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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
  38. Altmetric Badge
    Chapter 37 Aspect Extraction from Reviews Using Convolutional Neural Networks and Embeddings
Attention for Chapter 13: From Web Crawled Text to Project Descriptions: Automatic Summarizing of Social Innovation Projects
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About this Attention Score

  • Average Attention Score compared to outputs of the same age
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

Mentioned by

2 tweeters

Readers on

8 Mendeley
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Chapter title
From Web Crawled Text to Project Descriptions: Automatic Summarizing of Social Innovation Projects
Chapter number 13
Book title
Lecture Notes in Computer Science
Published in
Lecture notes in computer science, June 2019
DOI 10.1007/978-3-030-23281-8_13
Book ISBNs
978-3-03-023280-1, 978-3-03-023281-8

Nikola Milošević, Dimitar Marinov, Abdullah Gök, Goran Nenadić, Abdullah Gӧk, Goran Nenadic

Twitter Demographics

The data shown below were collected from the profiles of 2 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 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 38%
Student > Ph. D. Student 3 38%
Student > Doctoral Student 1 13%
Unknown 1 13%
Readers by discipline Count As %
Computer Science 4 50%
Engineering 2 25%
Business, Management and Accounting 1 13%
Unknown 1 13%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 21 June 2019.
All research outputs
of 15,295,156 outputs
Outputs from Lecture notes in computer science
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Outputs of similar age
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Outputs of similar age from Lecture notes in computer science
of 18 outputs
Altmetric has tracked 15,295,156 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,554 research outputs from this source. They receive a mean Attention Score of 4.5. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 262,472 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.