Seminario Eloisa Vargiu, 06-09-2012

Eloisa Vargiu (evargiu@bdigital.org) – Cagliari, 6
September 2012

OUTLINE OF THE TALK
 Introduction
 Online Advertising

 A Modern Contextual Advertising System
 Syntactic Textual Analysis
 Semantic Textual Analysis
 Matching
 An Example: ConCA
 Experimental Results
 Conclusions
 References

Eloisa Vargiu (evargiu@bdigital.org) – Cagliari, 6 September 2012

OUTERNET & INTERNET


OUTERNET & INTERNET
 In Atkinson’s view something is missing…


ONLINE ADVERTISING


ONLINE ADVERTISING
 Sponsored Search


ONLINE ADVERTISING
 Banner Advertising


ONLINE ADVERTISING
 Contextual Advertising


CONTEXTUAL ADVERTISING
Webpage Ad


ONLINE ADVERTISING
 Is it always a good thing?


A MODERN CONTEXTUAL
ADVERTISING SYSTEM

A MODERN CONTEXTUAL ADVERTISING SYSTEM


SYNTACTIC TEXTUAL ANALYSIS
 Text Summarization
 Bag of Words Representation


 Text summarization

 State of the art techniques
 First and Last Paragraph (FLP)
 Title, First and Last Paragraph (TFLP)

 Snippet (S)

 Title and Snippet (TS)


 First and Last Paragraph (FLP)
You don’t need to shell out thousands,
survive various ballots, or swap a family
member for a ticket to enjoy the 2012
Summer Olympic Games this year. There's
all manner of free events and associated
shenanigans taking place in London and
across the UK to mark the occasion. Here
are ten ways to join in without spending any
money.

Indulge in a family feast
Volunteer chefs at 24 Sure Start Centres
across the UK are preparing to dish up free
delights throughout the period. Details,
along with all the other events that make up
the Cultural Olympiad, are available on the
site.
http://www.roughguides.com/website/Travel/SpotLight/ViewSpotLight.aspx?spotLightID=575

shenanigans taking place in London and
money.

site.

shenanigans taking place London 2012 – Ten ways to celebrate the Olympics for free
in London and
money.

site.

 Snippet (S)


 Title and Snippet (TS)


 Bag of Words (BoW) representation

 Dimensionality reduction
 Stop-words removal
 Stemming

 Vector representation
 Set of pairs <word, occurrences>


survive various ballots, or swap a
family member for a ticket to enjoy the
2012 Summer Olympic Games this
year. There's all manner of free events
and associated shenanigans taking
place in London and across the UK to
mark the occasion. Here are ten ways
to join in without spending any money.



X X X X X
survive various ballots, X swap X
or a
family member forX ticket X enjoy the
Xa to X
2012 Summer Olympic Games this X
X X
year. There's all manner X free events
of
X
placeX London and across the UK X
in X X to
X are
mark the occasion. Here X ten ways
X
to joinX without spending any money.
X in X X



X X X X X
survive various ballots, X swap X
or a
family member forX ticket X enjoy the
Xa to X
2012 Summer Olympic Games this X
X X
year. There's all manner X free events
of
X
Shell thousands, survive various
placeX London and across the UK X
in X X to swap family member ticket
ballots,
X are enjoy 2012 Summer Olympic Games
mark the occasion. Here X ten ways
X
to joinX without spending any money.
X in X X year. Manner free events associated
shenanigans taking place London
across UK mark occasion. ten ways
join spending money.


 Stemming
ballots, swap family member ticket
enjoy 2012 Summer Olympic Games
year. Manner free events associated


 Stemming
X
X
X
X
enjoy 2012 Summer Olympic Games X
X X
X
X
X X
X


 Stemming
X
X
X
X
enjoy 2012 Summer Olympic Games X
X X
X
X
X X
Shell thousand, surviv various ballot,
X
swap famil member ticket enjoy 2012
Summer Olymp Game year. Manner
free event associat shenanigan tak
place London across UK mark
occasion. ten way join spend money.


 Vector representation
 TFIDF

<free0.0116>
<olymp, 0.0235>
<event, 0.0012>
<way, 0.0125>
<london, 0.0421>
<celebrat, 0.0005>
<chef, 0.0127>
…


IS ENOUGH THE SOLE SYNTACTIC APPROACH?


