aquaint kickoff meeting advanced techniques for answer extraction and formulation language computer...
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AQUAINT Kickoff Meeting
Advanced Techniques for Answer Extraction and Formulation
Language Computer Corporationwww.languagecomputer.com
Dallas, Texas
PI: Dan [email protected]
AQUAINT Kickoff Meeting - AnsQA
Advanced Techniques for Answer Extraction and Formulation
People Dan Moldovan, PI Sanda Harabagiu, Co-PI Mihai Surdeanu Marius Pasca John Lehmann Vasile Rus Earl Hood
AQUAINT Kickoff Meeting - AnsQA
Tasks
Task 1. QA System Taxonomy Task 2. Answer fusion Task 3. Develop methods for on-line ontology
construction Task 4. Develop an inference engine capable of
providing answer justification Task 5. Formulate concise and coherent
answers Task 6. Explore new QA System Architectures.
AQUAINT Kickoff Meeting - AnsQA
T1: QA System Taxonomy
Need for a taxonomy Goal: Develop elaborate taxonomies for QA
systems and applications Approach:
Develop QA System theoretical models Study tradeoffs and theoretical limits
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T1: QA System Taxonomy
Model QA system performance as a function of: Question space: scope, context, judgement Answer space: multiple-sources, fusion,
interpretations Document space: type, indexing System architecture: modules, feedbacks Computing power: processing time, computer
parameters Resources used: dictionaries, knowledge bases,
knowledge acquisition, parsers, theorem provers
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T2: Answer Fusion
Goal: Develop methods to handle questions whose answers spread across several documents.
Approach: Map the original question into simpler queries Collect answers to these simple queries Ensemble an answer by fusing partial answers.
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T2: Answer Fusion
Study answer fusion at various levels of complexity Questions asking simple facts
What countries import sugar from Cuba? Questions that require on-line ontology development
What software products does Microsoft sell? What causes asthma? What are the effects of alcohol on the brain?
Speculative questions about future events Does the Fed cut the interest rate at their next meeting?
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T2: Answer Fusion
Use IE and Text Mining to map semantic relations into lexico-syntactic patterns that in turn help to develop ontologies.
Q: What causes asthma?
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T3: On-line Ontology Development
Goal: Develop automatically ontological structures that help answer some complex questions
Approach: Use knowledge acquisition from text methods to extract and classify concepts and relations relevant to question keywords
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T3: On-line Ontology Development
Example: What software products does Microsoft sell?
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T4: Answer Justification
Goal: Develop an inference engine capable of justifying an answer via a logical proof
Approach: Transform questions and document paragraphs into logical
representations Use world knowledge axioms extracted from WordNet glosses Construct lexical chains between query concepts and candidate
answer sentence concepts Apply unification on lexical chains.
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T4: Answer Justification
Logic proof for P2 succeeds and agent of shooting Sheriff_Pat_Garret unifies with PERSON
e1 = e1’x1 = x2’x2 = x1’
P1: The scene called for Phillips’ character to be saved from a lynching when Billy the Kid (Emilio Estevez) shot the rope in half just as he was about to be hanged.
P2: In 1881, outlaw William H. Bonney Jr., alias Billy the Kid, was shot and killed by Sheriff Pat Garrett in Fort Summer, N.M.
Example Q481: Who shot Billy the Kid?
LFT:
)(__&),,(&)(__:2
)(&),,(&)(__:1
)(__&),,(&)(:
21211
22111
22111
lllll
lllll
xGarretPatSheriffxxeshootxKidtheBillyP
xropexxeshootxKidtheBillyP
xKidtheBillyxxeshootxPERSONQ
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T4: Answer Justification
WordNet axioms:
Columbian: of Columbiamurder: kill intentionally with premeditationkill: cause to die
A: “Several gunmen on a highway leading to the Columbian city of Ibaque murdered Colombian ambassador to Honduras Lucelly Garcia today.”
Example Q045: When did Lucelly Garcia, former ambassador of Columbia to Honduras die?
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T5: Answer formulation
Goal: Develop methods to formulate concise and coherent answers
Approach: Answer formulation receives inputs from answer
extraction, dialogue, and other modules. Language generation operators piece together the
information provided by the answer elements.
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T5: Answer formulation
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T5: Answer formulation
Example: How do trade liberalization and foreign aid affect international migration?
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T6: Explore new QA System Architectures
Goal: Study innovative QA system architectures that are high performance, modular, and tunable.
Approach: Study via analytical modeling, simulations and implementation the architectural implications of features such as: High recall High precision Fast response time Portability Large volume and diversity of documents