• Title/Summary/Keyword: Language Model Network

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FAGON: Fake News Detection Model Using Grammatical Transformation on Deep Neural Network

  • Seo, Youngkyung;Han, Seong-Soo;Jeon, You-Boo;Jeong, Chang-Sung
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.13 no.10
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    • pp.4958-4970
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    • 2019
  • As technology advances, the amount of fake news is increasing more and more by various reasons such as political issues and advertisement exaggeration. However, there have been very few research works on fake news detection, especially which uses grammatical transformation on deep neural network. In this paper, we shall present a new Fake News Detection Model, called FAGON(Fake news detection model using Grammatical transformation On deep Neural network) which determines efficiently if the proposition is true or not for the given article by learning grammatical transformation on neural network. Especially, our model focuses the Korean language. It consists of two modules: sentence generator and classification. The former generates multiple sentences which have the same meaning as the proposition, but with different grammar by training the grammatical transformation. The latter classifies the proposition as true or false by training with vectors generated from each sentence of the article and the multiple sentences obtained from the former model respectively. We shall show that our model is designed to detect fake news effectively by exploiting various grammatical transformation and proper classification structure.

DESIGN AND IMPLEMENTATION OF METADATA MODEL FOR SENSOR DATA STREAM

  • Lee, Yang-Koo;Jung, Young-Jin;Ryu, Keun-Ho;Kim, Kwang-Deuk
    • Proceedings of the KSRS Conference
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    • v.2
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    • pp.768-771
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    • 2006
  • In WSN(Wireless Sensor Network) environment, a large amount of sensors, which are small and heterogeneous, generates data stream successively in physical space. These sensors are composed of measured data and metadata. Metadata includes various features such as location, sampling time, measurement unit, and their types. Until now, wireless sensors have been managed with individual specification, not the explicit standardization of metadata, so it is difficult to collect and communicate between heterogeneous sensors. To solve this problem, OGC(Open Geospatial Consortium) has proposed a SensorML(Sensor Model Language) which can manage metadata of heterogeneous sensors with unique format. In this paper, we introduce a metadata model using SensorML specification to manage various sensors, which are distributed in a wide scope. In addition, we implement the metadata management module applied to the sensor data stream management system. We provide many functions, namely generating metadata file, registering and storing them according to definition of SensorML.

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Design of Query Processing System to Retrieve Information from Social Network using NLP

  • Virmani, Charu;Juneja, Dimple;Pillai, Anuradha
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.3
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    • pp.1168-1188
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    • 2018
  • Social Network Aggregators are used to maintain and manage manifold accounts over multiple online social networks. Displaying the Activity feed for each social network on a common dashboard has been the status quo of social aggregators for long, however retrieving the desired data from various social networks is a major concern. A user inputs the query desiring the specific outcome from the social networks. Since the intention of the query is solely known by user, therefore the output of the query may not be as per user's expectation unless the system considers 'user-centric' factors. Moreover, the quality of solution depends on these user-centric factors, the user inclination and the nature of the network as well. Thus, there is a need for a system that understands the user's intent serving structured objects. Further, choosing the best execution and optimal ranking functions is also a high priority concern. The current work finds motivation from the above requirements and thus proposes the design of a query processing system to retrieve information from social network that extracts user's intent from various social networks. For further improvements in the research the machine learning techniques are incorporated such as Latent Dirichlet Algorithm (LDA) and Ranking Algorithm to improve the query results and fetch the information using data mining techniques.The proposed framework uniquely contributes a user-centric query retrieval model based on natural language and it is worth mentioning that the proposed framework is efficient when compared on temporal metrics. The proposed Query Processing System to Retrieve Information from Social Network (QPSSN) will increase the discoverability of the user, helps the businesses to collaboratively execute promotions, determine new networks and people. It is an innovative approach to investigate the new aspects of social network. The proposed model offers a significant breakthrough scoring up to precision and recall respectively.