 Polysemy…

“BASS”


 Synonymity…

Vehicle Machine
Car Auto
Automobile


SEMANTIC TEXTUAL ANALYSIS
 Taxonomy-based Classification
 Word Disambiguation


 Taxonomy-based Classification
 Classification Features (CF) representation

 Adopted classifiers
 Rocchio
 SVM


 Rocchio
 Each centroid is defined as a sum of TF-IDF values of each
term, normalized by the number of webpages in the class

 The classification is based on the
cosine of the angle between the
webpage and the centroid of each class


 SVM

 The score is related to the
distance of the webpage from a
separation hyperplane


 Word Disambiguation
 Bag of Concepts (BoC) representation

 Adopted lexical supports
 WordNet
 YAGO
 ConceptNet


 WordNet
 A large lexical database
of English. Nouns, verbs,
adjectives and adverbs
are grouped into sets of
cognitive synonyms
(synsets), each
expressing a distinct
concept.


 YAGO
 A semantic knowledge base, derived from Wikipedia,
WordNet and GeoNames


 ConceptNet
 A network of concepts connected by several semantic
relations (e.g., “IsA”, “PartOf”)


MATCHING
 Similarity calculation
 Ranking


MATCHING
 Similarity calculation

 Adopted approaches
 Cosine similarity
 Jaccard index


MATCHING
o Cosine similarity


MATCHING
o Jaccard index
The Jaccard coefficient measures similarity between sample sets,
and is defined as the size of the intersection divided by the size of
the union of the sample sets


MATCHING
 Ranking
 Adopted approaches
 Simple ranking according to the calculated scores
 Learning to rank model


MATCHING
o Learning to rank model
Pointwise approach
o Each query-document pair in the training data has a numerical
or ordinal score
o Regression problem approach: given a single query-document
pair, predict its score
Pairwise approach
o Classification problem approach: learning a binary classifier
which can tell which document is better in a given pair of
documents
Listwise approach
o Optimization problem approach: try to directly optimize the
value of one of the above evaluation measures, averaged over
all queries in the training data


AN EXAMPLE: CONCA
CONCEPTS IN CONTEXTUAL ADVERTISING

CONCA


SYNTACTICAL ANALYSIS
 Text summarization techniques comparison
 FLP vs TFLP vs S vs TS
 Comparison metrics

| {relevant documents}  {retrieved documents} | TP
  
| {retrieved documents} | TP  FP
| {relevant documents}  {retrieved documents} | TP
  
| {relevant documents} | TP  FN
 
 F1  2
 

 Taxonomy
 BankSearch Dataset

SYNTACTICAL ANALYSIS
 Results
FLP TFLP S TS
π 0.745 0.832 0.734 0.806
ρ 0.719 0.801 0.730 0.804
F1 0.732 0.816 0.732 0.805
#t 24 26 12 14

 Adding information about the title improves the
performances
 TFLP has the best performance


SEMANTIC ANALYSIS
 Semantic approaches comparison
 Anagnostopoulos et al. (2007) system vs Armano et al.
(2011-TIR) vs ConCA
 Matching function
  ( p, a)    simBoC  (1   )  simCF

 Comparison metric
N k

 TP
i 1 j 1
ij

  @k  N k

 (TP  FP )
i 1 j 1
ij ij


SEMANTIC ANALYSIS
 Ad repository
 Built by hand by a domain expert
 Taxonomy


SEMANTIC ANALYSIS
 Results

k Anagnostopoulos Armano et al. ConCA
et al.
π α π α π α
1 0.674 0 0.768 0.2 0.773 0.1
2 0.653 0 0.750 0.2 0.752 0.1
3 0.617 0.2 0.729 0.3 0.728 0.1
4 0.582 0.2 0.701 0.3 0.701 0.1
5 0.546 0.1 0.663 0.0 0.668 0.1


SEMANTIC ANALYSIS
 Results


SEMANTIC ANALYSIS
 Results
 Slight improvement by using concepts
 Low values of α → CF more impact then BoC


SYNTACTICAL ANALYSIS VS SEMANTIC ANALYSIS
 Contextual Advertising System
 Armano et al. (2011-TIR)
 Matching function
  ( p, a)    simBoW  (1   )  simCF

 Comparisons varying α
 α = 1 → pure syntax
 α = 0 → pure semantics
 Comparison metric
N k

