=Paper=
{{Paper
|id=None
|storemode=property
|title=Comparing Word Sense Disambiguation and Distributional Models for Cross-Language Information Filtering
|pdfUrl=https://ceur-ws.org/Vol-835/paper14.pdf
|volume=Vol-835
|dblpUrl=https://dblp.org/rec/conf/iir/MustoNBLGS12
}}
==Comparing Word Sense Disambiguation and Distributional Models for Cross-Language Information Filtering==
https://ceur-ws.org/Vol-835/paper14.pdf
Comparing Word Sense Disambiguation and
Distributional Models for Cross-Language
Information Filtering
Cataldo Musto, Fedelucio Narducci, Pierpaolo Basile, Pasquale Lops, Marco de
Gemmis, Giovanni Semeraro
Department of Computer Science - University of Bari “Aldo Moro”, Italy
{cataldomusto,narducci,basilepp,lops,degemmis,semeraro}@di.uniba.it
Abstract. In this paper we deal with the problem of providing users
with cross-language recommendations by comparing two different content-
based techniques: the first one relies on a knowledge-based word sense
disambiguation algorithm that uses MultiWordNet as sense inventory,
while the latter is based on the so-called distributional hypothesis and
exploits a dimensionality reduction technique called Random Indexing
in order to build language-independent user profiles.
This paper summarizes the results already presented within the confer-
ence AI*IA 2011 [1].
Keywords: Cross-language Information Filtering, Word Sense Disambiguation,
Distributional Models
1 Introduction
Nowadays the amount of information we have to deal with is usually greater than
the amount of information we can process in an effective way. In this context In-
formation Filtering (IF) systems are rapidly emerging since they can adapt their
behavior to individual users by learning their preferences and performing a pro-
gressive removal of non-relevant content. Specifically, the content-based filtering
approach analyzes a set of documents (usually textual descriptions of items) and
builds a model of user interests based on the features (usually keywords) that
describe the items previously rated as relevant by an individual user. One rele-
vant problem related to content-based approaches is the strict connection with
the user language, since the information already stored in the user profile cannot
be exploited to provide suggestions for items whose description is provided in
other languages. In this paper we investigated whether it is possible to repre-
sent user profiles in order to create a mapping between preferences expressed in
different languages. Specifically, we compared two approaches: the first one ex-
ploits a Word Sense Disambiguation (WSD) technique based on MultiWordnet,
while the second one is based on the distributional models. It assumes that in
every language each term often co-occurs with the same other terms (expressed
in different languages, of course) thus, by representing a content-based user pro-
file in terms of the co-occurences of its terms, user preferences become inerently
independent from the language. The paper is organized as follows. Section 2
analyzes related works in the area of cross-language filtering and retrieval. An
overview of the approaches is provided in Section 3. Experiments carried out in
a movie recommendation scenario are described in Section 4. Conclusions and
future work are drawn in the last section.
2 Related Work
The Multilingual Information Filtering task at CLEF 20091 has introduced the
issues related to the cross-language representation in the area of Information
Filtering. The use of distributional models [2] in the area of monolingual and
multilingual Information Filtering is a relatively new topic. Recently the research
about semantic vector space models gained more and more attention: Semantic
Vectors (SV)2 package implements a Random Indexing algorithm and defines a
negation operator based on quantum logic. Some initial investigations about the
effectiveness of the SV for retrieval and filtering tasks is reported in [3].
3 Description of the approaches
Learning profiles through MultiWordnet. In this approach we can imagine
a general architecture composed by three main components: the Content Ana-
lyzer allows to obtain a language-independent document representation by using
a Word Sense Disambiguation algorithm based on MultiWordnet [4]. Similary
to WordNet, the basic building block of MultiWordNet is the synset (SYNonym
SET), a structure containing sets of words with synonymous meanings, which
represents a specific meaning of a word. In MultiWordNet, for example the Italian
WordNet is aligned with the English one, so by processing textual descriptions
of items in both the languages, a language-independent representation in terms
of MultiWordNet synsets is obtained. The generation of the cross-language user
profile is performed by the Profile Learner, using a naı̈ve Bayes text classifier,
since each document has to be classified as interesting or not with respect to the
user preferences. Finally the Recommender exploits the cross-language user pro-
files to suggest relevant items by matching concepts contained in the semantic
profile against those contained in the disambiguated documents.
Distributional Models. The second strategy used to represent items content
in a semantic space relies on the distributional approach. This approach repre-
sents documents as vectors in a high dimensional space, such as WordSpace [2].
