1. From Question-Answering to Information-Seeking Dialogs Jerry R. Hobbs Artificial Intelligence Center SRI International Menlo Park, California (with Douglas Appelt, Chris Culy, David Israel, David Martin, Martin Reddy, Mark Stickel, and Richard Waldinger)

2. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International2 Key Ideas 1. Logical analysis/decomposition of questions into component questions, using a reasoning engine 2. Bottoming out in variety of web resources and information extraction engine 3. Use of component questions to drive subsequent dialogue, for elaboration, revision, and clarification 4. Use of analysis of questions to determine, formulate, and present answers.

3. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International3 Plan of Attack Inference-Based System: Inference for Question-Answering -- this year Inference for Dialog Structure -- next year, but starting design this year Document retrieval and information extraction for question-answering: Incorporate as resource in inference-based system -- this year

4. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International4 Composition of Information from Multiple Sources How far is it from Mascat to Kandahar? What is the lat/long of Mascat? What is the distance between the two lat/longs? What is the lat/long of Kandahar? Alexandrian Digital Library Gazetteer Geographical Formula or www.nau.edu/~cvm/latlongdist.html Question Decomposition via Logical Rules Resources Attached to Reasoning Process Alexandrian Digital Library Gazetteer GEMINI SNARK

5. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International5 Composition of Information from Multiple Sources Show me the region 100 km north of the capital of Afghanistan. What is the capital of Afghanistan? What is the lat/long 100 km north? What is the lat/long of Kabul? CIA Fact Book Geographical Formula Question Decomposition via Logical Rules Alexandrian Digital Library Gazetteer Show that lat/long Terravision Resources Attached to Reasoning Process

6. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International6 Combining Time, Space, and Personal Information Could Mohammed Atta have met with an Iraqi official between 1998 and 2001? IE Engine Geographical Reasoning Question Decomposition via Logical Rules Resource Attached to Reasoning Process meet(a,b,t) & 1998  t  2001 at(a,x 1,t) & at(b,x 2,t) & near(x 1,x 2 ) & official(b,Iraq) go(a,x 1,t)go(b,x 2,t) IE Engine Temporal Reasoning Logical Form

7. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International7 Two Central Systems GEMINI: Large unification grammar of English Under development for more than a decade Fast parser Generates logical forms Used in ATIS and CommandTalk SNARK: Large, efficient theorem prover Under development for more than a decade Built-in temporal and spatial reasoners Procedural attachment, incl for web resources Extracts answers from proofs Strategic controls for speed-up

8. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International8 Linguistic Variation How far is Mascat from Kandahar? How far is it from Mascat to Kandahar? How far is it from Kandahar to Mascat? How far is it betweeen Mascat and Kandahar? What is the distance from Mascat to Kandahar? What is the distance between Mascat and Kandahar? GEMINI parses and produces logical forms for most TREC-type queries Use TACITUS and FASTUS lexicons to augment GEMINI lexicon Unknown word guessing based on "morphology" and immediate context

9. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International9 "Snarkification" Problem: GEMINI produces logical forms not completely aligned with what SNARK theories need Current solution: Write simplification code to map from one to the other Long-term solution: Logical forms that are aligned better

10. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International10 Relating Lexical Predicates to Core Theory Predicates "... distance..." "how far..." distance-between Need to write these axioms for every domain we deal with Have illustrative examples

11. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International11 Decomposition of Questions lat-long(l 1,x) & lat-long(l 2,y) & lat-long-distance(d,l 1,l 2 ) --> distance-between(d,x,y) Need axioms relating core theory predicates and predicates from available resources Have illustrative examples

12. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International12 Procedural Attachment Declaration for certain predicates: There is a procedure for proving it Which arguments are required before called lat-long(l 1,x) lat-long-distance(d,l 1,l 2 ) When predicate with those arguments bound is generated in proof, procedure is exectuted.

13. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International13 Open Agent Architecture OAA Agent GEMINI snarkify SNARK Resources via OAA Agents

14. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International14 Use of SMART + TextPro Question Subquestion-1 Other Resources Question Decomposition via Logical Rules Resources Attached to Reasoning Process Subquestion-2 Subquestion-3 SMART + TextPro One Resource Among Many

15. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International15 Information Extraction Engine as a Resource SMART: Document retrieval for pre-processing TextPro: Top of the line information extraction engine Analyze NL query w GEMINI and SNARK Run TextPro over documents retrieved by SMART Retrieve best-match passage Use TextPro annotations or GEMINI analysis to extract answer from passage

16. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International16 Linking SNARK with TextPro TextSearch(EntType(?x), Terms(p), Terms(c), WSeq) & Analyze(WSeq, p(?x,c)) --> p(?x,c) Call to SMART+TextPro Type of questioned constituent Synonyms and hypernyms of word associated with p or c Answer: Ordered sequence of strings of words Match pieces of answer strings with pieces of query Subquery generated by SNARK during analysis of query

17. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International17 Information Extraction Engine as a Resource SMART: Document retrieval for pre-processing TextPro: Top of the line information extraction engine Analyze NL query w GEMINI and SNARK Run TextPro over documents retrieved by SMART TextPro returns relevant templates Agent turns templates into logic for SNARK to use in proof

18. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International18 Domain-Specific Patterns Decide upon domain (e.g., nonproliferation) Compile list of principal properties and relations of interest Implement these patterns in TextPro Implement link between TextPro and SNARK, converting between templates and logic

19. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International19 Temporal Reasoning: Structure Topology of Time: start, end, before, between Measures of Duration: for an hour,... Clock and Calendar: 3:45pm, Wednesday, June 12 Temporal Aggregates: every other Wednesday Deictic Time: last year,...

20. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International20 Temporal Reasoning: Goals Develop temporal ontology (DAML) Reason about time in SNARK (AQUAINT, DAML) Link with Temporal Annotation Standards (AQUAINT) Answer questions with temporal component (AQUAINT) Nearly complete In progress

21. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International21 Spatial and Geographical Reasoning: Structure Topology of Space: Is Albania a part of Europe? Dimensionality Measures: How large is North Korea? Orientation and Shape: What direction is Monterey from SF? Latitude and Longitude: Alexandrian Digital Library Gazetteer Political Divisions: CIA World Fact Book,...

22. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International22 Spatial and Geographical Reasoning: Goals Develop spatial and geographical ontology (DAML) Reason about space and geography in SNARK (AQUAINT, DAML) Attach spatial and geographical resources (AQUAINT) Answer questions with spatial component (AQUAINT) Some capability now

23. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International23 Dialog Modeling Key Idea: System matches user's utterance with one of several active tasks. Understanding dialog is one active task. Rules of form: property(situation) --> active(Task 1 ) including utter(u,w) --> active(DialogTask) want(u,Task 1 ) --> active(Task 1 ) Understanding is matching utterance (conjunction of predications) with an active task or the condition of an inactive task.

24. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International24 Dialog Task Model understand(a,e,t): hear(a,w) & parse(w,e) & match(e,t) yes Action determined by utterance and task no -- x unmatched Ask about x

25. 05/15/02Principal Investigator: Jerry R. Hobbs, SRI International25 Fixed-Domain QA Evaluation Pick a domain, e.g., nonproliferation Pick a set of resources, including a corpus of texts, structured databases, web services Have expert make up 200+ realistic questions, answerable with resources + inference Divide questions into training and test sets Give sites one month+ to work on training set Test on test set