• Title/Summary/Keyword: Word-embedding

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A Study on the Improvement Model of Document Retrieval Efficiency of Tax Judgment (조세심판 문서 검색 효율 향상 모델에 관한 연구)

  • Lee, Hoo-Young;Park, Koo-Rack;Kim, Dong-Hyun
    • Journal of the Korea Convergence Society
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    • v.10 no.6
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    • pp.41-47
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    • 2019
  • It is very important to search for and obtain an example of a similar judgment in case of court judgment. The existing judge's document search uses a method of searching through key-words entered by the user. However, if it is necessary to input an accurate keyword and the keyword is unknown, it is impossible to search for the necessary document. In addition, the detected document may have different contents. In this paper, we want to improve the effectiveness of the method of vectorizing a document into a three-dimensional space, calculating cosine similarity, and searching close documents in order to search an accurate judge's example. Therefore, after analyzing the similarity of words used in the judge's example, a method is provided for extracting the mode and inserting it into the text of the text, thereby providing a method for improving the cosine similarity of the document to be retrieved. It is hoped that users will be able to provide a fast, accurate search trying to find an example of a tax-related judge through the proposed model.

A Philological Study of Previous Research on Open-Close-Pivot(開闔樞) (개합추(開闔樞)에 대한 문헌학적 고찰)

  • Choi, Dong-Hyun;Baik, You-sang;Jeong, Chang-hyun;Jang, Woo-chang
    • Journal of Korean Medical classics
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    • v.32 no.2
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    • pp.33-47
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    • 2019
  • Objectives : To review the meaning of Open-Close-Pivot and its transition over time. Methods : Annotations and explanations in chapters where the theory of Open-Close-Pivot originated from in the "Suwen(素問)" and "Lingshu(靈樞)" were examined, followed by philological examination of key physicians. Results & Conclusions : Yang Shang Shan(楊上善) compared the 'Open-Close-Pivot' to a door. Wang Bing(王冰) explained it's movement and stillness to be the principle behind the Three-Yin-Three-Yang's threefold division. Wang Ji(汪機), in "XuSuwenChao(續素問鈔)" explained its physiological function as the entering and exiting of Ying Wei(營衛), and its scope to be exterior, interior, and center. Ma Shi(馬蒔) stratified it similar to "Shanghanlun(傷寒論)"'s six stages, while Wu Kun(吳崑) categorized Shaoyang(少陽) as being in-between exterior and interior. Zhang Jing Yue(張景岳) suggested a standard for the entering-exiting and exterior-interior-center concepts of the Open-Close-Pivot. Zhang Zhi Cong(張志聰) argued a theory of Open-Close-Pivot that emphasized the meaning of Three-Yin-Three-Yang, while Gao Shi Zong(高士宗) explained the relationship between Open, Close, Pivot and the functioning process. Shi Shou Tang(石壽棠) explained the physiology and pathology in the actions among Open-Close-Pivot with a focus on Pivot. Ke Qin(柯琴) applied Open-Close-Pivot to "Shanghanlun(傷寒論)" and used it as the basic principle to the Six Channel Ground Theory(六經地面說), also clinically applying it in the identification and treatment of the Six Channel patterns. Conclusions : Counting based word embedding methods seems to be more effective than.

A BERT-Based Deep Learning Approach for Vulnerability Detection (BERT를 이용한 딥러닝 기반 소스코드 취약점 탐지 방법 연구)

  • Jin, Wenhui;Oh, Heekuck
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.32 no.6
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    • pp.1139-1150
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    • 2022
  • With the rapid development of SW Industry, softwares are everywhere in our daily life. The number of vulnerabilities are also increasing with a large amount of newly developed code. Vulnerabilities can be exploited by hackers, resulting the disclosure of privacy and threats to the safety of property and life. In particular, since the large numbers of increasing code, manually analyzed by expert is not enough anymore. Machine learning has shown high performance in object identification or classification task. Vulnerability detection is also suitable for machine learning, as a reuslt, many studies tried to use RNN-based model to detect vulnerability. However, the RNN model is also has limitation that as the code is longer, the earlier can not be learned well. In this paper, we proposed a novel method which applied BERT to detect vulnerability. The accuracy was 97.5%, which increased by 1.5%, and the efficiency also increased by 69% than Vuldeepecker.

