1. Question Answering via Question-to-Question Mapping
Tait Larson
Johnson Gong
Josh Daniel

2. Overview QA systems on the web currently are popular
Common QA systems extract facts from the web, match natural language questions to facts
Our approach
Take pre-answered questions, and match questions to questions
Hopefully useful because of the vast amount of pre-answered questions available on the web
Google answers
Yahoo answers
Lawguru.com
FAQs
Do this via several NLP techniques, primarily focused around query expansion using Wordnet and language model

3. Query Expansion POS tagging – preprocess
Search through domain of semantically similar sentences
Goal: Generate phrases that will identify semantically equivalent questions in our corpus
Semantic expansion
Language Model for pruning
Prune incorrect word sense
Trained on question repository
“Can I get into Stanford?” -> “Butt I get into Stanford?”
Phrase Extraction

4. Information Retrieval
Different from traditional IR
Bigrams
Index
Query
No stop words
No stemming
Why?
These choice emphasize semantic structure of question

5. Results Us vs Yahoo Metric - Mean Reciprocal Rank
Test questions from “unresolved” Yahoo questions
Metric - Mean Reciprocal Rank
We only index questions, Yahoo indexes answers also

6. Example Results
A sharp pain in the center of the chest breastbone area?
Keep getting a throbbing pain in the middle of my rib cage . any idea what it could be?
Do Bush baked beans give you gas
Do baked beans make you fart???? yes/no?
Why i sweat and how can i stop this problem
How can I stop sweating? I sweat more when it’s cold…