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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 Influence of Treebank Design on Representation of Multiword Expressions
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    Chapter 2 Combining Contextual and Structural Information for Supersense Tagging of Chinese Unknown Words
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    Chapter 3 Identification of Conjunct Verbs in Hindi and Its Effect on Parsing Accuracy
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    Chapter 4 Identification of Reduplicated Multiword Expressions Using CRF
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    Chapter 5 Computational Linguistics and Natural Language Processing
  7. Altmetric Badge
    Chapter 6 An Unsupervised Approach for Linking Automatically Extracted and Manually Crafted LTAGs
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    Chapter 7 Tamil Dependency Parsing: Results Using Rule Based and Corpus Based Approaches
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    Chapter 8 Incremental Combinatory Categorial Grammar and Its Derivations
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    Chapter 9 Dependency Syntax Analysis Using Grammar Induction and a Lexical Categories Precedence System
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    Chapter 10 Labelwise Margin Maximization for Sequence Labeling
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    Chapter 11 Co-related Verb Argument Selectional Preferences
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    Chapter 12 Combining Diverse Word-Alignment Symmetrizations Improves Dependency Tree Projection
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    Chapter 13 An Analysis of Tree Topological Features in Classifier-Based Unlexicalized Parsing
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    Chapter 14 Part-of-Speech Tagging from 97% to 100%: Is It Time for Some Linguistics?
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    Chapter 15 Ripple Down Rules for Part-of-Speech Tagging
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    Chapter 16 An Efficient Part-of-Speech Tagger for Arabic
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    Chapter 17 An Evaluation of Part of Speech Tagging on Written Second Language Spanish
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    Chapter 18 Onoma: A Linguistically Motivated Conjugation System for Spanish Verbs
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    Chapter 19 Measuring Similarity of Word Meaning in Context with Lexical Substitutes and Translations
  21. Altmetric Badge
    Chapter 20 A Quantitative Evaluation of Global Word Sense Induction
  22. Altmetric Badge
    Chapter 21 Computational Linguistics and Intelligent Text Processing
  23. Altmetric Badge
    Chapter 22 Deep Semantics for Dependency Structures
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    Chapter 23 Combining Heterogeneous Knowledge Resources for Improved Distributional Semantic Models
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    Chapter 24 Improving Text Segmentation with Non-systematic Semantic Relation
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    Chapter 25 Automatic Identification of Cause-Effect Relations in Tamil Using CRFs
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    Chapter 26 Comparing Approaches to Tag Discourse Relations
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    Chapter 27 Semi-supervised Discourse Relation Classification with Structural Learning
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    Chapter 28 Integrating Japanese Particles Function and Information Structure
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    Chapter 29 Assessing Lexical Alignment in Spontaneous Direction Dialogue Data by Means of a Lexicon Network Model
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    Chapter 30 Towards Well-Grounded Phrase-Level Polarity Analysis
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    Chapter 31 Implicit Feature Identification via Co-occurrence Association Rule Mining
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    Chapter 32 Construction of Wakamono Kotoba Emotion Dictionary and Its Application
  34. Altmetric Badge
    Chapter 33 Temporal Analysis of Sentiment Events – A Visual Realization and Tracking
  35. Altmetric Badge
    Chapter 34 Highly-Inflected Language Generation Using Factored Language Models
  36. Altmetric Badge
    Chapter 35 Prenominal Modifier Ordering in Bengali Text Generation
  37. Altmetric Badge
    Chapter 36 Bootstrapping Multiple-Choice Tests with The-Mentor
Overall attention for this book and its chapters
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About this Attention Score

  • Good Attention Score compared to outputs of the same age and source (65th percentile)

Mentioned by

wikipedia
2 Wikipedia pages

Readers on

mendeley
10 Mendeley
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Title
Computational Linguistics and Intelligent Text Processing
Published by
Lecture notes in computer science, January 2011
DOI 10.1007/978-3-642-19400-9
ISBNs
978-3-64-219399-6, 978-3-64-219400-9
Authors

Alexander F. Gelbukh

Editors

Gelbukh, Alexander F.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 10 100%

Demographic breakdown

Readers by professional status Count As %
Professor 2 20%
Student > Ph. D. Student 1 10%
Researcher 1 10%
Student > Master 1 10%
Unknown 5 50%
Readers by discipline Count As %
Computer Science 2 20%
Linguistics 2 20%
Biochemistry, Genetics and Molecular Biology 1 10%
Economics, Econometrics and Finance 1 10%
Engineering 1 10%
Other 0 0%
Unknown 3 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 26 July 2012.
All research outputs
#7,459,393
of 22,805,349 outputs
Outputs from Lecture notes in computer science
#2,487
of 8,126 outputs
Outputs of similar age
#54,414
of 180,739 outputs
Outputs of similar age from Lecture notes in computer science
#75
of 317 outputs
Altmetric has tracked 22,805,349 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,126 research outputs from this source. They receive a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 55% 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 180,739 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 317 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 65% of its contemporaries.