Spam Image Detection Model based on Deep Learning for Improving Spam Filter

  • Seong-Guk Nam;Dong-Gun Lee;Yeong-Seok Seo
    • Journal of Information Processing Systems
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    • v.19 no.3
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    • pp.289-301
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    • 2023
  • Due to the development and dissemination of modern technology, anyone can easily communicate using services such as social network service (SNS) through a personal computer (PC) or smartphone. The development of these technologies has caused many beneficial effects. At the same time, bad effects also occurred, one of which was the spam problem. Spam refers to unwanted or rejected information received by unspecified users. The continuous exposure of such information to service users creates inconvenience in the user's use of the service, and if filtering is not performed correctly, the quality of service deteriorates. Recently, spammers are creating more malicious spam by distorting the image of spam text so that optical character recognition (OCR)-based spam filters cannot easily detect it. Fortunately, the level of transformation of image spam circulated on social media is not serious yet. However, in the mail system, spammers (the person who sends spam) showed various modifications to the spam image for neutralizing OCR, and therefore, the same situation can happen with spam images on social media. Spammers have been shown to interfere with OCR reading through geometric transformations such as image distortion, noise addition, and blurring. Various techniques have been studied to filter image spam, but at the same time, methods of interfering with image spam identification using obfuscated images are also continuously developing. In this paper, we propose a deep learning-based spam image detection model to improve the existing OCR-based spam image detection performance and compensate for vulnerabilities. The proposed model extracts text features and image features from the image using four sub-models. First, the OCR-based text model extracts the text-related features, whether the image contains spam words, and the word embedding vector from the input image. Then, the convolution neural network-based image model extracts image obfuscation and image feature vectors from the input image. The extracted feature is determined whether it is a spam image by the final spam image classifier. As a result of evaluating the F1-score of the proposed model, the performance was about 14 points higher than the OCR-based spam image detection performance.

Approximate Top-k Labeled Subgraph Matching Scheme Based on Word Embedding (워드 임베딩 기반 근사 Top-k 레이블 서브그래프 매칭 기법)

  • Choi, Do-Jin;Oh, Young-Ho;Bok, Kyoung-Soo;Yoo, Jae-Soo
    • The Journal of the Korea Contents Association
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    • v.22 no.8
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    • pp.33-43
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    • 2022
  • Labeled graphs are used to represent entities, their relationships, and their structures in real data such as knowledge graphs and protein interactions. With the rapid development of IT and the explosive increase in data, there has been a need for a subgraph matching technology to provide information that the user is interested in. In this paper, we propose an approximate Top-k labeled subgraph matching scheme that considers the semantic similarity of labels and the difference in graph structure. The proposed scheme utilizes a learning model using FastText in order to consider the semantic similarity of a label. In addition, the label similarity graph(LSG) is used for approximate subgraph matching by calculating similarity values between labels in advance. Through the LSG, we can resolve the limitations of the existing schemes that subgraph expansion is possible only if the labels match exactly. It supports structural similarity for a query graph by performing searches up to 2-hop. Based on the similarity value, we provide k subgraph matching results. We conduct various performance evaluations in order to show the superiority of the proposed scheme.

Coating defect classification method for steel structures with vision-thermography imaging and zero-shot learning

  • Jun Lee;Kiyoung Kim;Hyeonjin Kim;Hoon Sohn
    • Smart Structures and Systems
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    • v.33 no.1
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    • pp.55-64
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    • 2024
  • This paper proposes a fusion imaging-based coating-defect classification method for steel structures that uses zero-shot learning. In the proposed method, a halogen lamp generates heat energy on the coating surface of a steel structure, and the resulting heat responses are measured by an infrared (IR) camera, while photos of the coating surface are captured by a charge-coupled device (CCD) camera. The measured heat responses and visual images are then analyzed using zero-shot learning to classify the coating defects, and the estimated coating defects are visualized throughout the inspection surface of the steel structure. In contrast to older approaches to coating-defect classification that relied on visual inspection and were limited to surface defects, and older artificial neural network (ANN)-based methods that required large amounts of data for training and validation, the proposed method accurately classifies both internal and external defects and can classify coating defects for unobserved classes that are not included in the training. Additionally, the proposed model easily learns about additional classifying conditions, making it simple to add classes for problems of interest and field application. Based on the results of validation via field testing, the defect-type classification performance is improved 22.7% of accuracy by fusing visual and thermal imaging compared to using only a visual dataset. Furthermore, the classification accuracy of the proposed method on a test dataset with only trained classes is validated to be 100%. With word-embedding vectors for the labels of untrained classes, the classification accuracy of the proposed method is 86.4%.

