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Natural Language Processing and Information Systems

Overview of attention for book
Cover of 'Natural Language Processing and Information Systems'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Deep Neural Network Models for Paraphrased Text Classification in the Arabic Language
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    Chapter 2 Model Answer Generation for Word-Type Questions in Elementary Mathematics
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    Chapter 3 Learning Mobile App Embeddings Using Multi-task Neural Network
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    Chapter 4 Understanding User Query Intent and Target Terms in Legal Domain
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    Chapter 5 Bidirectional Transformer Based Multi-Task Learning for Natural Language Understanding
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    Chapter 6 LSVS: Link Specification Verbalization and Summarization
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    Chapter 7 Deceptive Reviews Detection Using Deep Learning Techniques
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    Chapter 8 JASs: Joint Attention Strategies for Paraphrase Generation
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    Chapter 9 Structure-Based Supervised Term Weighting and Regularization for Text Classification
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    Chapter 10 Gated Convolutional Neural Networks for Domain Adaptation
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    Chapter 11 A Keyword Search Approach for Semantic Web Data
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    Chapter 12 Intent Based Association Modeling for E-commerce
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    Chapter 13 From Web Crawled Text to Project Descriptions: Automatic Summarizing of Social Innovation Projects
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    Chapter 14 Cross-Corpus Training with CNN to Classify Imbalanced Biomedical Relation Data
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    Chapter 15 Discourse-Driven Argument Mining in Scientific Abstracts
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    Chapter 16 TAGS: Towards Automated Classification of Unstructured Clinical Nursing Notes
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    Chapter 17 Estimating the Believability of Uncertain Data Inputs in Applications for Alzheimer’s Disease Patients
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    Chapter 18 Deep Genetic Algorithm-Based Voice Pathology Diagnostic System
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    Chapter 19 An Arabic-Multilingual Database with a Lexicographic Search Engine
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    Chapter 20 Bug Severity Prediction Using a Hierarchical One-vs.-Remainder Approach
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    Chapter 21 A Coherence Model for Sentence Ordering
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    Chapter 22 Unified Parallel Intent and Slot Prediction with Cross Fusion and Slot Masking
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    Chapter 23 Evaluating the Accuracy and Efficiency of Sentiment Analysis Pipelines with UIMA
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    Chapter 24 Comparing Different Word Embeddings for Multiword Expression Identification
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    Chapter 25 Analysis and Prediction of Dyads in Twitter
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    Chapter 26 Mathematical Expression Extraction from Unstructured Plain Text
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    Chapter 27 A Study on Self-attention Mechanism for AMR-to-text Generation
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    Chapter 28 PreMedOnto: A Computer Assisted Ontology for Precision Medicine
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    Chapter 29 An Approach for Arabic Diacritization
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    Chapter 30 A Novel Approach Towards Fake News Detection: Deep Learning Augmented with Textual Entailment Features
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    Chapter 31 Contextualized Word Embeddings in a Neural Open Information Extraction Model
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    Chapter 32 Towards Recognition of Textual Entailment in the Biomedical Domain
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    Chapter 33 Development of a Song Lyric Corpus for the English Language
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    Chapter 34 A Natural Language Interface Supporting Complex Logic Questions for Relational Databases
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    Chapter 35 Waste Not: Meta-Embedding of Word and Context Vectors
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    Chapter 36 Extracting Statistical Mentions from Textual Claims to Provide Trusted Content
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    Chapter 37 Aspect Extraction from Reviews Using Convolutional Neural Networks and Embeddings
Overall attention for this book and its chapters
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Title
Natural Language Processing and Information Systems
Published by
Springer International Publishing, January 2019
DOI 10.1007/978-3-030-23281-8
ISBNs
978-3-03-023280-1, 978-3-03-023281-8
Editors

Elisabeth Métais, Farid Meziane, Sunil Vadera, Vijayan Sugumaran, Mohamad Saraee

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 25 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 36%
Student > Ph. D. Student 4 16%
Lecturer 2 8%
Student > Bachelor 2 8%
Researcher 1 4%
Other 1 4%
Unknown 6 24%
Readers by discipline Count As %
Computer Science 13 52%
Mathematics 1 4%
Linguistics 1 4%
Medicine and Dentistry 1 4%
Chemistry 1 4%
Other 1 4%
Unknown 7 28%