1. Named Entity Classification Chioma Osondu & Wei Wei

2. Classifiers Decision Tree Multinomial Naïve Bayes Support Vector Machines

3. Features Unigrams Bigrams Trigrams Quadrigrams Specialized features like number of words, presence of numbers, etc Stemmed words

4. Accuracy with Tree Depth Accuracy does not grow with the tree depth Accuracy is lower than Maximum Entropy Model with the same sets features.

5. Results & Error Analysis (1) Features Are not abstract enough: Corp., Corporation, Inc., is really the same feature. Out of the 599 disputed classifications, MEM had 481 correct, and the decision tree had 118 correct Not enough features defined on Place, Movie and Person.

6. Results & Error Analysis (2)

7. Results & Error Analysis (3)

8. Results & Error Analysis (4)

9. Conclusion & Future Work Stemmed words are too coarse for multi-way Better accuracies of over 94% can be achieved using a combination of features See Automatic Classification of Previously Unseen Proper Noun Phrases into Semantic Categories Using an N-Gram Letter Model by Stephen Patel & Joseph Smarr (2001 Final Project) Combining classifiers