Subject-Balanced Intelligent Text Summarization Scheme (주제 균형 지능형 텍스트 요약 기법)

  • Yun, Yeoil;Ko, Eunjung;Kim, Namgyu
    • Journal of Intelligence and Information Systems
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    • v.25 no.2
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    • pp.141-166
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    • 2019
  • Recently, channels like social media and SNS create enormous amount of data. In all kinds of data, portions of unstructured data which represented as text data has increased geometrically. But there are some difficulties to check all text data, so it is important to access those data rapidly and grasp key points of text. Due to needs of efficient understanding, many studies about text summarization for handling and using tremendous amounts of text data have been proposed. Especially, a lot of summarization methods using machine learning and artificial intelligence algorithms have been proposed lately to generate summary objectively and effectively which called "automatic summarization". However almost text summarization methods proposed up to date construct summary focused on frequency of contents in original documents. Those summaries have a limitation for contain small-weight subjects that mentioned less in original text. If summaries include contents with only major subject, bias occurs and it causes loss of information so that it is hard to ascertain every subject documents have. To avoid those bias, it is possible to summarize in point of balance between topics document have so all subject in document can be ascertained, but still unbalance of distribution between those subjects remains. To retain balance of subjects in summary, it is necessary to consider proportion of every subject documents originally have and also allocate the portion of subjects equally so that even sentences of minor subjects can be included in summary sufficiently. In this study, we propose "subject-balanced" text summarization method that procure balance between all subjects and minimize omission of low-frequency subjects. For subject-balanced summary, we use two concept of summary evaluation metrics "completeness" and "succinctness". Completeness is the feature that summary should include contents of original documents fully and succinctness means summary has minimum duplication with contents in itself. Proposed method has 3-phases for summarization. First phase is constructing subject term dictionaries. Topic modeling is used for calculating topic-term weight which indicates degrees that each terms are related to each topic. From derived weight, it is possible to figure out highly related terms for every topic and subjects of documents can be found from various topic composed similar meaning terms. And then, few terms are selected which represent subject well. In this method, it is called "seed terms". However, those terms are too small to explain each subject enough, so sufficient similar terms with seed terms are needed for well-constructed subject dictionary. Word2Vec is used for word expansion, finds similar terms with seed terms. Word vectors are created after Word2Vec modeling, and from those vectors, similarity between all terms can be derived by using cosine-similarity. Higher cosine similarity between two terms calculated, higher relationship between two terms defined. So terms that have high similarity values with seed terms for each subjects are selected and filtering those expanded terms subject dictionary is finally constructed. Next phase is allocating subjects to every sentences which original documents have. To grasp contents of all sentences first, frequency analysis is conducted with specific terms that subject dictionaries compose. TF-IDF weight of each subjects are calculated after frequency analysis, and it is possible to figure out how much sentences are explaining about each subjects. However, TF-IDF weight has limitation that the weight can be increased infinitely, so by normalizing TF-IDF weights for every subject sentences have, all values are changed to 0 to 1 values. Then allocating subject for every sentences with maximum TF-IDF weight between all subjects, sentence group are constructed for each subjects finally. Last phase is summary generation parts. Sen2Vec is used to figure out similarity between subject-sentences, and similarity matrix can be formed. By repetitive sentences selecting, it is possible to generate summary that include contents of original documents fully and minimize duplication in summary itself. For evaluation of proposed method, 50,000 reviews of TripAdvisor are used for constructing subject dictionaries and 23,087 reviews are used for generating summary. Also comparison between proposed method summary and frequency-based summary is performed and as a result, it is verified that summary from proposed method can retain balance of all subject more which documents originally have.

