2. Is Question Answering an Acquired Skill? Soumen Chakrabarti IIT Bombay With Ganesh Ramakrishnan Deepa Paranjpe Pushpak Bhattacharyya

3. QAChakrabarti The query-response gap  Language models for Web corpus and Web queries radically different (Church, 2003—4)  Not surprising, because Users are conditioned to drop verbs, prepositions and articles (anything interesting) Queries inherently seek to express a “missing piece”, documents don’t  IR vs. DB DB queries clearly indicate what’s given and what’s missing in a query IR systems do not (yet)

4. QAChakrabarti Web search and QA  Information need – words relating “things” + “thing” aliases = telegraphic Web queries Cheapest laptop with wireless  best price laptop 802.11 Why is the sky blue?  sky blue because When was the Space Needle built?  “Space Needle” history  People used to ask telegraphic queries Fix keywords you are sure of Guess document features that will answer the missing piece in your query

5. QAChakrabarti Factoid QA  Specialize given domain to a token related to ground constants in the query What animal is Winnie the Pooh? hyponym(“animal”) NEAR “Winnie the Pooh” When was television invented? instance-of(“time”) NEAR “television” NEAR synonym(“invented”)  FIND x “NEAR” GroundConstants(question) WHERE x IS-A Atype(question) Ground constants: Winnie the Pooh, television Atypes: animal, time

6. QAChakrabarti A relational view of QA  Entity class or atype may be expressed by A finite IS-A hierarchy (e.g. WordNet, TAP) A surface pattern matching infinitely many strings (e.g. “digit+”, “Xx+”, “preceded by a preposition”)  Match selectors, specialize atype to answer tokens QuestionAtype clues Selectors Answer passage Question words “Answer zone” Direct syntactic match Entity class IS-A Limit search to certain rows Locate which column to read “Answer zone” Attribute or column name

7. QAChakrabarti But who provides is-a info?  Compiled knowledge bases (WordNet, CYC)  Automatic “soft” compilations Google sets KnowItAll BioText  Basic tricks Do jordan and basketball cooccur more often than you’d expect? Small phrase probes like “actor Willis”

8. QAChakrabarti Benefits of the relational view  “Scaling up by dumbing down” Next stop after vector-space Far short of real knowledge representation and inference Barely getting practical at (near) Web scale  Can set up as a learning problem: train with questions (query logs) and answers in context  Transparent, self-tuning, easy to deploy Feature extractors used in entity taggers Relational/graphical learning on features

9. QAChakrabarti Broad strategy  Learn soft patterns of correlation between question features and answer context  Use models to index corpus with atype annotations  Given query, assign a soft reward to all atype patterns  Search efficiently for passages containing promising tokens  Score passages and report best token sequences

10. QAChakrabarti What TREC QA feels like  How to assemble chunker, parser, POS and NE tagger, WordNet, WSD, … into a QA system?  Experts get much insight from old QA pairs Matching an upper-cased term adds a 60% bonus … for multi-words terms and 30% for single words Matching a WordNet synonym … discounts by 10% (lower case) and 50% (upper case) Lower-case term matches after Porter stemming are discounted 30%; upper-case matches 70%

11. QAChakrabarti Talk outline  Relational interpretation of QA  Motivation for a “clean-room” IE+ML system  Learning to map between questions and answers using is-a hierarchies and IE-style surface patterns Can handle prominent finite set of atypes: person, place, time, measurements,…  Extending to arbitrary atype specializations Required for what… and which… questions  Ongoing work and concluding remarks

12. QAChakrabarti Feature + Soft match  FIND x “NEAR” GroundConstants(question) WHERE x IS-A Atype(question)  No fixed question or answer type system  Convert “x IS-A Atype(question)” to a soft match “DoesAtypeMatch(x, question) QuestionAnswer tokens Passage IE-style surface feature extractors WordNet hypernym feature extractors IE-style surface feature extractors Question feature vector Snippet feature vector Learn joint distrib.

