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From Question-Answering to Information-Seeking Dialogs

From Question-Answering to Information-Seeking Dialogs. Chris Culy SRI International Menlo Park, CA. Jerry R. Hobbs USC/ISI Marina del Rey, CA. with Douglas Appelt, David Israel, Peter Jarvis, David Martin, Mark Stickel, and Richard Waldinger of SRI. Decomposing Questions.

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From Question-Answering to Information-Seeking Dialogs

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  1. From Question-Answeringto Information-Seeking Dialogs Chris Culy SRI International Menlo Park, CA Jerry R. Hobbs USC/ISI Marina del Rey, CA with Douglas Appelt, David Israel, Peter Jarvis, David Martin, Mark Stickel, and Richard Waldinger of SRI

  2. Decomposing Questions Could Mohammed Atta have met with an Iraqi official between 1998 and 2001? Logical Form Question Decomposition via Logical Rules meet(a,b,t) & 1998  t 2001 SNARK at(a,x1,t) & at(b,x2,t) & near(x1,x2) & official(b,Iraq) go(a,x1,t) go(b,x2,t) Temporal Reasoning IE Engine IE Engine Geographical Reasoning Resource Attached to Reasoning Process Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  3. The Problem Inference in large knowledge bases is required for competent question-answering Many rich but heterogeneous knowledge bases exist today How do we make use of them in a single system? Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  4. Outline Three Resources: The Semantic Web: Teknowledge’s search engine ASCS An Information Extraction Engine: SRI’s TextPro 3. An Ontology of Time: DAML-Time Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  5. DAML Search Engine Tecknowledge has developed ASCS: pred: capital namespace Searches entire (soon to be exponentially growing) Semantic Web arg1: ?x namespace arg2: Indonesia namespace Also conjunctive queries: population of capital of Indonesia Problem: you have to know logic and RDF to use it. Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  6. DAML Search Engineas AQUAINT Web Resource AQUAINT System capital(?x,Indonesia) procedural attachment in SNARK pred: capital namespace Searches entire (soon to be exponentially growing) Semantic Web arg1: ?x namespace arg2: Indonesia namespace Solution: You only have to know English to use it; Makes the entire Semantic Web accessible to AQUAINT users. Also: Can use it for subqueries. Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  7. Namespace Problem Where to find the right predicates? In QUARK: Subtheories linking predicates to namespaces Subtheories linking topics to namespaces In DAML/ASCS: EQUIVALENT statements Standardized ontologies Use WordNet and SUMO to expand query Any namespace Decreasing precision Decreasing precision Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  8. Information ExtractionEngine as a Resource Document retrieval for pre-processing TextPro: Top of the line information extraction engine recognizes subject-verb-object, coref rels Analyze NL query w GEMINI and SNARK Bottom out in a pattern for TextPro to seek Keyword search on very large corpus TextPro runs over documents retrieved Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  9. Linking SNARK with TextPro Type of questioned constituent Synonyms and hypernyms of word associated with p or c Call to TextPro TextSearch(EntType(?x), Terms(p), Terms(c), WSeq) & Analyze(WSeq, p(?x,c)) --> p(?x,c) Answer: Ordered sequence of annotated strings of words Match pieces of annotated answer strings with pieces of query Subquery generated by SNARK during analysis of query Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  10. Three Modes of Operationfor TextPro Search for predefined patterns and relations (ACE-style) and translate relations into SNARK's logic Where does the CEO of IBM live? Search for subject-verb-object relations in processed text that matches predicate-argument structure of SNARK's logical expression "Samuel Palmisano is CEO of IBM." Search for passage with highest density of relevant words and entity of right type for answer "Samuel Palmisano .... CEO .... IBM." Use coreference links to get most informative answer ACE Role and AT Relations Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  11. First Mode TextSearch(Person, Terms(CEO), Terms(IBM), WSeq) & Analyze(WSeq, Role(?x,Management,IBM,CEO)) --> CEO(?x,IBM) Entity1: {Samuel Palmisano, Palmisano, head, he} Entity2: {IBM, International Business Machines, they} Relation: Role(Entity1,Entity2, Management,CEO) <relation TYPE=Role SUBTYPE=Management> <rel_entity_arg ID=“Entity1” ARGNUM=“1”/> <rel_entity_arg