 TP
i 1 j 1
ij

  @k  N k

 (TP  FP )
i 1 j 1
ij ij


 Ad repository
 Built by hand by a domain expert
 Taxonomy


 Results
α π@1 π@2 π@3 π@4 π@5
0 0.765 0.746 0.719 0.696 0.663
0.1 0.767 0.749 0.724 0.698 0.663
0.2 0.768 0.750 0.729 0.699 0.662
0.3 0.766 0.749 0.729 0.701 0.661
0.4 0.756 0.747 0.729 0.698 0.658
0.5 0.744 0.735 0.721 0.693 0.651
0.6 0.722 0.717 0.703 0.681 0.640
0.7 0.685 0.687 0.680 0.658 0.625
0,8 0.632 0.637 0.635 0.614 0.586
0.9 0.557 0.552 0.548 0.534 0.512
1 0.408 0.421 0.372 0.388 0.640

CONCLUSIONS
 Online advertising
 represents one of the major sources of income for a large
number of websites
 is aimed at suggesting products and services to the
population of Internet users
 Modern contextual advertising systems
 put ads within the content of a generic, third party,
webpage
 adopt both syntactical and semantic textual analyses to
select the most relevant ads for a given webpage
 an example is ConCA


CONCLUSIONS
 Results show that
 the impact of semantics is stronger than that of syntax
 adopting more advanced semantic techniques, such as
concepts, improves the performances
 the more the suggested ads are, the worse the
performance is


REFERENCES
 Syntactical Textual Analysis
 Armano G., Giuliani A., & Vargiu E. Experimenting text summarization
techniques for contextual advertising. 2nd Italian Information Retrieval
Workshop (IIR’11) , 2011.
 Armano G., Giuliani A. & Vargiu, E. Using snippets in text summarization: a
comparative study and an application. 3rd Italian Information Retrieval
Workshop (IIR’12), 2012.
 Kolcz A., Prabakarmurthi V. & Kalita J. Summarization as feature selection for
text categorization. 10th International Conference on Information and
Knowledge Management (CIKM’01). ACM, New York, NY, USA, pp. 365–370,
2001.
 Porter M. An algorithm for sufﬁx stripping. Program 14, 3, 130–137, 1980.
 Salton G., Wong A. & Yang C.S, A vector space model for automatic indexing,
Communications of the ACM, 18, 11, pp.613-620, 1975.


REFERENCES
 Semantic Textual Analysis
 Cortes C. & Vapnik, V.N. Support-Vector Networks, Machine Learning, 20,
1995.
 Fellbaum C. WordNet: An Electronic Lexical Database. Cambridge, MA: MIT
Press, 1998.
 Liu H. & Singh P. ConceptNet: A practical commonsense reasoning tool-kit. BT
Technology Journal 22, pp. 211–226, 2004.
 Miller G.A. WordNet: A Lexical Database for English. Communications of the
ACM, 38, 11, pp. 39-41, 1995.
 Rocchio J. The SMART Retrieval System: Experiments in Automatic Document
Processing. PrenticeHall, Chapter: Relevance feedback in information
retrieval, pp. 313–323, 1971.
 Suchanek F.M., Kasneci G. & Weikum G. Yago - A Core of Semantic
Knowledge. 16th International World Wide Web conference (WWW 2007),
2007.


REFERENCES
 Matching
 Liu T.Y. Learning to rank for information retrieval. Found. Trends Inf. Retr. 3, 3,
pp. 225–331, 2009.
 Radomski P.J. & Goeman, T.J. The homogenizing of Minnesota lake ﬁsh
assemblages. Fisheries, 20, pp. 20–23, 1995.

 Comparison Systems
 Anagnostopoulos A., Broder A. Z., Gabrilovich E., Josifovski V. & Riedel L. Just-
in-time contextual advertising. 16th ACM Conference on Information and
Knowledge Management (CIKM’07). ACM, New York, NY, USA, pp. 331–340,
2007.
 Armano G., Giuliani A. & Vargiu E. Studying the impact of text summarization
on contextual advertising. 8th International Workshop on Text-based
Information Retrieval (TIR’11), 2011.
 Armano G., Giuliani A. & Vargiu E. Semantic enrichment of contextual
advertising by using concepts. International Conference on Knowledge
Discovery and Information Retrieval, 2011.


Contact: Eloisa Vargiu – evargiu@bdigital.org

Seminario Eloisa Vargiu, 06-09-2012

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