The core idea behind WordSpace is that words and concepts (and documents, as
well) are represented by points in a mathematical space, and this representation
is learned from text in such a way that concepts with similar or related meanings
1
http://www.clef-campaign.org/2009.html
2
http://code.google.com/p/semanticvectors/
are near to one another in that space (geometric metaphor of meaning). There-
fore, semantic similarity between documents can be represented as proximity in
a n-dimensional space. Since these techniques are expected to efficiently handle
high dimensional vectors, a common choice is to adopt dimensionality reduction
that allows for representing high-dimensional data in a lower-dimensional space
without losing information. Random Indexing (RI) [2] targets the problem of
dimensionality reduction by removing the need for the matrix decomposition or
factorization since it is based on the concept of Random Projection: the idea
is that high dimensional vectors randomly chosen are “nearly orthogonal”. This
yields a result that is comparable to orthogonalization methods, but saving com-
putational resources. Given two corpus (one for language L1 and another one
for L2 ) we build two monolingual spaces SL1 and SL2 that share the same ran-
dom base by following the procedure introduced in [3]. Since both spaces share
the same random base it is possible to compare elements belonging to different
spaces: for example we can compute how a user profile in SL1 is similar to an
item in SL2 (or viceversa). This property is used to provide recommendations.
4 Experimental evaluation
The goal of the experimental evaluation was to measure the predictive accuracy
of both the content-based multilingual recommendation approaches. We com-
pared the language-independent user profiles represented through MultiWord-
Net sysnsets and the approaches based on distributional hypothesis (W-SV) and
Random Indexing (W-RI), already presented in [3].
The experimental work has been performed on a subset of the MovieLens
dataset3 containing 40,717 ratings provided by 613 different users on 520 movies.
The content information for each movie was crawled from both the English and
Italian version of Wikipedia. User profiles are learned by analyzing the ratings
stored in the MovieLens dataset while the effectiveness of the recommendation
approaches has been evaluated by means of Precision@n (n = 5, 10). We designed
four different experiments: In Exp#1 and Exp#2 we learned user profiles on
movies with English (respectively, Italian) description and recommended movies
with Italian (respectively English) description and we compared their accuracy
with the classical monolingual baselines calculated in Exp#3 and Exp#4. Re-
sults of the experiments are reported in Table 1, averaged over all the users.
In general, the main outcome of the experimental session is that the strategy
implemented for providing cross-language recommendations is quite effective for
both the approaches. Specifically, the approach based on the bayesian classifier
gained the best results in the Precision@5. This means that model has a higher
capacity to rank the best items at the top of the recommendation list. On the
other side, the absence of a linguistic pre-processing is one of the strongest point
of the approaches based on the distributional model and the results gained by the
W-SV and W-RI models in the Precision@10 further underlined the effective-
ness of this model. In conclusion, both the approaches gained good results. Even
3
http://www.grouplens.org
Table 1. Precision@5 and Precision@10
Precision@5 Precision@10
Experiment W-SV W-RI Bayes W-SV W-RI Bayes
exp#1 – eng-ita 84,65 84,65 85,61 84,73 84,43 84,60
exp#2 – ita-eng 84,85 84,63 85,20 84,77 84,54 84,56
exp#3 – eng-eng 85,23 85,29 85,23 85,10 84,86 84,89
exp#4 – ita-ita 85,27 84,84 85,71 85,11 84,86 84,93
though in most of the experiments the cross-lingua recommendation approaches
get worse results w.r.t. the mono-lingual ones, the difference in the predictive ac-
curacy does not appear statistically significant. In general the bayesian approach
fits better in scenarios where the number of items to be represented is not too
high, and this can justify the application of the pre-processing steps required
for building the MultiWordNet synset representation, while the distributional
models, thanks to their simplicity and effectiveness, fit better in scenarios where
real-time recommendations need to be provided.
5 Conclusions
This paper compared two approaches for providing cross-language recommen-
dations. The key idea is to provide a bridge among different languages by ex-
ploiting a language-independent representation of documents and user profiles
based on word meanings. Experiments were carried out in a movie recommen-
dation scenario, and the main outcome is that the accuracy of cross-language
recommmendations is comparable to that of classical (monolingual) content-
based recommendations.
References
1. C. Musto, F. Narducci, P. Basile, P. Lops, M. de Gemmis, and G. Semeraro, “Cross-
language information filtering: Word sense disambiguation vs. distributional mod-
els,” in AI*IA, 2011, pp. 250–261.
2. M. Sahlgren, “The Word-Space Model: Using distributional analysis to represent
syntagmatic and paradigmatic relations between words in high-dimensional vector
spaces,” Ph.D. dissertation, Stockholm University, Department of Linguistics, 2006.
3. C. Musto, “Enhanced vector space models for content-based recommender systems,”
in Proceedings of the fourth ACM conference on Recommender systems, ser. RecSys
’10. New York, NY, USA: ACM, 2010, pp. 361–364.
4. E. Pianta, L. Bentivogli, and C. Girardi, “MultiwordNet: developing an aligned
multilingual database,” in Proc. of the 1st Int. WordNet Conference, Mysore, India,
2002, pp. 293–302.