ID=“Entity2” ARGNUM=“2”/> <rel_attribute ATTR=“POSITION”>CEO</rel_attribute> </relation> Analyze CEO(Samuel Palmisano,IBM) Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  12. Three Modes of Operationfor TextPro Search for predefined patterns (MUC-style) and translate template into SNARK's logic Where does the CEO of IBM live? Search for subject-verb-object relations in processed text that matches predicate-argument structure of SNARK's logical expression "Samuel Palmisano heads IBM." Search for passage with highest density of relevant words and entity of right type for answer "Samuel Palmisano .... CEO .... IBM." Use coreference links to get most informative answer Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  13. Second Mode TextSearch(Person, Terms(CEO), Terms(IBM), WSeq) & Analyze(WSeq, CEO(?x,IBM)) --> CEO(?x,IBM) "<subj> Samuel Palmisano </subj> <verb> heads </verb> <obj> IBM </obj>" Analyze CEO(Samuel Palmisano,IBM) Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  14. Three Modes of Operationfor TextPro Search for predefined patterns (MUC-style) and translate template into SNARK's logic Where does the CEO of IBM live? Search for subject-verb-object relations in processed text that matches predicate-argument structure of SNARK's logical expression "Samuel Palmisano is CEO of IBM." Search for passage with highest density of relevant words and entity of right type for answer "Samuel Palmisano .... CEO .... IBM." Use coreference links to get most informative answer Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  15. Third Mode TextSearch(Person, Terms(CEO), Terms(IBM), WSeq) & Analyze(WSeq, CEO(?x,IBM)) --> CEO(?x,IBM) "<person> Samuel Palmisano </person> ...." coref "<person> He </person>has recently been rumored to have been appointed Lou Gerstner's successor as <CEOword> CEO </CEOword> of the major computer maker nicknamed <co> Big Blue </co>" Analyze CEO(Samuel Palmisano,IBM) Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  16. Challenges for IE Cross-document identification of individuals Document 1: Osama bin Laden Document 2: bin Laden Document 3: Usama bin Laden Do entities with the same or similar names represent the same individual? Metonymy Text: Beijing approved the UN resolution on Iraq. Query involves “China”, not “Beijing” Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  17. 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, ... Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  18. Temporal Reasoning: Goals Develop temporal ontology (DAML) Reason about time in SNARK (AQUAINT, DAML) Link with Temporal Annotation Language TimeML (AQUAINT) Answer questions with temporal component (AQUAINT) Nearly complete In progress Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  19. Convergence DAML Annotation of Temporal Information on Web (DAML-Time) Annotation of Temporal Information in Text (TimeML) Most information on Web is in text The two annotation schemes should be intertranslatable Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  20. TimeML Annotation Scheme(An Abstract View) clock & calendar intervals & instants 2001 inclusion intervals 6 mos Sept 11 durations before warning instantaneous events Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  21. TimeML Example The top commander of a Cambodian resistance force said Thursday he hassenta team to recover the remains of a British mine removal expert kidnapped and presumed killed by Khmer Rouge guerrillas two years ago. resist command recover sent Thursday said now 2 years presumed remove kidnap killed remain Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  22. Vision for Time Manual DAML temporal annotation of web resources Manual temporal annotation of large NL corpus Programs for automatic temporal annotation of NL text Automatic DAML temporal annotation of web resources Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  23. Spatial and GeographicalReasoning: Structure Topology of Space: Is Albania a part of Europe? Dimensionality: How long/big is Chile? 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, ... Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  24. Spatial and GeographicalReasoning: 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 Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

  25. Status and Future Directions Basic architecture essentially complete Good sampling of web and other resources have been incorporated Focus on bulking up knowledge base relevant to domain (nonproliferation) Focus on dialogue structure Chris Culy, SRI, and Jerry Hobbs, USC/ISI, PIs

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