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T-LAB
Introduction
What T-LAB does and what it enables us to do
Requirements and Performances
Corpus Preparation
Corpus Preparation
Structural Criteria
Formal Criteria
File
Prepare a Corpus (Corpus Builder)
Import a Corpus
Open an existing project
Settings
Automatic Settings
Customized Settings
Co-occurrence Analysis
Word Associations
Co-Word Analysis and Concept Mapping
Comparison between Word pairs
Sequence Analysis
Concordances
Thematic Analysis
Thematic Analysis of Elementary Contexts
Thematic Document Classification
Dictionary-Based Classification
Modeling of Emerging Themes
Key Contexts of Thematic Words
Comparative Analysis
Specificity Analysis
Correspondence Analysis
Multiple Correspondence Analysis
Cluster Analysis
Contingency Tables
Lexical Tools
Corpus Vocabulary
Dictionary Building
Disambiguation
Stop-Word List
Multi-Word List
Other Tools
Variable Manager
Create a Sub-Corpus
Editor
Memo
Glossary
Analysis Unit
Association Indexes
Chi-Square
Cluster Analysis
Coding
Context Unit
Corpus and Subsets
Correspondence Analysis
Data Table
Disambiguation
Dictionary
Elementary Context
Frequency Threshold
Homograph
IDnumber
Isotopy
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

Automatic Settings


When this option is selected in the subsequent analyses the Key-Terms used will be chosen automatically by T-LAB.

The automatic list includes up to 3,000 lexical units which belong to the category of content words: nouns, verbs, adjectives and adverbs.

The selection criterion varies according to the kind of file analysed.

If the corpus is a single text T-LAB simply selects the lexical units with the highest occurrence values.

If the corpus is made up of two or more texts T-LAB uses the following algorithm:

· it selects the words with occurrence values higher than the minimum threshold;
· it computes the TF-IDF or applies the chi-square test to all the crosses of each selected word for all the texts being analysed (N.B.: In the case of chi square test, the maximum number of text allowed is 500);
· it selects the words with the TF-IDF or chi-square highest values, that is those words that, in the corpus, make the difference.

You can check the list of T-LAB key-terms by using the function Memo.