1. Extracting Why Text Segment from Web Based on Grammar-gram
Iulia Nagy, Master student,

2. Summary Introduction Related work “Bag of Function Words” method
Rule Based Methods
Machine Learning Approach
“Bag of Function Words” method
Method outline
Adaptation of “Bag of Function Words” to English
Experiments and Evaluation
Conclusion and Remarks

3. Problem
tremendous growth of the Internet　　information hard to find

4. Solution Create QA system an exact question
system capable to give an exact answer to
an exact question
detect answer from arbitrary corpora
Purpose
obtain viable information rapidly

5. Purpose of our research
Create a why-QA system with automatically-built classifier
Classifier
Use a model presented in Japanese Literature
created using Machine learning
based on Bag of Grammar approach
Purpose of this paper
adapt Japanese method to English
test effectiveness of the method on English

6. Related word Two main trends Rule Based methods
Machine Learning methods
Preprocess text
Detect patterns
Create set of rules
Apply rules to identify why-answer from text
Preprocess text
Identify and extract relevant features
Create classification scheme
Classify

7. Rule based in why-QA Suzan Vererne’s Approach Method : + -
Improve performance by re-ranking
Method :
weight the score assigned to a QA-pair by QAP with a number of syntactic features. + -
Hardly adaptable to various languages
Deep grammar knowledge
Labour intensive
Importance of syntax
Effective

8. Machine Learning method
Higashinaka and Isozaki’s Approach
Acquire causal expression from Japanese EDR dictionary
Method :
train a ranker based on clause structures extracted from EDR + -
Hardly adaptable to various languages
Not fully automated: based on EDR
EDR rather high priced
Partially automated
Effective

9. Machine Learning method
Tanaka’s Approach
Build why-classifier with function words as features
Method :
Bag of function words
Adaptable to different languages
Domain independent
Scalable
Effective
Fully automated

10. Bag of function words Function words
Machine learning approach to automatically build domain independent why-classifier based of function words
Conditions to obtain domain-independence
Class fulfilling conditions
Convergence and reasonable size of feature space
Generality of features in feature space
Ability of features to discriminate causality
Function words

11. Bag of function words Method – same baseline for Japanese and English
Ts 1
Create feature
space
Create feature vectors
Extract
function words
Ts 2 …
Tag
label all words with POS tagger
Classify
Determine POS for function words
Ts n
Mapping using “tf-idf” on function words
𝑥 𝑖 , 𝑦 𝑖
𝑦 𝑖 є 𝑡𝑟𝑢𝑒, 𝑓𝑎𝑙𝑠𝑒
Vectors' format:
Fv 1
Fv 2
Fv n …
for because at after in under which that why to therefore
Classification scheme
Trainer
Loogit Boost
weak learners

12. Adaptation to English Differences Japanese English Adjustments
Identify eligible function words in English
Japanese
Forms phrases by adding new words at the end of the phrase
Use of particles to define syntactic roles in a phrase
English
Forms phrases by adding new words at the beginning of the phrase
Words do not belong to an only grammatical category

13. Dataset : 432 text segments
Experiment
216 Why answers
216 definitions
Dataset : 432 text segments
Data
Processing
Label all words with POS and extract function words
Calculate tf-idf for each function word
Map features from feature set into feature vectors

14. Experiment Classifier Evaluation
Used Loogit Boost (Weka) with Decision stump
Created 5 classifiers (50, 100, 150, 200, 250 iterations)
Evaluation
10-fold cross validation
Models trained on 9 folds and tested on 1
Measured precision, recall and F-measure

15. Results – why text segments
No of iterations

16. Results – non why text segments (NWTS)
No of iterations

17. Method effective on English
Conclusion
Results
321 instances out of 432 correctly classified
76.1% precision and 70.6% recall on WTS
72.6% precision and 77.9% recall on NWTS
Method effective on English
Type of TS

18. Future works Experiment with a increased dataset (> 5000)
Use Yahoo!Answers database to extract dataset
Interest
Include causative construction in the analysis
to identify optimal number of iteration
to make a better selection of the function words to be used English
English often expresses cause by a closed set of verbs or nouns
Increase accuracy of the classifier

19. Thank you for your attention !
Questions and remarks
Thank you for your attention !