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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 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.

question answering
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?”)
question answering on the web
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

askmsr

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
traditional approach straw man
“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.
askmsr shallow approach
AskMSR: Shallow approach
  • Just ignore those documents, and look for ones like this instead:
step 1 rewrite queries
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.
query rewriting
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.

query rewriting weights
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

step 2 query search engine
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
step 3 mining n grams
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
mining n grams
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
step 4 filtering n grams
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

step 5 tiling the answers
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

experiments
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
results summary
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)
example
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)
open issues
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