Question Answering over Implicitly Structured Web Content
Eugene Agichtein* Emory University
Chris Burges Microsoft Research
Eric Brill Microsoft Research
* Research done while at Microsoft Research
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What was the name of president Fillmore’s cat?
Who invented crocs? …
Questions are Problematic for Web Search
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Web search: What was the name of
president Fillmore’s cat?
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Web Question Answering
Why are questions problematic for web search engines?
Search engines treat questions as keyword queries, ignoring the semantic relationships between words, and the explicitly stated information need
Poor performance for long (> 5 terms) queries
Problem exacerbated when common keywords are included
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… and millions more of other tables and lists …
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Implicitly Structured Web Content HTML Tables, Lists
Product descriptions Example: Lists of favorite things, “top 10” lists, etc.
HTML Syntax (sometimes) reflects semantics Authors imply semantic relationships, entity types by grouping Can infer information about ambiguous entities from others in the
same column
Millions of HTML tables, lists on the “surface” web alone No common schema Keyword queries: primary access method. How to exploit this structured content for good (e.g., for Question
Answering) at web scale?
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Related Work Web Question Answering
AskMSR (TREC 2001) Aranea (TREC 2003) Mulder (WWW 2001) A No-Frills Architecture for Lightweight Answer Retrieval (WWW 2007)
Web-scale Information Extraction QXtract (ICDE 2003): learn keyword queries to retrieve content KnowItAll (WWW 2004): minimal supervision, larger scale TextRunner (IJCAI 2007): single pass scan, disambiguate at query time Towards Domain-Independent Information Extraction from Web Tables
(WWW 2007)
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Our System TQA: Overview
1. Index all promising HTML tables
2. Translate a question into select/project query
3. Select table rows, project candidate answers
4. Rank candidate answers
5. Return top K answers
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TableQA: Indexing Crawl the Web Identify “promising”
tables (heuristic, could be improved)
Extract metadata for each table Context Document content Document metadata
Index extracted metadata
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Table Metadata
Combines information about the source document, and table context
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TQA Question Processing
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Table QA: Querying Overview
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Features for Ranking Candidate Answers
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Ranking Answer Candidates Frequency-based (AskMSR):
Heuristic weight assignment (AskMSR improved)
Neither is robust or general
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Ranking Answer Candidates (cont) Solution: machine learning-based ranking
Naïve Bayes:
Score(answer) =
RankNet (Burges et al. 2005): scalable Neural Net implementation: Optimized for ranking – predicting an ordering of items,
not scores for each Trains on pairs (where first point is to be ranked higher
or equal to second) Uses cross entropy cost and gradient descent to set
weights
).|( ii
Fanswerrelevantp
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Some Implementation Details
Lucene, distributed indices (20M tables per index)
NLP Tools: MS internal Named Entity tagger (many free ones exist) Porter Stemmer
Relatively light-weight architecture: Client (question processing): desktop machine Table index server: dual-processor, 8 Gb RAM, WinNT
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Experimental Setup
Queries: TREC QA 2002, 2003 questions
Corpus: 100M web pages (a “random” subset of an MSN Search crawl, from 2005)
Evaluation: TREC QA factoid patterns “Minimal” regular expressions to match only right
answers Not comprehensive (based on judgement pool)
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Evaluation Metrics
MRR (mean reciprocal rank): MRR @ K = , averaged over all
questions
Recall @ K: The fraction of the questions for which a system
returned a correct answer ranked at or above K.
Ki ianswerrel..1 )(
1
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Results (1): Accuracy vs. Corpus Size
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Results (2): Comparing Ranking Methods
If output consumed by another system, large K ok
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Results (3): Accuracy on Hard Questions
TQA can retrieve answer in top 100 when best QA system not able to return any answer
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Result Summary
Requires indexing more than 150M tables before respectable accuracy achieved
Performance was around median on TREC 2002, 2003 benchmarks
Can be helpful for questions difficult for traditional QA systems
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Promising Directions for Future Work
Craw-time: aggressive pruning/classification Index-time: Integration of related tables Query-time: taxonomies integration/hypernimy
User behavior modeling Past clickthrough to rerank candidate tables,
answers Query reformulation
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Conclusions
Implicitly structured web content can be useful for web question answering
We demonstrated scalability of a lightweight table-based web QA approach
Much room for improvement, future research
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Thank you!
Questions?
E-mail: [email protected]: User Interactions for Web Question Answering:
http://www.mathcs.emory.edu/~eugene/uqa/
E. Agichtein, E. Brill, S. Dumais, Mining user behavior to improve web search ranking, SIGIR 2006
E. Agichtein, User Behavior Mining and Information Extraction: Towards closing the gap, IEEE Data Engineering Bulletin, Dec. 2006
E. Agichtein, C. Castillo, D. Donato, A. Gionis, and G. Mishne, Finding High Quality Content in Social Media with applications to Community-based Question Answering, to appear WSDM 2008