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Computational Linguistics and Intelligent Text Processing

Overview of attention for book
Cover of 'Computational Linguistics and Intelligent Text Processing'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Thinking Outside the Box for Natural Language Processing
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    Chapter 2 A Graph-Based Method to Improve WordNet Domains
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    Chapter 3 Corpus-Driven Hyponym Acquisition for Turkish Language
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    Chapter 4 Automatic Taxonomy Extraction in Different Languages Using Wikipedia and Minimal Language-Specific Information
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    Chapter 5 Ontology-Driven Construction of Domain Corpus with Frame Semantics Annotations
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    Chapter 6 Building a Hierarchical Annotated Corpus of Urdu: The URDU.KON-TB Treebank
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    Chapter 7 A Morphological Analyzer Using Hash Tables in Main Memory (MAHT) and a Lexical Knowledge Base
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    Chapter 8 Optimal Stem Identification in Presence of Suffix List
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    Chapter 9 On the Adequacy of Three POS Taggers and a Dependency Parser
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    Chapter 10 Will the Identification of Reduplicated Multiword Expression (RMWE) Improve the Performance of SVM Based Manipuri POS Tagging?
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    Chapter 11 On Formalization of Word Order Properties
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    Chapter 12 Core-Periphery Organization of Graphemes in Written Sequences: Decreasing Positional Rigidity with Increasing Core Order
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    Chapter 13 Discovering Linguistic Patterns Using Sequence Mining
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    Chapter 14 What about Sequential Data Mining Techniques to Identify Linguistic Patterns for Stylistics?
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    Chapter 15 Resolving Syntactic Ambiguities in Natural Language Specification of Constraints
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    Chapter 16 A Computational Grammar of Sinhala
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    Chapter 17 Automatic Identification of Persian Light Verb Constructions
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    Chapter 18 A Cognitive Approach to Word Sense Disambiguation
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    Chapter 19 A graph-Based Approach to WSD Using Relevant Semantic Trees and N-Cliques Model
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    Chapter 20 Using Wiktionary to Improve Lexical Disambiguation in Multiple Languages
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    Chapter 21 Two Stages Based Organization Name Disambiguity
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    Chapter 22 Optimizing CRF-Based Model for Proper Name Recognition in Polish Texts
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    Chapter 23 Methods of Estimating the Number of Clusters for Person Cross Document Coreference Task
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    Chapter 24 Coreference Resolution Using Tree CRFs
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    Chapter 25 Arabic Entity Graph Extraction Using Morphology, Finite State Machines, and Graph Transformations
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    Chapter 26 Integrating Rule-Based System with Classification for Arabic Named Entity Recognition
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    Chapter 27 Space Projections as Distributional Models for Semantic Composition
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    Chapter 28 Distributional Models and Lexical Semantics in Convolution Kernels
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    Chapter 29 Multiple Level of Referents in Information State
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    Chapter 30 Inferring the Scope of Negation in Biomedical Documents
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    Chapter 31 LDA-Frames: An Unsupervised Approach to Generating Semantic Frames
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    Chapter 32 Unsupervised Acquisition of Axioms to Paraphrase Noun Compounds and Genitives
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    Chapter 33 Age-Related Temporal Phrases in Spanish and Italian
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    Chapter 34 Can Modern Statistical Parsers Lead to Better Natural Language Understanding for Education?
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    Chapter 35 Exploring Classification Concept Drift on a Large News Text Corpus
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    Chapter 36 An Empirical Study of Recognizing Textual Entailment in Japanese Text
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    Chapter 37 Automated Detection of Local Coherence in Short Argumentative Essays Based on Centering Theory
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    Chapter 38 A Symbolic Approach for Automatic Detection of Nuclearity and Rhetorical Relations among Intra-sentence Discourse Segments in Spanish
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    Chapter 39 Feature Specific Sentiment Analysis for Product Reviews
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    Chapter 40 Biographies or Blenders: Which Resource Is Best for Cross-Domain Sentiment Analysis?
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    Chapter 41 A Generate-and-Test Method of Detecting Negative-Sentiment Sentences
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    Chapter 42 Roles of Event Actors and Sentiment Holders in Identifying Event-Sentiment Association
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    Chapter 43 Applying Sentiment and Social Network Analysis in User Modeling
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    Chapter 44 The 5W Structure for Sentiment Summarization-Visualization-Tracking
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    Chapter 45 The Naive Bayes Classifier in Opinion Mining: In Search of the Best Feature Set
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    Chapter 46 A Domain Independent Framework to Extract and Aggregate Analogous Features in Online Reviews
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    Chapter 47 Learning Lexical Subjectivity Strength for Chinese Opinionated Sentence Identification
  49. Altmetric Badge
    Chapter 48 Building Subjectivity Lexicon(s) from Scratch for Essay Data
  50. Altmetric Badge
    Chapter 49 Emotion Ontology Construction from Chinese Knowledge
Overall attention for this book and its chapters
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

twitter
9 X users
wikipedia
2 Wikipedia pages

Readers on

mendeley
5 Mendeley
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Title
Computational Linguistics and Intelligent Text Processing
Published by
Lecture notes in computer science, January 2012
DOI 10.1007/978-3-642-28604-9
ISBNs
978-3-64-228603-2, 978-3-64-228604-9
Authors

Alexander Gelbukh

Editors

Gelbukh, Alexander, Gelbukh, Alexander

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 40%
Switzerland 1 20%
Unknown 2 40%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 80%
Student > Doctoral Student 2 40%
Student > Ph. D. Student 2 40%
Student > Bachelor 2 40%
Researcher 2 40%
Other 1 20%
Readers by discipline Count As %
Computer Science 7 140%
Engineering 2 40%
Linguistics 1 20%
Social Sciences 1 20%
Business, Management and Accounting 1 20%
Other 0 0%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 20 May 2022.
All research outputs
#3,064,311
of 23,567,572 outputs
Outputs from Lecture notes in computer science
#645
of 8,153 outputs
Outputs of similar age
#24,570
of 247,623 outputs
Outputs of similar age from Lecture notes in computer science
#45
of 489 outputs
Altmetric has tracked 23,567,572 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,153 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has done particularly well, scoring higher than 92% of its peers.
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 247,623 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 489 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.