13. QAChakrabarti Feature extraction: Intuition howwho fastmanyfarrich wrotefirst How fast can a cheetah run? A cheetah can chase its prey at up to 90 km/h How fast does light travel? Nothing moves faster than 186,000 miles per hour, the speed of light rate#n#2 abstraction#n#6 NNS rate#n#2 magnitude_relation#n#1 mile#n#3 linear_unit#n#1 measure#n#3 definite_quantity#n#1 paper_money#n#1 currency#n#1 writer, composer, artist, musician NNP, person explorer

14. QAChakrabarti Feature extractors  Question features: 1, 2, 3-token sequences starting with standard wh-words  Passage surface features: hasCap, hasXx, isAbbrev, hasDigit, isAllDigit, lpos, rpos,…  Passage WordNet features: all noun hypernym ancestors of all senses of token  Get top 300 passages from IR engine  For each token invoke feature extractors  Label = 1 if token is in answer span, 0 o/w  Question vector x q, passage vector x p

15. QAChakrabarti Preliminary likelihood ratio tests Surface patternsWordNet hypernyms

16. QAChakrabarti Joint feature-vector design  Obvious “linear” juxtaposition x=(x p,x q ) Does not expose pairwise dependencies  “Quadratic” form x = x q  x p All pairwise product of elements  Model has param for every pair  Can discount for redundancy in pair info  If x q (x p ) is fixed, what x p (x q ) will yield the largest Pr(Y=1|x)? (linear iceberg query) how_far when what_city region#n#3 entity#n#1

17. QAChakrabarti Classification accuracy  Pairing more accurate than linear model  Steep learning curve; linear never “gets it” beyond “prior” atypes like proper nouns (common in TREC)  Are the estimated w parameters meaningful?

18. QAChakrabarti Parameter anecdotes  Surface and WordNet features complement each other  General concepts get negative params: use in predictive annotation  Learning is symmetric (Q  A)

19. QAChakrabarti Query-driven information extraction  “Basis” of atypes A, a  A could be a synset, a surface pattern, feature of a parse tree  Question q “projected” to vector (w a : a  A) in atype space via learning conditional model  E.g. if q is “when…” or “how long…” w hasDigit and w time_period#n#1 are large, w region#n#1 is small  Each corpus token t has associated indicator features  a (t ) for every a  E.g.  hasDigit (3,000) =  is-a(region#n#1) (Japan) = 1  Can also learn [0,1] value of is-a proximity

20. QAChakrabarti Single token scoring  A token t is a candidate answer if  H q (t ): Reward tokens appearing “near” selectors matched from question 0/1: appears within fixed window with selector/s Activation in linear token sequence model Proximity in chunk sequences, parse trees,…  Order tokens by decreasing Atype indicator features of the token Projection of question to “atype space” …the armadillo, found in Texas, is covered with strong horny plates

21. QAChakrabarti Mean reciprocal rank (MRR)  n q = smallest rank among answer passages  MRR = (1/|Q|)  q  Q (1/n q ) Dropping passage from #1 to #2 as bad as dropping it from #2 to   TREC requires MRR5: round up n q >5 to  Improving rank from 20 to 6 as useless as improving it from 20 to 15  Aggregate score influenced by many complex subsystems Complete description rarely available

22. QAChakrabarti Effect of eliminating non-answers  300 top IR score hits  If Pr(Y=1|token) < threshold reject token  All tokens rejected then reject passage  Present survivors in IR order

23. QAChakrabarti Drill-down and ablation studies  Scale average MRR improvement to 1 What, Which < average Who  average  Atype of what… and which… not captured well by 3-grams starting at wh-words  Atype ranges over essentially infinite set with relatively little training data

24. QAChakrabarti Talk outline  Relational interpretation of QA  Motivation for a “clean-room” IE+ML system  Learning to map between questions and answers using is-a hierarchies and IE-style surface patterns Can handle prominent finite set of atypes: person, place, time, measurements,…  Extending to arbitrary atype specializations Required for what… and which… questions  Ongoing work and concluding remarks

