CS626/449 : Speech, NLP and the Web/Topics in AI Programming (Lecture 5: Wordnet; Application in Query Expansion)

1. CS626/449 : Speech, NLP and the Web/Topics in AI Programming(Lecture 5: Wordnet; Application in Query Expansion) Pushpak BhattacharyyaCSE Dept., IIT Bombay

2. Lexical Matrix

3. Wordnet - Lexical Matrix (with examples)

4. Psycholinguistic Theory Animal Bird canary • Human lexical memory for nouns as a hierarchy. • Can canary sing? - Pretty fast response. • Can canary fly? - Slower response. • Does canary have skin? – Slowest response. (can move, has skin) (can fly) (can sing) Wordnet - a lexical reference system based on psycholinguistic theories of human lexical memory.

5. Linked Wordnets in India Bengali Wordnet Dravidian Language Wordnets Sanskrit Wordnet Punjabi Wordnet Hindi Wordnet North East Language Wordnet Marathi Wordnet Konkani Wordnet English Wordnet

6. Semantic relations in wordnet • Synonymy • Hypernymy / Hyponymy • Antonymy • Meronymy / Holonymy • Gradation • Entailment • Troponymy 1, 3 and 5 are lexical (word to word), rest are semantic (synset to synset).

7. Synset: the foundation(house) 1. house -- (a dwelling that serves as living quarters for one or more families; "he has a house on Cape Cod"; "she felt she had to get out of the house")2. house -- (an official assembly having legislative powers; "the legislature has two houses")3. house -- (a building in which something is sheltered or located; "they had a large carriage house")4. family, household, house, home, menage -- (a social unit living together; "he moved his family to Virginia"; "It was a good Christian household"; "I waited until the whole house was asleep"; "the teacher asked how many people made up his home")5. theater, theatre, house -- (a building where theatrical performances or motion-picture shows can be presented; "the house was full")6. firm, house, business firm -- (members of a business organization that owns or operates one or more establishments; "he worked for a brokerage house")7. house -- (aristocratic family line; "the House of York")8. house -- (the members of a religious community living together)9. house -- (the audience gathered together in a theatre or cinema; "the house applauded"; "he counted the house")10. house -- (play in which children take the roles of father or mother or children and pretend to interact like adults; "the children were playing house")11. sign of the zodiac, star sign, sign, mansion, house, planetary house -- ((astrology) one of 12 equal areas into which the zodiac is divided)12. house -- (the management of a gambling house or casino; "the house gets a percentage of every bet")

8. Synset - DSF format (2/2) ID :: 121 CATEGORY :: NOUN CONCEPT :: अपने से छोटों के प्रति हृदय में उठनेवाला प्रेम EXAMPLE :: “चाचा नेहरू को बच्चों से बहुत ही स्नेह था” SYNSET :: स्नेह,नेह,लगाव,ममता

9. Creation of Synsets Three principles: • Minimality • Coverage • Replacability

10. Gloss and Example Crucially needed for concept explication, wordnet building using another wordnet and wordnet linking. {earthquake, quake, temblor, seism} -- (shaking and vibration at the surface of the earth resulting from underground movement along a fault plane of from volcanic activity)

11. Semantic Relations • Hypernymy and Hyponymy • Relation between word senses (synsets) • X is a hyponym of Y if X is a kind of Y • Hyponymy is transitive and asymmetrical • Hypernymy is inverse of Hyponymy (lion->animal->animate entity->entity)

12. Semantic Relations (continued) • Meronymy and Holonymy • Part-whole relation, branch is a part of tree • X is a meronymy of Y if X is a part of Y • Holonymy is the inverse relation of Meronymy {kitchen} ………………………. {house}

13. Lexical Relation • Antonymy • Oppositeness in meaning • Relation between word forms • Often determined by phonetics, word length etc. ({rise, ascend} vs. {fall, descend})

15. Gradation

16. WordNet Sub-Graph (English) Hyponymy Dwelling,abode Hypernymy Meronymy kitchen Hyponymy bckyard bedroom M e r o n y m y house,home veranda A place that serves as the living quarters of one or mor efamilies Hyponymy study guestroom hermitage cottage Gloss

17. चौपाया,पशु (chaupaayaa, pashu) Four-legged animal शाकाहारी (shaakaahaarii) herbivorous Hypernym पूँछ (puunchh ) Tail गाय, गऊ (gaaya ,gauu) Cow Attribute meronym सींगवाला एक शाकाहारी मादा चौपाया (siingwaalaa eka sakaahaarii maadaa choupaayaa) A horny, herbivorous, four-legged female animal) Gloss थन (thana) udder Hyponym Ability Verb पगुराना( paguraanaa) ruminate Antonym कामधेनु kaamadhenu A kind of cow मैनी गाय mainii gaaya A kind of cow बैल (baila) Ox WordNet Sub-Graph: Hindi

18. Wordnet Subgraph (Marathi) वनस्पती रान H Y P E R N Y M Y खोड HOLONYMY M E R O N Y M Y झाड,वृक्ष,तरू बाग मूळ GLOSS H Y P O N Y M Y मुळे,खोड,फांद्या,पाने इत्यादींनी युक्त असा वनस्पतिविशेष:"झाडे पर्यावरण शुद्ध करण्याचे काम करतात" लिंबू आंबा

19. Pan-India Dictionary Standard

20. Sanskrit Wordnet: a new effort- A column in the Concept based Multilingual dictionary

21. Query Expansion Acknowledgement: part of the slides borrowed from my Dual Degree Project Student Nishikant Dhanuka

22. Problem with Keywords • May not retrieve relevant documents that include synonymous terms • “restaurant” vs. “cafe” • “India” vs. “Bharat” • May retrieve irrelevant documents that include ambiguous terms • “bat” (baseball vs. mammal) • “Apple” (company vs. fruit) • “bit” (unit of data vs. act of eating)

