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Analysis of Classification Algorithms in Feature Extraction for Text Processing

This report details the methodologies employed by Caleb Barr and Maria Alexandropoulou to automate classification tasks using Java and Python. Key tools included Illinois Chunker for feature extraction and Mallet for classification, focusing on syntactic features for coarse classification. The study utilized various algorithms such as MaxEnt and NaiveBayes, evaluated on different training sets, including Li and Roth's datasets. Results indicated maximum test accuracy with specific feature combinations, highlighting the impacts of unigrams and bigrams on outcomes, while trigrams negatively affected accuracy.

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Analysis of Classification Algorithms in Feature Extraction for Text Processing

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  1. Deliverable #2: Question Classification Group 5 Caleb Barr Maria Alexandropoulou

  2. Software used • JAVA in order to perform feature extraction • Illinois Chunker was applied to extract chunks • Python • Automating classification tasks • Preprocessing of data when necessary • Mallet was used for the classification task

  3. System Properties • Classification Algorithms • MaxEnt • NaiveBayes • Training data • Sum of: • Li and Roth Training set 5 (5500 questions) • TREC-2004 • Test data • Li and Roth test data set • TREC-2005.xml

  4. System Properties (cont.) • Features extracted Focused on syntactic features since we targeted coarse classification (i.e. conclusion in Li and Roth) • Unigrams • Bigrams • Trigrams • Chunks with POS tags • e.g. [NP (DT) (JJ) (NN)] • Head NP/VP chunks as in Li and Roth • e.g. [NP (DT the) (JJS oldest) ] in “What is the oldest profession ? “

  5. Runs performed • Runs were performed for all combinations of classification algorithms and feature templates e.g. MaxEnt, Unigrams NaiveBayes, Unigrams, Bigrams, Chunks etc

  6. Charts

  7. Conclusions • Maximum test accuracy • TREC10: 0.892 • UnigramsBigramsHeads • Maxent • TREC2005: 0.81758 • UnigramsBigramsHeads • NaiveBayes(MaxEnt was very close) • Trigrams affect accuracy negatively – bad feature

  8. Sample confusion matrix for our best accuracy • TREC_10_MaxEnt_UnigramBigramHeads:

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