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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

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

Motivation

Information overload in the scientific congress scenario

Motivation

Information overload in the scientific congress scenario

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?

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

Learning User Profiles as a Text Categorization problem

OUR STRATEGY content-based recommendations by learning from TEXT and USER FEEDBACK on items

Keyword-based profiles: problems

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

Keyword-based profiles: problems

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

Keyword-based profiles: problems

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

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

ITem Recommender (ITR)

Word Sense Disambiguation (WSD)

Process of deciding which sense of a word is used in a specific context WordNet as sense inventory nouns, verbs, adverbs and adjectives organized into SYNonym SETs (synset), each one representing an underlying lexical concept change of text representation from vectors (bag) of words (BOW) into vectors (bag) of synsets (BOS)

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 si obtained by disambiguating wi based on the context of each word Some words not recognized by WordNet Groups of words recognized as a single concept

JIGSAWnouns: The idea

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

Synset Semantic Similarity

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

JIGSAWnouns

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

JIGSAWnouns

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

JIGSAWverbs: synset description

Glosses Description of synset si = gloss + example phrases in WordNet for si

JIGSAWverbs: synset description

Example phrases Description of synset si = gloss + example phrases in WordNet for si

Showing 1 - 20 of 53 items Details

Name: 
ircdl2007_personalization
Author: 
G. Semeraro
Company: 
Univ. di Bari
Description: 
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
Tags: 
age | wine | bottl | play | user | profil | synset | word
Created: 
3/7/2002 4:39:27 PM
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53
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