1. Learning Extraction Patterns for Subjective Expressions 2007/10/09 DataMining Lab 안민영

2. Content  Background  Learning and Bootstrapping Extraction Patterns for Subjectivity  Experimental Results  Conclusions

3. Background  The Goal of Work  Classify individual sentences as subjective or objective  The Goal of Research  To use high-precision subjectivity classifiers to automatically identify subjective and objective  Extraction Patterns  Typically use lexico-syntactic patterns to identify relevant  Hypotheses  Extraction patterns would be able to represent subjective expression that have noncompositional meanings Ex) drives (someone) up the wall -George drives me up the wall -She drives me up the wall

4. Learning and Bootstrapping Extraction Patterns for Subjectivity  Bootstrapping process for subjectivity classification  High precision classifiers can be used to automatically Using to a training set to automatically learn extraction  this data can be used as a training set to automatically learn extraction patterns  Learned patterns can be used to grow training set  Subjectivity clues are divided into  Strongly subjective,Weakly subjective  Use a combination of manual review and empirical results

5. Learning and Bootstrapping Extraction Patterns for Subjectivity

6.  High-precision subjective classifier  Classify a sentence as subjective if it contains two or more  Test set : 2197 sentences, 59% subjective  Precision: 91.5%, Recall: 31.9%  High-precision objective classifier  Rather than looking for the presence of lexical items, looks for absence  Precision: 82.6%, Recall: 16.4%

7. Learning and Bootstrapping Extraction Patterns for Subjectivity  Learning Subjective Extraction Patterns  To automatically learn extraction patterns that are associated with subjectivity  Use a learning algorithm similar to AutoSlog-TS (Riloff, 1996).

8. Learning and Bootstrapping Extraction Patterns for Subjectivity  What is AutoSlog-TS (Riloff, 1996)?  Autoslog is the first system to learn text extraction dictionary from training examples  AutoSlog-TS is what generates extraction patterns from untagged text  Requires two cormora:  Relevant  Irrelevant  Based on AutoSlog which requires tagging

9. Learning and Bootstrapping Extraction Patterns for Subjectivity  for this work  Want a fully automatic process that does not depend on a human reviewer  Were most interested in finding patterns that can identify subjective expressions with high precision. (noun) fact = subjective expression

10. Experimental Results  Subjectivity Data  Use consists of English-language version of foreign news documents from FBIS, U.S.Foreign Broadcast Information Service  Evaluation of the learned patterns  Pool of unannotated texts : 302163 individual sentences  Evaluated 18 different subsets of the patterns by selecting the patterns that pass certain thresholds  Extraction patterns perform quite well : precision ranges from 71%~85%

11. Experimental Results  Evaluation of the Bootstrapping Preprocess  Use the learned extraction patterns to classify previously unlabeled sentences  bootstrapping process does not learn new objective sentences  Didn’t want to simply add the new subjective sentences  Modify the HP-Subj classifier to use extraction patterns  - contains two or more learned patterns  - contains one of the clues used by the original one

12. Experimental Results

13. Conclusions  High-precision subjectivity classification can be used to generate a large amount of labeled training data  Show that an extraction pattern learning technique can learn subjective expressions that are linguistically richer than individual words or fixed phrases.  Augment our original high-precision subjective classifier with these newly learned extraction patterns