CR-M-SpanBERT: Multiple embedding-based DNN coreference resolution using self-attention SpanBERT

  • Joon-young Jung
    • ETRI Journal
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    • v.46 no.1
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    • pp.35-47
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    • 2024
  • This study introduces CR-M-SpanBERT, a coreference resolution (CR) model that utilizes multiple embedding-based span bidirectional encoder representations from transformers, for antecedent recognition in natural language (NL) text. Information extraction studies aimed to extract knowledge from NL text autonomously and cost-effectively. However, the extracted information may not represent knowledge accurately owing to the presence of ambiguous entities. Therefore, we propose a CR model that identifies mentions referring to the same entity in NL text. In the case of CR, it is necessary to understand both the syntax and semantics of the NL text simultaneously. Therefore, multiple embeddings are generated for CR, which can include syntactic and semantic information for each word. We evaluate the effectiveness of CR-M-SpanBERT by comparing it to a model that uses SpanBERT as the language model in CR studies. The results demonstrate that our proposed deep neural network model achieves high-recognition accuracy for extracting antecedents from NL text. Additionally, it requires fewer epochs to achieve an average F1 accuracy greater than 75% compared with the conventional SpanBERT approach.

A Natural Language Information Retrieval Model using Automatic Network and Two-level Document Ranking (자동 키워드망과 2단계 문서 순위 결정에 의한 자연어 정보검색 모델)

  • Kang, Hyun-Kyu;Park, Se-Young;Choi, Key-Sun
    • Annual Conference on Human and Language Technology
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    • 1995.10a
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    • pp.8-12
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    • 1995
  • 본 논문은 정보검색에서 사용자에게 순서화된 문서를 제시하기 이전에 1차로 검색된 문서들에 대하여 자동 키워드망과 2단계로 문서 순위 결정하는 모델에 대하여 논하였다. 자연어 검색을 위한 색인은 자동으로 구축된 키워드 색인으로 1차로 자연어 검색을 하고, 2차로 자동 키워드망을 이용한 순위재조정을 통해 검색효율의 향상에 관해 검색 효율을 평가하여 1차 검색 결과보다 최대 10.9%의 검색효율 향상을 보였다. 또한 문서 순위 조정 방법에 있어서 여러 가지 공식을 비교 분석하였으며 내용 검색을 반영하는 공식을 찾았다. 본 논문에서 제시한 2단계 순위 결정 방법은 리스트를 기반으로 하는 정보 검색의 분야에 적용되어 검색효율을 높일 수 있는 한가지 방법이 될 수 있을 것이다.

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Formal Modeling and Verification of an Enhanced Variant of the IEEE 802.11 CSMA/CA Protocol

  • Hammal, Youcef;Ben-Othman, Jalel;Mokdad, Lynda;Abdelli, Abdelkrim
    • Journal of Communications and Networks
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    • v.16 no.4
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    • pp.385-396
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    • 2014
  • In this paper, we present a formal method for modeling and checking an enhanced version of the carrier sense multiple access with collision avoidance protocol related to the IEEE 802.11 MAC layer, which has been proposed as the standard protocol for wireless local area networks. We deal mainly with the distributed coordination function (DCF) procedure of this protocol throughout a sequence of transformation steps. First, we use the unified modeling language state machines to thoroughly capture the behavior of wireless stations implementing a DCF, and then translate them into the input language of the UPPAAL model checking tool, which is a network of communicating timed automata. Finally, we proceed by checking of some of the safety and liveness properties, such as deadlock-freedom, using this tool.

AMR-CNN: Abstract Meaning Representation with Convolution Neural Network for Toxic Content Detection

  • Ermal Elbasani;Jeong-Dong Kim
    • Journal of Web Engineering
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    • v.21 no.3
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    • pp.677-692
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    • 2022
  • Recognizing the offensive, abusive, and profanity of multimedia content on the web has been a challenge to keep the web environment for user's freedom of speech. As profanity filtering function has been developed and applied in text, audio, and video context in platforms such as social media, entertainment, and education, the number of methods to trick the web-based application also has been increased and became a new issue to be solved. Compared to commonly developed toxic content detection systems that use lexicon and keyword-based detection, this work tries to embrace a different approach by the meaning of the sentence. Meaning representation is a way to grasp the meaning of linguistic input. This work proposed a data-driven approach utilizing Abstract meaning Representation to extract the meaning of the online text content into a convolutional neural network to detect level profanity. This work implements the proposed model in two kinds of datasets from the Offensive Language Identification Dataset and other datasets from the Offensive Hate dataset merged with the Twitter Sentiment Analysis dataset. The results indicate that the proposed model performs effectively, and can achieve a satisfactory accuracy in recognizing the level of online text content toxicity.

