1 / 53

Outline

Outline . Motivation Information overload in a scientific congress scenario Conference Participant Advisor Service Profile-driven paper recommending User Profiles as Bayesian Text Classifiers User Profiles learned from documents semantically indexed through a WSD procedure [*]

kirestin-tillman
Download Presentation

Outline

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Outline • Motivation • Information overload in a scientific congress scenario • Conference Participant Advisor Service • Profile-driven paper recommending • User Profiles as Bayesian Text Classifiers • User Profiles learned from documents semantically indexed through a WSD procedure [*] • Empirical Evaluation • Conclusions and Future Work [*] Combining Learning and Word Sense Disambiguation for Intelligent User Profiling - IJCAI 2007

  2. Motivation • Information overload in the scientific congress scenario

  3. Motivation • Information overload in the scientific congress scenario

  4. Web Personalization • Personalized systems adapt their behavior to individual users by learning user profiles • Structured model of the user interests • Exploitable for providing personalized content and services • Personalization usually done automatically based on the user profile and possibly the profiles of other users with similar interests (collaborative approach) • How personalization can be used in the scientific congress scenario?

  5. Web Personalization in the scientific congress scenario • Learn research interests of participants from papers they rated • Store research interests in personal profiles • Used to build personalized programs delivered to participants

  6. Learning User Profiles as a Text Categorization problem OUR STRATEGY content-based recommendations by learning from TEXTand USER FEEDBACK on items

  7. doc1 AI is a branch of computer science doc2 the 2007 International Joint Conference on Artificial Intelligence will be held in India USER PROFILE artificial 0.02 intelligence 0.01 apple 0.13 AI 0.15 … doc3 apple launches a new product… Keyword-based profiles: problems MULTI-WORD CONCEPTS

  8. doc1 AI is a branch of computer science doc2 the 2007 International Joint Conference on Artificial Intelligence will be held in India USER PROFILE artificial 0.02 intelligence 0.01 apple 0.13 AI 0.15 … doc3 apple launches a new product… Keyword-based profiles: problems SYNONYMY

  9. doc1 AI is a branch of computer science doc2 the 2007 International Joint Conference on Artificial Intelligence will be held in India USER PROFILE artificial 0.02 intelligence 0.01 apple 0.13 AI 0.15 … doc3 apple launches a new product… Keyword-based profiles: problems POLYSEMY

  10. ITem Recommender (ITR) • Advanced NLP techniques used to represent documents • Naïve Bayes text classification to assign a score (level of interest) to items according to the user preferences • Result: semantic user profile - as a binary text classifier (user-likes and user-dislikes) - containing the probabilistic model of user preferences

  11. ITem Recommender (ITR)

  12. Word Sense Disambiguation (WSD) • Process of deciding which sense of a word is used in a specific context • WordNet as sense inventory • nouns, verbs, adverbsand adjectivesorganized into SYNonym SETs (synset), each one representing an underlying lexical concept • change of text representation from vectors (bag) ofwords (BOW) into vectors (bag) of synsets (BOS)

  13. JIGSAW WSD algorithm • Three different strategies to disambiguate nouns, verbs, adjectives and adverbs • Effectiveness of WSD strongly influenced by the POS tag of the target word • Input: d = {w1, w2, …. , wh} document • Output: X = {s1, s2, …. , sk} (kh) • Each siobtained by disambiguating wibased on the context of each word • Some words not recognized by WordNet • Groups of words recognized as a single concept

  14. Adaptation of the Resnik algorithm Semantic similarity between synsets inversely proportional to their distance in the WordNet IS-A hierarchy Path length similarity between synsets used to assign scores to the candidate synsets of a polysemous word JIGSAWnouns: The idea

  15. Placentalmammal Carnivore Rodent 3 4 Mouse (rodent) 5 Feline, felid 2 Cat (feline mammal) 1 Synset Semantic Similarity SINSIM(cat,mouse) = -log(5/32)=0.806 Leacock-Chodorow similarity

  16. mouse cat 02244530: any of numerous small rodents… 02037721: feline mammal… cat 03651364: a hand-operated electronic device … 00847815: computerized axial tomography… mouse JIGSAWnouns “The white cat is hunting the mouse” w = cat C = {mouse} white cat hunt mouse Wcat={02037721,00847815} T={02244530,03651364}

