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Enhancing Named Entity Recognition Through Advanced Classification Techniques

This project explores the implementation of various classifiers, including MaxEnt, DecisionTree, and NaiveBayes, using the Mallet package for effective query processing. We examine the challenges faced with poor classification results and data sparseness, as evidenced by low accuracy in Named Entity Recognition (NER) tasks. Pre-trained models with NLTK were utilized, highlighting the need for alternative NER tools. Our findings indicate possible improvements with BalancedWinnow and MaxEnt classifiers, achieving test accuracies of 0.804 and 0.78 respectively. Future work will focus on leveraging WordNet and exploring class-specific enhancements.

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Enhancing Named Entity Recognition Through Advanced Classification Techniques

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  1. Q/A System First Stage: Classification Project by: Abdullah Alotayq, Dong Wang, Ed Pham

  2. Query Processing • Classification Package: Mallet • Classifiers: Maxent, DecisionTree, C45, NaiveBayes, AdaBoost, Winnow, Balanced Winnow, Bagging Trainer .etc

  3. Main Techniques

  4. Features Semantic Morphological Neighboring (Syntactic)

  5. Stemming • nltk stemmer

  6. N-grams • Bigrams:

  7. Trigrams: • Poor Classification results • 0.48 • 0.478 • Not A good strategy .

  8. NER (Named Entity Recognition) • nltk NER • pre-trained model to do this task. • 6 types of NE

  9. Frequencies Training Data:

  10. Test Data:

  11. NO Named Entity detected • In training data: 3533, namely 64.8% • In test data, 353, 70.6%. -> data sparseness problem

  12. NER Results & Future work • Test data accuracy= 0.802 • we might try other NE tools, which would give more NE types and cover more percentage on training and test data.

  13. Binary and Real Values • Testing for potential improvement. • Best performing classifiers: For Binary: • BalancedWinnow: Test data accuracy= 0.804 • MaxEnt: Test accuracy mean = 0.78 For Real Values: • BalancedWinnow: Test data accuracy= 0.784 • MaxEnt: Test data accuracy= 0.758

  14. Data set1:

  15. Data set2:

  16. Proposed future improvement • WordNetSenses • Class-Specific Related Words

  17. Issues • Performing poorly on some refinements. • Low accuracy scores: • 0.42 • 0.54 • Memory consuming classifiers. • Classifiers showed some error messages.

  18. Successes • Made progress in creating the system. • Had some hands-on experience dealing with classifiers, and NLP packages. • Learned ways to improve classification results.

  19. Readings that helped • Employing Two Question Answering Systems in TREC-2005, SandaHarabagiu & others.

  20. Software packages participated • Mallet • NLTK • Porter-stemmer • Self-written code files • Stanford Parser, Berkeley Parser

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