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T-LAB
Introduction
What T-LAB does and what it enables us to do
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Dictionary Building
Co-occurrence Analysis
Word Associations
Co-Word Analysis and Concept Mapping
Comparison between Word pairs
Sequence and Network Analysis
Concordances
Thematic Analysis
Thematic Analysis of Elementary Contexts
Modeling of Emerging Themes
Thematic Document Classification
Dictionary-Based Classification
Key Contexts of Thematic Words
Comparative Analysis
Specificity Analysis
Correspondence Analysis
Multiple Correspondence Analysis
Cluster Analysis
Singular Value Decomposition
Lexical Tools
Text Screening / Disambiguations
Corpus Vocabulary
Stop-Word List
Multi-Word List
Word Segmentation
Other Tools
Variable Manager
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Contingency Tables
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Glossary
Analysis Unit
Association Indexes
Chi-Square
Cluster Analysis
Coding
Context Unit
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Correspondence Analysis
Data Table
Disambiguation
Dictionary
Elementary Context
Frequency Threshold
Graph Maker
Homograph
IDnumber
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Key-Word (Key-Term)
Lemmatization
Lexical Unit
Lexie and Lexicalization
Markov Chain
MDS
Multiwords
N-grams
Naïve Bayes
Normalization
Occurrences and Co-occurrences
Poles of Factors
Primary Document
Profile
Specificity
Stop Word List
Test Value
Thematic Nucleus
TF-IDF
Variables and Categories
Words and Lemmas
Bibliography
www.tlab.it

Association Indexes


In T-LAB the association indexes (or similarity coefficients) are used to analyse the co-occurrences of the lexical units (LU) inside the elementary contexts (EC), that is to analyse binary data of the presence/absence type.

For instance, given two LU and ten EC, we can obtain the following example:

 

 

 

The same data can be represented in the following way:

 

 

Generalizing and using the letters of the alphabet:

 

 

The formulas corresponding to the six association indexes used by T-LAB are the following:

 

If, for example, we have calculated association coefficients of co-occurrence relationships concerning ten LU, we can obtain a table like the following:

 

In effect T-LAB produces and analyses analogous tables of N x N dimensions (where N can correspond to hundreds of columns), both using Multidimensional Scaling and Cluster Analysis.

Similar tables are also used to calculate second order similarities between pairs of keywords (see the Word Associations tool).