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Deliverable #2: Question Classification

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

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Deliverable #2: Question Classification

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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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