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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 Multiword Expressions: A Pain in the Neck for NLP
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    Chapter 2 A Hypothesis on the Origin of the Sign Types
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    Chapter 3 Quantification and Intensionality in Situation Semantics
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    Chapter 4 Generalized Quantification in Situation Semantics
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    Chapter 5 Sign Language Translation via DRT and HPSG
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    Chapter 6 Multilayered Extended Semantic Networks as a Language for Meaning Representation in NLP Systems
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    Chapter 7 Constructing a Sensuous Judgment System Based on Conceptual Processing
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    Chapter 8 Towards a Natural Language Driven Automated Help Desk
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    Chapter 9 Lexical Tuning
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    Chapter 10 A Baseline Methodology for Word Sense Disambiguation
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    Chapter 11 An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet
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    Chapter 12 Feature Selection Analysis for Maximum Entropy-Based WSD
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    Chapter 13 Combining Supervised-Unsupervised Methods for Word Sense Disambiguation
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    Chapter 14 A Proposal for WSD Using Semantic Similarity
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    Chapter 15 A New, Fully Automatic Version of Mitkov’s Knowledge-Poor Pronoun Resolution Method
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    Chapter 16 Pronominal Anaphora Generation in an English-Spanish MT Approach
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    Chapter 17 Using LSA for Pronominal Anaphora Resolution
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    Chapter 18 The Spanish Auxiliary Verb System in HPSG
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    Chapter 19 Surface Syntactic Relations in Spanish
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    Chapter 20 Parsing Ill-Formed Inputs with Constraint Graphs
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    Chapter 21 Part-of-Speech Tagging with Evolutionary Algorithms
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    Chapter 22 Formal Methods of Tokenization for Part-of-Speech Tagging
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    Chapter 23 Sepe: A POS Tagger for Spanish
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    Chapter 24 Fuzzy Set Tagging
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    Chapter 25 Towards a Standard for a Multilingual Lexical Entry: The EAGLES/ISLE Initiative
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    Chapter 26 Quantitative Comparison of Homonymy in Spanish EuroWordNet and Traditional Dictionaries
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    Chapter 27 Compilation of a Spanish Representative Corpus
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    Chapter 28 Aligning Multiword Terms Using a Hybrid Approach
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    Chapter 29 Automatic Selection of Defining Vocabulary in an Explanatory Dictionary
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    Chapter 30 Integrated Natural Language Generation with Schema-Tree Adjoining Grammars
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    Chapter 31 Experiments with a Bilingual Document Generation Environment
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    Chapter 32 A Computational Model of Change in Politeness with the Addition of Word Endings
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    Chapter 33 Tartar Morphology Implementation
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    Chapter 34 Automatic Generation of Pronunciation Lexicons for Spanish
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    Chapter 35 Diacritics Restoration: Learning from Letters versus Learning from Words
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    Chapter 36 A Comparative Study of Information Extraction Strategies
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    Chapter 37 Answer Extraction in Technical Domains
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    Chapter 38 Automatic Extraction of Non-standard Lexical Data for a Metalinguistic Information Database
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    Chapter 39 Text Segmentation for Efficient Information Retrieval
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    Chapter 40 Using Syntactic Dependency-Pairs Conflation to Improve Retrieval Performance in Spanish
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    Chapter 41 Multi-document Summarization Using Informative Words and Its Evaluation with a QA System
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    Chapter 42 Automated Selection of Interesting Medical Text Documents by the TEA Text Analyzer
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    Chapter 43 Chinese Documents Classification Based on N-Grams
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    Chapter 44 Cross-Lingual Document Similarity Calculation Using the Multilingual Thesaurus EUROVOC
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    Chapter 45 Empirical Formula for Testing Word Similarity and Its Application for Constructing a Word Frequency List
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    Chapter 46 AutoMarkup: A Tool for Automatically Marking up Text Documents
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    Chapter 47 Identification of Recurrent Patterns to Extract Definitory Contexts
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    Chapter 48 Specification Marks Method: Design and Implementation
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 (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

patent
1 patent
wikipedia
7 Wikipedia pages

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
5 Mendeley
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Title
Computational Linguistics and Intelligent Text Processing
Published by
Lecture notes in computer science, February 2002
DOI 10.1007/3-540-45715-1
ISBNs
978-3-54-043219-7, 978-3-54-045715-2
Authors

Alexander Gelbukh

Editors

Alexander Gelbukh

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 %
United Kingdom 1 20%
United States 1 20%
Unknown 3 60%

Demographic breakdown

Readers by professional status Count As %
Professor 1 20%
Lecturer 1 20%
Other 1 20%
Researcher 1 20%
Unspecified 1 20%
Other 0 0%
Readers by discipline Count As %
Computer Science 3 60%
Linguistics 1 20%
Unspecified 1 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 04 February 2023.
All research outputs
#4,827,013
of 23,283,373 outputs
Outputs from Lecture notes in computer science
#1,582
of 8,163 outputs
Outputs of similar age
#13,389
of 122,991 outputs
Outputs of similar age from Lecture notes in computer science
#4
of 36 outputs
Altmetric has tracked 23,283,373 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,163 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has done well, scoring higher than 79% 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 122,991 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 36 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.