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Lexical Characteristics Analysis of Chinese Clinical Documents

Lexical Characteristics Analysis of Chinese Clinical Documen...

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IEEE international Symposium on information (IT) in Medicine and Education, ITME

作者： Meizhi Ju Huilong Duan Haomin Li College of Biomedical Engineering and Instrument Science Zhejiang University Hangzhou China The Children's Hospital The Institute of Translational Medicine Zhejiang University Hangzhou China

Understanding lexical characteristics of clinical documents is the foundation of sublanguage based Medical language processing (MLP) approach. However, there are limited studies focused on the lexical characters of Chinese clinical documents. In this study, a lexical characteristics analysis on both syntactic and semantic levels was conducted in a clinical corpus which contains 3,500 clinical documents generated during daily practices. the analysis was based on the automatic tagging results of a lexicon-based part-of-speech (POS) and semantic tagging method. the medical lexicon contains 237,291 entries annotated with both semantic and syntactic classes. the normalized frequency of different terms, syntactic and semantic classes was calculated and visualized. Major contribution of this paper is providing a wide-coverage Chinese medical semantic lexicon and presenting the lexical characteristics of Chinese clinical documents. Both of these will lay a good foundation for sublanguage based MLP studies in China.

关键词： Semantics Syntactics Medical diagnostic imaging Tagging Noise measurement Grammar Natural language processing

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information retrieval in Telugu language using Synset relationships

Information retrieval in Telugu language using Synset relati...

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international conference on advanced Computing Technologies (ICACT)

作者： Kolikipogu Ramakrishna B Padmaja Rani D Subrahmanyam Department of Information Technology Sridevi Women's Engineering College Hyderabad India Department of CSE JNTU Hyderabad India

information technology brought many applications of information Retrieval as simple as possible through web and other Digital information Access Environment. there is a demand in building Applications of IR in Local languages, which allows common people with minimal knowledge in at least one language to avail the information services. Word mismatch is a common problem in IR System Applications. the research is started to overcome this problem in 4 decades back and still the problem is unsolved. the methods were moving from word replacement to knowledge replacement to solve word mismatch and concept mismatches. this paper mainly explored towards solving word mismatch problem in India language context particularly Telugu language. IR in Telugu language is in inception phase and need huge research to solve many issues in information processing and retrieval. Cross Lingual IR is quite comfortable stage with parallel rule conversion techniques, where as it is difficult to process Telugu alone and build information Retrieval System. Telugu is morphologically rich with high conflational rate. In this paper we build IR in Telugu language and extended to solve vocabulary mismatch problem using Synset relationships. When compared to base Retrieval system, expanded search yield offered results. this paper identified many issues related to topical search and given direction to solve with base proof with implementation results. We found improvement in Recall and precision with Expanded Search with Non-Expanded Retrieval.

关键词： Indexing Vocabulary Context Semantics Search problems Dictionaries

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Flexible Query Answering Systems 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Henrik Legind Larsen Maria J. Martin-Bautista María Amparo Vila Troels Andreasen Henning Christiansen

ISBN: (数字)9783642407697

ISBN: (纸本)9783642407680

this book constitutes the refereed proceedings of the 10th international conference on Flexible Query Answering Systems, FQAS 2013, held in Granada, Spain, in September 2013. the 59 full papers included in this volume were carefully reviewed and selected from numerous submissions. the papers are organized in a general session train and a parallel special session track. the general session train covers the following topics: querying-answering systems; semantic technology; patterns and classification; personalization and recommender systems; searching and ranking; and web and human-computer interaction. the special track covers some specific and, typically, newer fields, namely: environmental scanning for strategic early warning; generating linguistic descriptions of data; advances in fuzzy querying and fuzzy databases: theory and applications; fusion and ensemble techniques for online learning on data streams; and intelligent information extraction from texts.

关键词： Artificial Intelligence information Storage and Retrieval Data Mining and Knowledge Discovery Database Management information Systems Applications (incl. Internet) Natural language processing (NLP)

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Developing a framework to implement public key infrastructure enabled security in XML documents

Developing a framework to implement public key infrastructur...

