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Question-Answering

Question-Answering via the Web: the AskMSR System Note: these viewgraphs were originally developed by Professor Nick Kushmerick, University College Dublin, Ireland. These copies are intended only for use for review in ICS 278. Question-Answering.

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Question-Answering

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  1. Question-Answering via the Web: the AskMSR SystemNote: these viewgraphs were originally developed by Professor Nick Kushmerick, University College Dublin, Ireland. These copies are intended only for use for review in ICS 278.

  2. Question-Answering • Users want answers, not documents Databases Information Retrieval Information Extraction Question Answering Intelligent Personal Electronic Librarian • Active research over the past few years, coordinated by US government “TREC” competitions • Recent intense interest from security services (“What is Bin Laden’s bank account number?”)

  3. Question-Answering on the Web • Web = a potentially enormous “data set” for data mining • e.g., >8 billion Web pages indexed by Google • Example: AskMSR Web question answering system • “answer mining” • Users pose relatively simple questions • E.g., “who killed Abraham Lincoln”? • Simple parsing used to reformulate as a “template answer” • Search engine results used to find answers (redundancy helps) • System is surprisingly accurate (on simple questions) • Key contributor to system success is massive data (rather than better algorithms) • References: • Dumais et al, 2002: Web question answering: is more always better? In Proceedings of SIGIR'02

  4. Lecture 5 AskMSR Adapted from: COMP-4016 ~ Computer Science Department ~ University College Dublin ~ www.cs.ucd.ie/staff/nick ~ © Nicholas Kushmerick 2002 • Web Question Answering: Is More Always Better? • Dumas, Bank, Brill, Lin, Ng (Microsoft, MIT, Berkeley) • Q: “Where isthe Louvrelocated?” • Want “Paris”or “France”or “75058Paris Cedex 01”or a map • Don’t justwant URLs

  5. “Traditional” approach (Straw man?) • Traditional deep natural-language processing approach • Full parse of documents and question • Rich knowledge of vocabulary, cause/effect, common sense, enables sophisticated semantic analysis • E.g., in principle this answers the “who killed Lincoln?” question: • The non-Canadian, non-Mexican president of a North American country whose initials are AL and who was killed by John Wilkes booth died ten revolutions of the earth around the sun after 1855.

  6. AskMSR: Shallow approach • Just ignore those documents, and look for ones like this instead:

  7. AskMSR: Details 2 1 3 5 4

  8. Step 1: Rewrite queries • Intuition: The user’s question is often syntactically quite close to sentences that contain the answer • Where istheLouvreMuseumlocated? • TheLouvreMuseumislocated in Paris • Who createdthecharacterofScrooge? • Charles DickenscreatedthecharacterofScrooge.

  9. Query rewriting • Classify question into seven categories • Who is/was/are/were…? • When is/did/will/are/were …? • Where is/are/were …? a. Category-specific transformation rules eg “For Where questions, move ‘is’ to all possible locations” “Where is the Louvre Museum located”  “is the Louvre Museum located”  “the is Louvre Museum located”  “the Louvre is Museum located”  “the Louvre Museum is located”  “the Louvre Museum located is” (Paper does not give full details!) b. Expected answer “Datatype” (eg, Date, Person, Location, …) When was the French Revolution?  DATE • Hand-crafted classification/rewrite/datatype rules(Could they be automatically learned?) Nonsense,but whocares? It’s only a fewmore queriesto Google.

  10. Query Rewriting - weights • One wrinkle: Some query rewrites are more reliable than others Where is the Louvre Museum located? Weight 5if we get a match, it’s probably right Weight 1 Lots of non-answerscould come back too +“the Louvre Museum is located” +Louvre +Museum +located

  11. Step 2: Query search engine • Throw all rewrites to a Web-wide search engine • Retrieve top N answers (100?) • For speed, rely just on search engine’s “snippets”, not the full text of the actual document

  12. Step 3: Mining N-Grams • Unigram, bigram, trigram, … N-gram:list of N adjacent terms in a sequence • Eg, “Web Question Answering: Is More Always Better” • Unigrams: Web, Question, Answering, Is, More, Always, Better • Bigrams: Web Question, Question Answering, Answering Is, Is More, More Always, Always Better • Trigrams: Web Question Answering, Question Answering Is, Answering Is More, Is More Always, More Always Betters

  13. Mining N-Grams • Simple: Enumerate all N-grams (N=1,2,3 say) in all retrieved snippets • Use hash table and other fancy footwork to make this efficient • Weight of an n-gram: occurrence count, each weighted by “reliability” (weight) of rewrite that fetched the document • Example: “Who created the character of Scrooge?” • Dickens - 117 • Christmas Carol - 78 • Charles Dickens - 75 • Disney - 72 • Carl Banks - 54 • A Christmas - 41 • Christmas Carol - 45 • Uncle - 31

  14. Step 4: Filtering N-Grams • Each question type is associated with one or more “data-type filters” = regular expression • When… • Where… • What … • Who … • Boost score of n-grams that do match regexp • Lower score of n-grams that don’t match regexp • Details omitted from paper…. Date Location Person

  15. Step 5: Tiling the Answers Scores 20 15 10 merged, discard old n-grams Charles Dickens Dickens Mr Charles Score 45 Mr Charles Dickens N-Grams N-Grams tile highest-scoring n-gram Repeat, until no more overlap

  16. Experiments • Used the TREC-9 standard query data set • Standard performance metric: MRR • Systems give “top 5 answers” • Score = 1/R, where R is rank of first right answer • 1: 1; 2: 0.5; 3: 0.33; 4: 0.25; 5: 0.2; 6+: 0

  17. Results [summary] • Standard TREC contest test-bed: ~1M documents; 900 questions • E.g., “who is president of Bolivia” • E.g., “what is the exchange rate between England and the US” • Technique doesn’t do too well (though would have placed in top 9 of ~30 participants!) • MRR = 0.262 (ie, right answered ranked about #4-#5) • Why? Because it relies on the enormity of the Web! • Using the Web as a whole, not just TREC’s 1M documents… MRR = 0.42 (ie, on average, right answer is ranked about #2-#3)

  18. Example • Question: what is the longest word in the English language? • Answer = pneumonoultramicroscopicsilicovolcanokoniosis (!) • Answered returned by AskMSR: • 1: “1909 letters long” • 2: the correct answer above • 3: “screeched” (longest 1-syllable word in English)

  19. Open Issues • In many scenarios (eg, monitoring Bin Laden’s email) we only have a small set of documents! • Works best/only for “Trivial Pursuit”-style fact-based questions • Limited/brittle repertoire of • question categories • answer data types/filters • query rewriting rules

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