1. Question Answering using Language Modeling Some workshop-level thoughts

2. UMass AQUAINT Project Question answering using language models Query triage Question answering with semi-structured data Answer updating Answer granularity

3. QA using LM: IR Models Document texts Document Models Estimate models Rank answers by probability Query text Query texts Relevance Models Document Texts or Models (a) Query-Likelihood Model(b) Document-Likelihood Model

4. QA using LM: Proposed Model Answer Model Question text Relevance ModelQuestion Class Estimate modelQuery triage Interaction Retrieval strategy Human experts Document answer passages Rank answers by probability

5. Query Triage Given a question, what can we infer from it?  IR vs QA (i.e. is it well-formed?)  Quality (does it need to be made more precise)  Type (likely form of answers and granularity)  Human intermediation (should it be directed to a human expert?) Old question in IR – potentially a critical issue in QA

6. QA with Semi-Structured Data: Goal Answer Model Question text Relevance ModelQuestion Class Extended to deal with numeric etc. classes) Estimate model Table answer passages (generated from analysis of tabular and other document structure) Rank answers by probability

7. Research Issues Driving forces: Increased user sophistication, voice input, more queries that look like questions, more natural interaction What to work on:  An appropriate formal basis for QA  Integration of QA and IR and IE  Query triage  Is there an “optimal” answer?  Making QA extensible/scalable  Is a question ontology necessary? Funding: Should recognize the need for integrating QA/IR into a more general notion of information retrieval