Korean Text to Gloss: Self-Supervised Learning approach

  • Thanh-Vu Dang;Gwang-hyun Yu;Ji-yong Kim;Young-hwan Park;Chil-woo Lee;Jin-Young Kim
    • Smart Media Journal
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    • v.12 no.1
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    • pp.32-46
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    • 2023
  • Natural Language Processing (NLP) has grown tremendously in recent years. Typically, bilingual, and multilingual translation models have been deployed widely in machine translation and gained vast attention from the research community. On the contrary, few studies have focused on translating between spoken and sign languages, especially non-English languages. Prior works on Sign Language Translation (SLT) have shown that a mid-level sign gloss representation enhances translation performance. Therefore, this study presents a new large-scale Korean sign language dataset, the Museum-Commentary Korean Sign Gloss (MCKSG) dataset, including 3828 pairs of Korean sentences and their corresponding sign glosses used in Museum-Commentary contexts. In addition, we propose a translation framework based on self-supervised learning, where the pretext task is a text-to-text from a Korean sentence to its back-translation versions, then the pre-trained network will be fine-tuned on the MCKSG dataset. Using self-supervised learning help to overcome the drawback of a shortage of sign language data. Through experimental results, our proposed model outperforms a baseline BERT model by 6.22%.

The Evolutionary Trends and Influential Factors Analysis of Agricultural Trade between South Korea and RCEP Member Countries

  • Qianli Wu;Jinyan Tian;Haiyan Yu;Ziyang Liu
    • Journal of Internet Computing and Services
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    • v.25 no.4
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    • pp.73-86
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    • 2024
  • With the acceleration of regional economic integration, the agricultural trade network within the RCEP region presents new opportunities and challenges for member countries. This study focuses on agricultural trade among RCEP members from 2011 to 2020, utilizing social network analysis to explore the structural characteristics and evolutionary trends of the trade network. Additionally, an extended gravity model is employed to empirically analyze the key factors influencing South Korea's agricultural trade with other member countries. The findings reveal that: (1) Agricultural trade relationships within the RCEP region are stable and mature, with high interconnectivity in the trade network, indicating a trend towards balanced development. (2) The positions of member countries within the agricultural trade network are characterized by both high density and heterogeneity. (3) South Korea's agricultural trade with RCEP member countries is positively influenced by the economic size, population size, and governance level of its trading partners, while South Korea's own indicators show no significant effect. The trade distance between South Korea and member countries also has a positive impact on agricultural trade. By combining social network analysis with an extended gravity model, this study provides a multi-faceted quantitative analysis of the RCEP agricultural trade network, offering new insights into regional agricultural trade. It also provides empirical evidence for agricultural trade cooperation between South Korea and other RCEP countries.

Machine Learning Based Domain Classification for Korean Dialog System (기계학습을 이용한 한국어 대화시스템 도메인 분류)

  • Jeong, Young-Seob
    • Journal of Convergence for Information Technology
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    • v.9 no.8
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    • pp.1-8
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    • 2019
  • Dialog system is becoming a new dominant interaction way between human and computer. It allows people to be provided with various services through natural language. The dialog system has a common structure of a pipeline consisting of several modules (e.g., speech recognition, natural language understanding, and dialog management). In this paper, we tackle a task of domain classification for the natural language understanding module by employing machine learning models such as convolutional neural network and random forest. For our dataset of seven service domains, we showed that the random forest model achieved the best performance (F1 score 0.97). As a future work, we will keep finding a better approach for domain classification by investigating other machine learning models.