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IEEE international conference on Multi Topic

作者： F.A. Alvi S.A. Khoja Z. Jabeen Department of Computer Engineering Sir Syed University of Engineering and Technology Pakistan Department of Computer Science Karachi Institute of Information Technology Karachi Pakistan

this work concentrates on proposing a framework to implement the PKI which enables security in XML documents, by defining a common framework and processing rules that can be shared across applications using common tools, avoiding the need for extensive customization of applications to add security. the Framework reuses the concepts, algorithms and core technologies of legacy security systems while introducing changes necessary to support extensible integration with XML. this allows interoperability with a wide range of existing infrastructures and across deployments. Currently no strict security models and mechanisms are available that can provide specification and enforcement of security policies for XML documents. Such models are crucial in order to facilitate a secure dissemination of XML documents, containing information of different sensitivity levels, among (possibly large) user communities.

关键词： Public key XML Data security information security Cryptography web services Computer security Computer science information technology Application software

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Model-driven security for web services in e-Government system: Ideal and real

Model-driven security for Web services in e-Government syste...

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international conference on Next Generation web Services Practices (NWeSP)

作者： Zhendong Ma Christian Wagner thomas Bleier Safety & Security Department Austrian Institute of Technology GmbH Seibersdorf Austria

web services facilitate agile development and deployment of new services in e-Government systems. Due to their distributed nature, web services as well as e-Government systems must be secured against various cyber attacks and ensured that only authorized users can have access to valuable and sometimes sensitive information and resources. However, correct and efficient security engineering for web service-based e-Government systems is not a trivial task. Model-driven security (MDS) is considered to be a promising approach to reduce complexity and increase efficiency in the design and development of secure web services for e-Government systems. Nevertheless, the practicability of MDS has yet not been fully assessed. In a recent pilot project, we have applied MDS to the design and development of web services for the actual e-Government system in Austria. Our work shows that despite extensive research work, several aspects of MDS need to be adapted and further developed such that one can really benefit from such an approach in practice. Our work on addressing these aspects provides realistic assessment and valuable insights on the application of MDS to web services in the real world.

关键词： Unified modeling language web services Electronic government Portals Access control

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An adaptive approach to detecting fake news based on generalized text features 7

An adaptive approach to detecting fake news based on general...

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7th international conference on Computational Linguistics and Intelligent Systems. Volume I: Machine Learning Workshop, CoLInS 2023

作者： Shupta, Andrii Barmak, Olexander Wierzbicki, Adam Skrypnyk, Tetiana Khmelnytskyi National University Institutes 11 st. Khmelnytskyi29016 Ukraine Polish-Japanese Academy of Information Technology Koszykowa 86 st. Warsaw02-008 Poland

Fake news has become a serious problem in recent years as they can quickly spread through social media and other online platforms. Various methods and materials can be used to detect fake news. One approach involves analyzing the content of the news, including the text and accompanying images or videos. Another approach involves considering the social context in which the news is spread, such as the news source and the mood of the people sharing them. An adaptive approach for detecting fake news using Natural language processing is presented in this work. It is proposed to use a feature vector constructed from generalized characteristics of news texts. the possibility of expanding the feature vector and training data sets to adapt the classifier to new types and types of fake news is also proposed. the experimental results presented qualitatively (visual analytics) and quantitatively (statistical metrics) demonstrate the ability of the proposed approach to detect fake news with sufficient quality (90%). Overall, the research aims to contribute to the development of a reliable and accurate system for detecting fake news, which may have important consequences for addressing this problem in modern society. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Fake detection

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A Feature-Augmented Deep Learning Model for Extractive Summarization 7th

A Feature-Augmented Deep Learning Model for Extractive Summa...