  17. cat 02244530: any of numerous small rodents… 0.806 02037721: feline mammal… 0.806 0.0 0.806 0.0 cat 03651364: a hand-operated electronic device … 00847815: computerized axial tomography… 0.107 mouse JIGSAWnouns “The white cat is hunting the mouse” w = cat C = {mouse} white hunt Wcat={02037721,00847815} T={02244530,03651364}

  18. Glosses JIGSAWverbs: synset description • Descriptionof synset si = gloss + example phrases in WordNet for si

  19. JIGSAWverbs: synset description • Descriptionof synset si = gloss + example phrases in WordNet for si Example phrases

  20. JIGSAWverbs: The idea • It tries to establish a relation between verbs and nouns • Not directly linked in WordNet • Verb w disambiguated using: • nounsin the context of w • nounsinto thedescription of each candidate synset for w

  21. JIGSAWverbs: Example (1/4) w=play N={basketball, soccer} I play basketball and soccer • (70) play -- (participate in games or sport; "We played hockey all afternoon"; "play cards"; "Pele played for the Brazilian teams in many important matches") • (29) play -- (play on an instrument; "The band played all night long") • … nouns(play,1): game, sport, hockey, afternoon, card, team, match nouns(play,2): instrument, band, night … nouns(play,35): …

  22. JIGSAWverbs: Example (2/4) w=play N={basketball, soccer} nouns(play,1): game, sport, hockey, afternoon, card, team, match game1 basketball1 game2 game … basketball … basketballh gamek sport1 sport2 sport MAXbasketball = MAXiSinSim(wi,basketball) winouns(play,1) … sportk

  23. JIGSAWverbs: Example (3/4) w=play N={basketball, soccer} nouns(play,1): game, sport, hockey, afternoon, card, team, match game1 soccer1 game2 game soccer … soccerh gamek sport1 sport2 sport MAXsoccer = MAXiSinSim(wi, soccer) winouns(play,1) … sportk

  24. JIGSAWverbs: Example (4/4) MAXbasketball Φ (play,1)= Weighted average of MAX values taking into account the position of each word in the context wrt the verb nouns(play,1) MAXsoccer ... ... Φ (play,i) nouns(play,i) Synset assigned to “play” = argmax Φ (play,i) i

  25. JIGSAWothers • Based on the Lesk algorithm • Similarity between the glosses of each candidate sense of target wordand the glosses of words in the context

  26. JIGSAWothers:Example (1/5) • 1. {01703749} aged, elderly, older, senior -- (advanced in years; "aged members of the society"; "elderly residents could remember the construction of the first skyscraper"; "senior citizen") • 2. {01546830} aged, ripened - (of wines, fruit, cheeses; having reached a desired or final condition; "mature well-aged cheeses") • … w=agedN={bottle, wine} I bought a bottle of aged wine Candidate synsets for the target word

  27. JIGSAWothers:Example (2/5) • 1. {01703749} aged, elderly, older, senior --(advanced in years; "aged members of the society"; "elderly residents could remember the construction of the first skyscraper"; "senior citizen") • 2. {01546830} aged, ripened -(of wines, fruit, cheeses; having reached a desired or final condition; "mature well-aged cheeses") • … w=agedN={bottle, wine} I bought a bottle of aged wine Keep glosses of candidate synsets

  28. JIGSAWothers:Example (2/5) • 1. {02848798} bottle --(a glass or plastic vessel used for storing drinks or other liquids; typically cylindrical without handles and with a narrow neck that can be plugged or capped) • 2. {13584548} bottle, bottleful -- (the quantity contained in a bottle) • … w=agedN={bottle, wine} I bought a bottle of aged wine Keep glosses of each word in the context

  29. JIGSAWothers:Example (2/5) • 1. {02848798} bottle --(a glass or plastic vessel used for storing drinks or other liquids; typically cylindrical without handles and with a narrow neck that can be plugged or capped) • 2. {13584548} bottle, bottleful -- (the quantity contained in a bottle) • … w=agedN={bottle, wine} I bought a bottle of aged wine • 1. {07784932} wine, vino -- (fermented juice (of grapes especially)) • 2. {04907195} wine, wine-colored -- (a red as dark as red wine)

