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

Questions are Problematic for Web Search What was the name of president Fillmore’s cat? Who invented crocs? … Agichtein et al., WI 2007

Web search: What was the name of president Fillmore’s cat? Agichtein et al., WI 2007

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 Agichtein et al., WI 2007

… and millions more of other tables and lists … Agichtein et al., WI 2007

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? Agichtein et al., WI 2007

Related Work Web Question Answering Web-scale Information Extraction 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) Agichtein et al., WI 2007

Our System TQA: Overview Index all promising HTML tables Translate a question into select/project query Select table rows, project candidate answers Rank candidate answers Return top K answers Agichtein et al., WI 2007

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 Agichtein et al., WI 2007

Table Metadata Combines information about the source document, and table context Agichtein et al., WI 2007

TQA Question Processing Agichtein et al., WI 2007

Table QA: Querying Overview Agichtein et al., WI 2007

Features for Ranking Candidate Answers Explain: Frequency, freq decayed, idf, type sim, page rank , column distance, tightness, overlap Agichtein et al., WI 2007

Ranking Answer Candidates Frequency-based (AskMSR): Heuristic weight assignment (AskMSR improved) Neither is robust or general Add animation – show one at a time Agichtein et al., WI 2007

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 Agichtein et al., WI 2007

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 Agichtein et al., WI 2007

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) Agichtein et al., WI 2007

Evaluation Metrics MRR (mean reciprocal rank): Recall @ K: 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. Agichtein et al., WI 2007

Results (1): Accuracy vs. Corpus Size Agichtein et al., WI 2007

Results (2): Comparing Ranking Methods If output consumed by another system, large K ok Agichtein et al., WI 2007

Results (3): Accuracy on Hard Questions TQA can retrieve answer in top 100 when best QA system not able to return any answer Agichtein et al., WI 2007

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 Agichtein et al., WI 2007

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 Agichtein et al., WI 2007

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 Agichtein et al., WI 2007

Thank you! Questions? E-mail: eugene@mathcs.emory.edu Plug: 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 Agichtein et al., WI 2007