23. Why Search Engines Fail to Search Relevant Documents • Users do not give sufficient number of keywords • Users do not give good keywords • Vocabulary gaps / Diversity & Vastness of web • Lack of domain knowledge • Solution: • Automatically generate new query which is better than initial query

24. Query Expansion Definition • adding more terms (keyword spices) to a user’s basic query Goal • to improve Precision and/or Recall Example • User Query: car • Expanded Query: car, cars, automobile, automobiles, auto, .. etc

25. Naïve Methods • Finding synonyms of query terms and searching for synonyms as well • Finding various morphological forms of words by stemming each word in the query • Fixing spelling errors and automatically searching for the corrected form • Re-weighting the terms in original query

26. Query Expansion Issues • Two major issues – • Which terms to include? • Which terms to weight more? • Concept based versus term based QE • Is it better to expand based upon the individual terms in the query, or the overall concept of the query

27. Objective To get proper set of words, which will improve Precision, when added to basic search query, without loosing the recall in considerable amount

28. Existing QE techniques • Global methods (static; of all documents in collection) • Query expansion • Thesauri (or WordNet) • Automatic thesaurus generation • Local methods (dynamic; analysis of documents in result set) • Relevance feedback • Pseudo relevance feedback

29. Global Analysis

30. Thesaurus based QE • For each term, t, in a query, expand the query with synonyms and related words of t from the thesaurus • feline → feline cat • May weight added terms less than original query terms. • Generally increases recall. • May significantly decrease precision, particularly with ambiguous terms. • “interest rate”  “interest rate fascinate evaluate” • There is a high cost of manually producing a thesaurus • And for updating it for scientific changes

31. Automatically Generated Thesauri • Attempt to generate a thesaurus automatically by analyzing the collection of documents • Two main approaches • Co-occurrence based (co-occurring words are more likely to be similar) • Shallow analysis of grammatical relations • Entities that are grown, cooked, eaten, and digested are more likely to be food items. • Co-occurrence based is more robust, grammatical relations are more accurate.

32. Example

33. Semantic Network/ Wordnet • To expand a query, find the word in the semantic network and follow the various arcs to other related words.

34. Global Methods: Summary • Pros • Thesauri and Semantic Networks (WordNet) can be used to find good words for users “more like this” • Cons • Little improvement has been found with automatic techniques to expand query without user intervention • Overall, not as useful as Relevance Feedback, may be as good as Pseudo Relevance Feedback

35. Local Analysis

36. Relevance Feedback • Relevance feedback: user feedback on relevance of docs in initial set of results • User issues a (short, simple) query • The user marks returned documents as relevant or non-relevant. • The system computes a better representation of the information need based on feedback. • Relevance feedback can go through one or more iterations.

37. Relevance Feedback Example: Initial Query and Top 8 Results • Query: New space satellite applications • + 1. 0.539, 08/13/91, NASA Hasn't Scrapped Imaging Spectrometer • + 2. 0.533, 07/09/91, NASA Scratches Environment Gear From Satellite Plan • 3. 0.528, 04/04/90, Science Panel Backs NASA Satellite Plan, But Urges Launches of Smaller Probes • 4. 0.526, 09/09/91, A NASA Satellite Project Accomplishes Incredible Feat: Staying Within Budget • 5. 0.525, 07/24/90, Scientist Who Exposed Global Warming Proposes Satellites for Climate Research • 6. 0.524, 08/22/90, Report Provides Support for the Critics Of Using Big Satellites to Study Climate • 7. 0.516, 04/13/87, Arianespace Receives Satellite Launch Pact From Telesat Canada • + 8. 0.509, 12/02/87, Telecommunications Tale of Two Companies

38. Relevance Feedback Example: Expanded Query • 2.074 new 15.106 space • 30.816 satellite 5.660 application • 5.991 nasa 5.196 eos • 4.196 launch 3.972 aster • 3.516 instrument 3.446 arianespace • 3.004 bundespost 2.806 ss • 2.790 rocket 2.053 scientist • 2.003 broadcast 1.172 earth • 0.836 oil 0.646 measure

39. Top 8 Results After Relevance Feedback • + 1. 0.513, 07/09/91, NASA Scratches Environment Gear From Satellite Plan • + 2. 0.500, 08/13/91, NASA Hasn't Scrapped Imaging Spectrometer • 3. 0.493, 08/07/89, When the Pentagon Launches a Secret Satellite, Space Sleuths Do Some Spy Work of Their Own • 4. 0.493, 07/31/89, NASA Uses 'Warm‘ Superconductors For Fast Circuit • + 5. 0.492, 12/02/87, Telecommunications Tale of Two Companies • 6. 0.491, 07/09/91, Soviets May Adapt Parts of SS-20 Missile For Commercial Use • 7. 0.490, 07/12/88, Gaping Gap: Pentagon Lags in Race To Match the Soviets In Rocket Launchers • 8. 0.490, 06/14/90, Rescue of Satellite By Space Agency To Cost $90 Million

40. Relevance Feedback: Problems Why do most search engines not use relevance feedback? • Users are often reluctant to provide explicit feedback • It’s often harder to understand why a particular document was retrieved after applying relevance feedback

41. Pseudo Relevance Feedback • Automatic local analysis • Pseudo relevance feedback attempts to automate the manual part of relevance feedback. • Retrieve an initial set of relevant documents. • Assume that top m ranked documents are relevant. • Do relevance feedback