1. Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology 1 A semantic approach for question classification using WordNet and Wikipedia Presenter : Cheng-Hui Chen Authors : Santosh Kumar Ray, Shailendra Singh, B.P. Joshi PRL, 2010

2. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outlines Motivation Objectives Methodology Experiments Conclusions Comments

3. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation  Question classification module of a Question Answering System plays a very important role.  Web pages retrieved by these search engines do not provide precise information and may contain irrelevant information in even top ranked results.  Moldovan et al. (2003) showed that 36.4% of the errors were generated due to incorrect question classification. 3

4. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objectives  Proposed a question classification method that exploits the powerful semantic features of the WordNet and the vast knowledge repository of the Wikipedia to describe informative terms explicitly.  Provide answers of the user queries in succinct form. 4

5. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology  Question classification algorithm to classify questions using WordNet and Wikipedia.  Detail ─ Question database collection ─ Identification of question patterns ─ Question classification algorithm 5

6. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Question database collection  The question database consists of 5500 training and 500 test questions collected from english questions published by USC.  All questions of the dataset have been manually labeled by Li and Roth according to the coarse and fine grained categories 6 Coarse classFine classes ABBREVIATION ENTITYabbreviation, expression abbreviated animal, body, color, creative, currency, diseases and medical, event, food,instrument, lang, letter, other, plant,product,religion, sport, substance, symbol, technique, term, vehicle, word DESCRIPTION HUMAN LOCATION NUMERIC definition, description, manner, reason group, ind, title, description city, country, mountain, other, state code, count, date, distance, money, order, other, period, percent, speed, temp, size, weight

7. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Identification of question patterns Question typeCharacteristicExample Functional Word Question (1)All Non-Wh questions (except how) (2)Start with Non-significant verb phrases. I don't know the man. When Questions(1)start with ‘‘When” keyword and related to the year or day with month. (2)The general pattern is “When (do|does|did|AUX) NP VP X”. When did you write that book? Where Questions(1)start with ‘‘Where” keyword and are related to the location. Where is my dog? Which Questions(1)The general pattern is ‘‘Which NP X”? Which company manufactures video-game hardware? Who/Whose/Wh om Questions (1) These questions generally ask about an individual or an organization. Who is Mary? 7

8. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Identification of question patterns 8 Question typecharacteristicExample Why Questions(1)These questions ask for certain reasons or explanations. Why do heavier objects travel downhill faster? How Question(1)The general pattern is ‘‘How [do|does|did|AUX] NP VP X?” (1.1)Answer type is description of some process (2)How[big|fast|long|many|much|far|awa yerthigh|…] X?” (2.1) pattern returns some number as answer. (1)How do you know? (2)How long are you living in? What QuestionsIt can ask for virtually anythingWhat is your name?

9. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Question classification algorithm  If any of the question patterns matches with the given question, its entity type will be determined using algorithm QC (question classification). 9 Where is my dog? Location label I don't know the man. Delete do and return the man

10. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Question classification algorithm  Takes a string as an input and calls the Procedure online for determination of expected entity type. 10 The man Human, Vehicle

11. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Question classification algorithm  Input and uses online resources, Wikipedia and WordNet, to determine the type of expected entity. ─ It was observed that a typical article in Wikipedia starts like‘‘...X is a Y, Z,...” Y, Z etc. are synonyms, hypernyms, hyponyms or some semantically related term to X and these are considered to be possible entity types. If a sentence written in Wikipedia is ‘‘X is Y, Z,...”, the procedure online takes Y, Z,... as possible entity type of X. 11 Vehicle, Human, Location (TE1) Human, Indiviadual, Vehicle (TE2) Human, Vehicle (C)

12. Intelligent Database Systems Lab N.Y.U.S.T. I. M. An application of question classification: answer validation  The question‘‘In what year did Arundhati Roy receive a Booker Prize?” 1. Similarity computation ─ Similarity score The Question contains five tokens ‘‘a Number”,‘‘Arundhati Roy”, ‘‘received”, ‘‘Booker”, ‘‘Prize”.  If a candidate answer sentence when parsed contains two tokens out of these five tokens, it has similarity score of 0.4.  The expanded query ‘‘ In what year did (‘‘Arundhati Roy” or Arundhati) (Receive OR Get) Booker (Prize OR Award)?”.  The passage retrieval phase return top 10 answer sentences. Five answer sentences out of these 10 answer sentences got required similarity score. 12

13. Intelligent Database Systems Lab N.Y.U.S.T. I. M. An application of question classification : answer validation 2. Entity type The question classification module computes ‘‘date” as expected entity type for this question. It considering date to be a number (optionally with month name or word ‘‘year”), four candidate answer sentences containing some number were sent to the next stage for further processing. 13

14. Intelligent Database Systems Lab N.Y.U.S.T. I. M. An application of question classification : answer validation 3. World Wide Web validation Four candidates passed the first two tests. Three contained‘‘1997” as answer in them and the fourth returned ‘‘ ￡ 20,000”. Only the first answer (1997) was validated by topmost documents returned by Google. Hence, the three candidate answer sentences containing this answer were validated as correct answers. 14

15. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments (QC algorithm) 15

16. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments (Answer validation)  Sourse ─ TREC (Text REtrieval Conference) ─ WorldBook (The World Book) ─ Worldfactbook (CIA the world Factbook) ─ Other standard resources. 16

17. Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments (Answer validation) 17

18. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 18 Conclusions  Question classification algorithm with high accuracy.  The proposed method seems to be promising for question classification in the field of open- domain question answering.  The proposed method combines the World Wide Web with Natural Language Processing (NLP) techniques.

19. Intelligent Database Systems Lab N.Y.U.S.T. I. M. 19 Comments  Advantages ─ The distinctive points of the algorithm are lying in its dynamic and extendible properties. ─ Proposed method promising for question classification.  Shortages ─ It is having few limitations  Applications ─ Information retrieval