보고, 의료 OCR의 발전 방향에 대해 제시한다. 현재의 의료 OCR은 검출한 문자 데이터를 자연어 처리(Natural Language Processing, NLP)하여 인식률을 개선하였다. 그러나, 정형화되지 않은 손글씨(Handwriting)나 변형된 문자에서는 여전히 인식 정확도에 한계를 보였다. 의료 데이터의 데이터베이스(Database)화, 이미지 전처리(Pre-processing), 특화된 자연어 처리를 통해 더욱 고도화된 의료 OCR을 발전시키는 것이 필요하다.

• 한국기술혁신학회:학술대회논문집
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• 한국기술혁신학회 2017년도 추계학술대회 논문집
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• pp.129-129
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• 2017

In planning and evaluating government R&D programs, one of the first steps is to understand the government's current R&D investment portfolio - which fields or topics the government is now investing in in R&D. Analysis methods of an investment portfolio of government R&D tend traditionally to rely on keyword searches or ad-hoc two-dimensional classifications. The main drawback of these approaches is their limited ability to account for the characteristics of the whole government investment in R&D and the role of individual R&D program in it, which tends to depend on the relationship with other programs. This paper suggests a new method for mapping and analyzing government investment in R&D using a combination of methods from natural language processing (NLP) and network analysis. The NLP enables us to build a network of government R&D programs whose links are defined as similarity in R&D topics. Then methods from network analysis show the characteristics of government investment in R&D, including major investment fields, unexplored topics, and key R&D programs which play a role like a hub or a bridge in the network of R&D programs, which are difficult to be identified by conventional methods. These insights can be utilized in planning a new R&D program, in reviewing its proposal, or in evaluating the performance of R&D programs. The utilized (filtered) Korean text corpus consists of hundreds of R&D program descriptions in the budget requests for fiscal year 2017 submitted by government departments to the Korean Ministry of Strategy and Finance.

• 한국정보과학회 언어공학연구회:학술대회논문집(한글 및 한국어 정보처리)
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• 한국정보과학회언어공학연구회 2012년도 제24회 한글 및 한국어 정보처리 학술대회
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• pp.16-20
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• 2012

본 논문에서는 한국어 자연언어처리를 위해 형태소분석기와 구구조 구문분석기와 의존구조 구문분석기를 통합한 하나의 플랫폼을 제공하고, 외국의 다양한 자연언어처리 도구들의 결과물과의 국제적 상호운용성 및 Linked Data를 위한 RDF 형태로의 변환 시스템을 제시한다.