25. QAChakrabarti What…, which…, name… atype clues  Assumption: Question sentence has a wh- word and a main/auxiliary verb  Observation: Atype clues are embedded in a noun phrase (NP) adjoining the main or auxiliary verb  Heuristic: Atype clue = head of this NP Use a shallow parser and apply rule  Head can have attributes Which (American (general)) is buried in Salzburg? Name (Saturn’s (largest (moon)))

26. QAChakrabarti Atype clue extraction stats  Simple heuristic quite effective  If successful, extracted atype is mapped to WordNet synset (moon  celestial body etc.)  If no atype of this form available, try the “self- evident” atypes (who, when, where, how_X etc.)  New boolean feature for candidate token: is token hyponym of atype synset?

27. QAChakrabarti The last piece: Learning selectors  Which question words are likely to appear (almost) unchanged in an answer passage? Constants in select-clauses of SQL queries Guides backoff policy for keyword query  Arises in Web search sessions too Opera login fails Opera problem with login Opera login accept password Opera account authentication …

28. QAChakrabarti Features for identifying selectors  Local and global features POS of word, POS of adjacent words, case info, proximity to wh-word Suppose word is associated with synset set S NumSense: size of S (how polysemous is the word?) NumLemma: average #lemmas describing s  S  Model as a sequential learning problem Each token has local context and global features POS@0POS@1POS@-1

29. QAChakrabarti Selector results  Global features (IDF, NumSense, NumLemma) essential for accuracy Best F1 accuracy with local features alone: 71—73% With local and global features: 81%  Decision trees better than logistic regression F1=81% as against LR F1=75% Intuitive decision branches But logistic regression gives scores for query backoff

30. QAChakrabarti Putting together a QA system QA System Wordnet POS Tagger Training Corpus Shallow parser Learning tools N-E Tagger

31. QAChakrabarti Question Passage Index Corpus Sentence splitter Passage indexer Candidate passage Keyword query Keyword query generator Shallow Parser Noun and verb markers Atype Extractor Atype clues Learning to rerank passages Sample features: Do selectors match? How many? Is some non-selector passage token a specialization of the question’s atype clue? Min, avg, linear token distance between candidate token and matched selectors Learning to rerank passages Sample features: Do selectors match? How many? Is some non-selector passage token a specialization of the question’s atype clue? Min, avg, linear token distance between candidate token and matched selectors Logistic Regression Reranked passages Putting together a QA system Tokenizer POS Tagger Tagged question Tokenizer POS Tagger Entity Extractor Tagged passage Selector Learner Is QA pair?

32. QAChakrabarti Learning to re-rank passages  Remove passage tokens matching selectors User already knows these are in passage  Find passage token/s specializing atype  For each candidate token collect Atype of question, original rank of passage Min, avg linear distances to matched selectors POS and entity tag of token if available Ushuaia, a port of about 30,000 dwellers set between the Beagle Channel and … How many inhabitants live in the town of Ushuaia selector match Surface pattern hasDigits WordNet match 5 tokens apart1

33. QAChakrabarti Re-ranking results  Categorical and numeric attributes  Logistic regression  Good precision, poor recall  Use logit score to re-rank passages  Rank of first correct passage shifts substantially

34. QAChakrabarti MRR gains from what, which, name  Substantial gain in MRR  What/which now show above-average MRR gains  TREC 2000 top MRRs: 0.76 0.71 0.46 0.46 0.31

35. QAChakrabarti Generalization across corpora  Across-year numbers close to train/test split on a single year  Features and model seem to capture corpus- independent linguistic Q+A artifacts

36. QAChakrabarti Conclusion  Clean-room QA= feature extraction+learning Recover structure info from question Learn correlations between question structure and passage features  Competitive accuracy with negligible domain expertise or manual intervention  Ongoing work Use model coefficients for predictive annotation Combine token scores to better passage scores Treat all question types uniformly Use redundancy available from the Web