A Hybrid Collaborative Filtering-based Product Recommender System using Search Keywords (검색 키워드를 활용한 하이브리드 협업필터링 기반 상품 추천 시스템)

  • Lee, Yunju;Won, Haram;Shim, Jaeseung;Ahn, Hyunchul
    • Journal of Intelligence and Information Systems
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    • v.26 no.1
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    • pp.151-166
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    • 2020
  • A recommender system is a system that recommends products or services that best meet the preferences of each customer using statistical or machine learning techniques. Collaborative filtering (CF) is the most commonly used algorithm for implementing recommender systems. However, in most cases, it only uses purchase history or customer ratings, even though customers provide numerous other data that are available. E-commerce customers frequently use a search function to find the products in which they are interested among the vast array of products offered. Such search keyword data may be a very useful information source for modeling customer preferences. However, it is rarely used as a source of information for recommendation systems. In this paper, we propose a novel hybrid CF model based on the Doc2Vec algorithm using search keywords and purchase history data of online shopping mall customers. To validate the applicability of the proposed model, we empirically tested its performance using real-world online shopping mall data from Korea. As the number of recommended products increases, the recommendation performance of the proposed CF (or, hybrid CF based on the customer's search keywords) is improved. On the other hand, the performance of a conventional CF gradually decreased as the number of recommended products increased. As a result, we found that using search keyword data effectively represents customer preferences and might contribute to an improvement in conventional CF recommender systems.

Development and Validation of the Letter-unit based Korean Sentimental Analysis Model Using Convolution Neural Network (회선 신경망을 활용한 자모 단위 한국형 감성 분석 모델 개발 및 검증)

  • Sung, Wonkyung;An, Jaeyoung;Lee, Choong C.
    • The Journal of Society for e-Business Studies
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    • v.25 no.1
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    • pp.13-33
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    • 2020
  • This study proposes a Korean sentimental analysis algorithm that utilizes a letter-unit embedding and convolutional neural networks. Sentimental analysis is a natural language processing technique for subjective data analysis, such as a person's attitude, opinion, and propensity, as shown in the text. Recently, Korean sentimental analysis research has been steadily increased. However, it has failed to use a general-purpose sentimental dictionary and has built-up and used its own sentimental dictionary in each field. The problem with this phenomenon is that it does not conform to the characteristics of Korean. In this study, we have developed a model for analyzing emotions by producing syllable vectors based on the onset, peak, and coda, excluding morphology analysis during the emotional analysis procedure. As a result, we were able to minimize the problem of word learning and the problem of unregistered words, and the accuracy of the model was 88%. The model is less influenced by the unstructured nature of the input data and allows for polarized classification according to the context of the text. We hope that through this developed model will be easier for non-experts who wish to perform Korean sentimental analysis.

Design of a Deep Neural Network Model for Image Caption Generation (이미지 캡션 생성을 위한 심층 신경망 모델의 설계)

  • Kim, Dongha;Kim, Incheol
    • KIPS Transactions on Software and Data Engineering
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    • v.6 no.4
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    • pp.203-210
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    • 2017
  • In this paper, we propose an effective neural network model for image caption generation and model transfer. This model is a kind of multi-modal recurrent neural network models. It consists of five distinct layers: a convolution neural network layer for extracting visual information from images, an embedding layer for converting each word into a low dimensional feature, a recurrent neural network layer for learning caption sentence structure, and a multi-modal layer for combining visual and language information. In this model, the recurrent neural network layer is constructed by LSTM units, which are well known to be effective for learning and transferring sequence patterns. Moreover, this model has a unique structure in which the output of the convolution neural network layer is linked not only to the input of the initial state of the recurrent neural network layer but also to the input of the multimodal layer, in order to make use of visual information extracted from the image at each recurrent step for generating the corresponding textual caption. Through various comparative experiments using open data sets such as Flickr8k, Flickr30k, and MSCOCO, we demonstrated the proposed multimodal recurrent neural network model has high performance in terms of caption accuracy and model transfer effect.