Abstract
Which features are the most important for the text classification tasks? In the automatic text categorization area, several studies seek answers to this question. In this paper, a feature extraction tool for Turkish texts (Text2arff) is presented. The toolbox automatically extracts several features such as the frequencies of the words and ngrams, word clustering, Latent semantic indexing etc. The features of the texts are saved in arff (WEKA) file format. The arff files can be used easily with WEKA machine learning library.
Keywords
Computer science
Toolbox
Artificial intelligence
Turkish
Cluster analysis
Search engine indexing
Feature extraction
Natural language processing
Categorization
Word (group theory)
Feature (linguistics)
Software
Information retrieval
Programming language
Subject Areas
Text and Document Classification Technologies
·Artificial Intelligence ·Physical Sciences
Natural Language Processing Techniques
·Artificial Intelligence ·Physical Sciences
Advanced Text Analysis Techniques
·Artificial Intelligence ·Physical Sciences
Citations by Year
2015: 1
2016: 1
2019: 1
2024: 1
OpenAlex SDG Match
SDGs auto-classified by OpenAlex (score ≥ 0.4 shown).
Quality Education 75%
Loading…
Loading…