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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

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

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

artificial 0.02

intelligence 0.01

apple 0.13

AI 0.15

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

artificial 0.02

intelligence 0.01

apple 0.13

AI 0.15

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
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
GlossesJIGSAWverbs: 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 overlapJIGSAWothers: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

conference participant advisor login
Conference Participant Advisor: Login

Conference Participant

Advisor service

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
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/

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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