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7th EAI international conference on Industrial Networks and Intelligent Systems, INISCOM 2021

作者： Anh, Bui thi Mai Trang, Nguyen thi thu Software Engineering Department School of Information and Communication Technology Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783030774233

Extractive text summarization can be seen as a classification task in which sentences from the document are labelled with either in-summary or not-in-summary. the most salient sentences (i.e., with highest ranking score) from the original document will be selected to generate the summary. Recent success of deep learning in the field of Natural language processing (NLP) has raised a trending research direction for text summarization task. Many neural models have been proposed in which applying recurrent neural network (rNN) for extractive summarization is also becoming increasingly popular. In this paper, we aim to improve the baseline sequence to sequence model proposed by Nallapati et al. by augmenting more sentence features so that the generated summary can benefit from potential features of the whole document. On one hand, the additional sentence-based features enrich the representation vector resulting from the sentence-level RNN of the baseline model. On the other hand, the relevant information from word-level will also be added to the final vector to increase the accuracy of the classification task. the experiment has been conducted for the DailyMail/CNN dataset to evaluate our proposed method and the state of the art works. the empirical results show that the proposed model with augmented features increases about 0.3-0.4 points of ROUGE-1 and ROUGE-2 in comparison with related works. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Recurrent neural networks

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Understanding Prosodic Pauses in Sign language from Motion-Capture and Video-data

Understanding Prosodic Pauses in Sign Language from Motion-C...

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international conference on information and Communication technology and Accessibility (ICTA)

作者： Imen Lagha Achraf Othman Research Laboratory on Technologies of Information and Communication and Electrical Engineering (LaTICE-GE) University of Tunis Tunisia

ISBN: (数字)9781728161839

ISBN: (纸本)9781728161846

In this paper, we deal to understand how the conversational agent (avatar) can achieve human-friendly Sign language interpretations to improve web accessibility to people with hearing disability and hearing impairment with a low level of literacy. One of the key element in Sign language is the prosodic pauses during a conversation in Sign language applied in a machine translation or in real-time interpretation using assistive technologies. We introduce the importance of the prosodic pauses in Sign language through several examples. After that, we present an approach to recognize prosodic pauses from Motion-Capture and Video-data based on the sentence syntax and other grammatical features.

关键词： Assistive technology Gesture recognition Avatars Auditory system Syntactics Sensors Animation

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Understanding Employees’ Capability Towards Workload Clustering From E-Portfolio Data

Understanding Employees’ Capability Towards Workload Cluste...

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international conference on information technology (INCIT)

作者： Piyawat Paramee Worasak Rueangsirarak Surapong Uttama Digital Transformation Technology School of Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in Computer and Communication Engineering for Capacity Building School of Information Technology Mae Fah Luang University Chiang Rai Thailand Center of Excellence in AI and Emerging Technologies School of Information Technology Mae Fah Luang University Chiang Rai Thailand

this paper proposed the workload clustering method for extracting a frequent task assigned to the employees. the data was exported from an Electronic Portfolio (e-Portfolio) system. In this work an investigation on the effectiveness of workload clustering through various clustering algorithms and dimension-reduction techniques were conducted. this research deployed Natural language processing for word tokenizing and feature engineering. the Silhouette scores was adopted to measure across different clustering algorithms such as K-means, Hierarchical, Gaussian Mixture Model (GMM), Fuzzy C-means (FCM), and DBSCAN. As a result, the hierarchical clustering on three clusters achieved the best Silhouette score of 0.442. Subsequently, after performing the Principal Component Analysis (PCA) and visualizing the clustering results in 2-dimensional space, the criteria on clear separation, and distinct employees’ capability were observed.

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Compact Transformer-based language Models for the Moroccan Darija

Compact Transformer-based Language Models for the Moroccan D...

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international conference on information Science and technology (ICIST)

作者： Mohamed Aghzal Mohamed Amine El Bouni Saad Driouech Asmaa Mourhir School of Science and Engineering Al Akhawayn University Ifrane Morocco

Over the past few years, pre-trained language models based on transformer architectures revolutionized the field of natural language processing, achieving state-of-the-art performance on various tasks. However, due to these models’ dependability on enormous corpora for training, very few such models were trained for underresourced languages and dialects, such as Moroccan Darija. In this work, we introduce DarRoBERTa and DarELECTRA which are two transformer-based language models for Darija. We evaluate the language models on the extrinsic tasks of text summarization and topic classification. As for the text summarization task, the results show that DarELECTRA achieves state-of-the art results with a score of 19.25 for Rouge 1, 5.79 for Rouge 2, and 18.01 for Rouge L. For the topic classification task, DarRoBERTa achieved an F1 score of 0.84 and accuracy of 0.86. While our Daija language models achieved results close the Arabic language models, they are much smaller and more efficient.

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