  30. JIGSAWothers:Example (3/5) • 1. {02848798} bottle --(a glass or plastic vessel used for storing drinks or other liquids; typically cylindrical without handles and with a narrow neck that can be plugged or capped) • 2. {13584548} bottle, bottleful -- (the quantity contained in a bottle) • … w=agedN={bottle, wine} I bought a bottle of aged wine + • 1. {07784932} wine, vino -- (fermented juice (of grapes especially)) • 2. {04907195} wine, wine-colored -- (a red as dark as red wine) = Gloss of the whole context • a glass or plastic vessel used for storing drinks or other liquids typically cylindrical without handles and with a narrow neck that can be plugged or cappedthe quantity contained in a bottle fermented juice (of grapes especially) a red as dark as red wine

  31. No overlap JIGSAWothers:Example (4/5) • 1. {01703749} aged, elderly, older, senior --(advanced in years; "aged members of the society"; "elderly residents could remember the construction of the first skyscraper"; "senior citizen") • 2. {01546830} aged, ripened -(of wines, fruit, cheeses; having reached a desired or final condition; "mature well-aged cheeses") w=agedN={bottle, wine} I bought a bottle of aged wine Overlap between Glosses • a glass or plastic vessel used for storing drinks or other liquids typically cylindrical without handles and with a narrow neck that can be plugged or cappedthe quantity contained in a bottle fermented juice (of grapes especially) a red as dark as red wine

  32. JIGSAWothers:Example (4/5) • 1. {01703749} aged, elderly, older, senior --(advanced in years; "aged members of the society"; "elderly residents could remember the construction of the first skyscraper"; "senior citizen") • 2. {01546830} aged, ripened -(of wines, fruit, cheeses; having reached a desired or final condition; "mature well-aged cheeses") w=agedN={bottle, wine} I bought a bottle of aged wine • a glass or plastic vessel used for storing drinks or other liquids typically cylindrical without handles and with a narrow neck that can be plugged or cappedthe quantity contained in a bottle fermented juice (of grapes especially) a red as dark as red wine Overlap

  33. selected synset: 01546830 JIGSAWothers:Example (5/5) • 1. {01703749} aged, elderly, older, senior --(advanced in years; "aged members of the society"; "elderly residents could remember the construction of the first skyscraper"; "senior citizen") • 2. {01546830} aged, ripened -(of wines, fruit, cheeses; having reached a desired or final condition; "mature well-aged cheeses") w=agedN={bottle, wine} I bought a bottle of aged wine • a glass or plastic vessel used for storing drinks or other liquids typically cylindrical without handles and with a narrow neck that can be plugged or cappedthe quantity contained in a bottle fermented juice (of grapes especially) a red as dark as red wine

  34. Paper Recommending Keyword-based representation (BOW) Tokenization + Stopword + Stemming Sense-based representation (BOS) Tokenization + Stopword + POS + disambiguation Title content-based recommendations by learning from TEXT and USER RATINGS (1-5) on papers Instance (paper) Authors Abstract

  35. An example of BOS-generated Profile

  36. Conference Participant Advisor: Login Conference Participant Advisor service

  37. Conference Participant Advisor: Selecting Papers to train the system

  38. Conference Participant Advisor: Query disambiguation

  39. Conference Participant Advisor: Rating Retrieved Papers

  40. Conference Participant Advisor: Getting the Personalized Program

  41. 1 - personalized conference program 2 - details about recommended papers Personalized Program delivered by mail

  42. Conference Participant Advisor: Personalized Program + Paper details

  43. Experimental Evaluation • Experiments: BOW-generated profiles vs. BOS-generated profiles • ISWC dataset • 100 papers accepted at ISWC 02-03 • 288 ratings collected by 11 users • 5-fold stratified cross-validation • Precision, Recall, F-measure, NDPM • Paper relevant if rating >3 • Probability of class “likes” >0.5 • Wilcoxon signed rank test • Classification for each user is a trial • Low number of independent trials • Significance level p < 0.05

  44. Results of Semantic Profiles Evaluation +2% = +2% +1%

  45. Conclusions & Future Works • Conference Participant Advisor • Intelligent service relying on concept-based profiles • WSD based on linguistic ontology • As a future work integration of: • domain-specific ontologies in the process of semantic representation and indexing of documents • social networks of conference participants as additional source of information

  46. Service details • Service deployed in VIKEF project at: http://193.204.187.223:8080/iswc_rebuild/

  47. Backup slides

  48. Bag of Synsets • Reduction of features • Recognition of bigrams • Synonyms represented by the same synsets Bag of Words Bag of Synsets

More Related