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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 [*]

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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 [*]

  • Empirical Evaluation

  • Conclusions and Future Work

    [*] Combining Learning and Word Sense Disambiguation for Intelligent User Profiling - IJCAI 2007


Motivation

Motivation

  • Information overload in the scientific congress scenario


Motivation1

Motivation

  • Information overload in the scientific congress scenario


Web personalization

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

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

Learning User Profiles as a Text Categorization problem

OUR STRATEGY

content-based recommendations by learning from TEXTand USER FEEDBACK on items


Keyword based profiles problems

doc1

AI is a branch of computer science

doc2

the 2007 International Joint Conference on Artificial Intelligence will be held in India

USER PROFILE

artificial0.02

intelligence0.01

apple0.13

AI0.15

doc3

apple launches a new product…

Keyword-based profiles: problems

MULTI-WORD CONCEPTS


Keyword based profiles problems1

doc1

AI is a branch of computer science

doc2

the 2007 International Joint Conference on Artificial Intelligence will be held in India

USER PROFILE

artificial0.02

intelligence0.01

apple0.13

AI0.15

doc3

apple launches a new product…

Keyword-based profiles: problems

SYNONYMY


Keyword based profiles problems2

doc1

AI is a branch of computer science

doc2

the 2007 International Joint Conference on Artificial Intelligence will be held in India

USER PROFILE

artificial0.02

intelligence0.01

apple0.13

AI0.15

doc3

apple launches a new product…

Keyword-based profiles: problems

POLYSEMY


Item recommender itr

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 itr1

ITem Recommender (ITR)


Word sense disambiguation wsd

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)


Jigsaw wsd algorithm

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


Jigsaw nouns 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

JIGSAWnouns: The idea


Synset semantic similarity

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


Jigsaw nouns

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}


Jigsaw nouns1

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}


Jigsaw verbs synset description

Glosses

JIGSAWverbs: synset description

  • Descriptionof synset si = gloss + example phrases in WordNet for si


Jigsaw verbs synset description1

JIGSAWverbs: synset description

  • Descriptionof synset si = gloss + example phrases in WordNet for si

Example phrases


Jigsaw verbs the idea

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


Jigsaw verbs example 1 4

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): …


Jigsaw verbs example 2 4

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


Jigsaw verbs example 3 4

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


Jigsaw verbs example 4 4

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


Jigsaw others

JIGSAWothers

  • Based on the Lesk algorithm

  • Similarity between the glosses of each candidate sense of target wordand the glosses of words in the context


Jigsaw others example 1 5

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


Jigsaw others example 2 5

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


Jigsaw others example 2 51

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


Jigsaw others example 2 52

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)


Jigsaw others example 3 5

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


Jigsaw others example 4 5

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


Jigsaw others example 4 51

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


Jigsaw others example 5 5

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


Paper recommending

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


An example of bos generated profile

An example of BOS-generated Profile


Conference participant advisor login

Conference Participant Advisor: Login

Conference Participant

Advisor service


Conference participant advisor selecting papers to train the system

Conference Participant Advisor: Selecting Papers to train the system


Conference participant advisor query disambiguation

Conference Participant Advisor: Query disambiguation


Conference participant advisor rating retrieved papers

Conference Participant Advisor: Rating Retrieved Papers


Conference participant advisor getting the personalized program

Conference Participant Advisor: Getting the Personalized Program


Personalized program delivered by mail

1 - personalized conference program

2 - details about recommended papers

Personalized Program delivered by mail


Conference participant advisor personalized program paper details

Conference Participant Advisor: Personalized Program + Paper details


Experimental evaluation

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


Results of semantic profiles evaluation

Results of Semantic Profiles Evaluation

+2%

=

+2%

+1%


Conclusions future works

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


Service details

Service details

  • Service deployed in VIKEF project at:

    http://193.204.187.223:8080/iswc_rebuild/


Backup slides

Backup slides


Bag of synsets

Bag of Synsets

  • Reduction of features

    • Recognition of bigrams

    • Synonyms represented by the same synsets

Bag of Words

Bag of Synsets


Classification phase

Classification Phase

  • Each document is represented as a vector of BOS, one for each slot

  • Each slot is independent from the others

S = {s1, s2, …, s|S|} is the set of slots

bim is the BOS in slot sm of instance di

tk is the kth token (occurring nkim times in BOS bim)


Training phase

Training Phase

C = {c+, c-}

  • C+likes(ratings 4-5)

  • C–dislikes(ratings 1-2)(3 is neutral)

User ratings ri  Weighted Instances


Evaluation

Evaluation

  • JIGSAW evaluated on SENSEVAL-3 English Sample task: 37.6% Precision

  • JIGSAW evaluated on SENSEVAL-3 English All Word task:52% Precision

SENSEVAL-3 English Sample task


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