=Paper=
{{Paper
|id=None
|storemode=property
|title=
From Terms to Concepts: A Revisited Approach to Local Context Analysis
|pdfUrl=https://ceur-ws.org/Vol-704/18.pdf
|volume=Vol-704
|dblpUrl=https://dblp.org/rec/conf/iir/CaputoBS11
}}
==
From Terms to Concepts: A Revisited Approach to Local Context Analysis
==
https://ceur-ws.org/Vol-704/18.pdf
From terms to concepts: a revisited approach to
Local Context Analysis
Annalina Caputo, Pierpaolo Basile and Giovanni Semeraro
Department of Computer Science
University of Bari
70126 Bari, Italy
{basilepp,acaputo,semeraro}@di.uniba.it
Abstract. Pseudo-Relevance Feedback (PRF) is a widely used tech-
nique which aims to improve the query representation assuming as rel-
evant the top ranked documents. This should results in better perfor-
mance as, after the expansion and re-weigh of the original query, the
resultant vector should contain all those worth features able to express
utterly the user’s information need. This paper presents the application
of a pseudo-relevance feedback technique, called Local Context Analysis
(LCA), to SENSE (SEmantic N-levels Search Engine). SENSE is an IR
system that tries to overcome the limitations of the ranked keyword ap-
proach by introducing semantic levels which integrate (and not simply
replace) the lexical level represented by keywords. The evaluation shows
that this PRF technique is able to work worthily on both the lexical level
represented by keywords and the semantic level represented by WordNet
synsets.
1 Introduction and Background
LCA [6] is a PRF technique which exploits the context of query words in a col-
lection of documents, by analyzing which words in the top ranked documents
simultaneously co-occur with the most of query terms. This paper presents an
extension of LCA in SENSE [2], an IR system which aims to be a step forward
traditional keyword-based systems. The main idea underlying SENSE is the def-
inition of an open framework to model different semantic aspects (or levels) per-
taining document content. Two basic levels are available in the framework: The
keyword level, the entry level in which the document is represented by the words
occurring in the text, and the word meaning level, represented through synsets
obtained by WordNet, a semantic lexicon for the English language. A synset is
a set of synonym words. Word Sense Disambiguation algorithms are adopted to
assign synsets to words. Analogously, several different levels of representation
are needed for representing queries. In this model also the notion of relevance of
a document d in the collection for the user query q is extended to several levels
of representation. A local similarity function computes the document relevance
for each level, according to feature weights defined by the corresponding local
scoring function. Then, a global ranking function is needed to merge all the result
lists that come from each level in a single list of documents ranked in decreasing
order of relevance. In the same way, the PRF technique should be able to work
over all the levels involved in our model.
2 nLCA
LCA proved its effectiveness on several test collections. This technique combines
the strength of a global relevance feedback method like PhraseFinder [4] while
preventing its drawbacks. LCA selects the expansion terms directly from the
collection on the basis of their co-occurrences with query terms. Differently from
PhraseFinder, this method computes this statistics on the basis of the top-ranked
documents that are assumed to be the relevant ones, with a considerable gain in
efficiency. Then, LCA joins the advantage of a global technique with the efficiency
of a local one. This technique is grounded on the hypothesis that terms frequently
occurring in the top-ranked documents frequently co-occur with all query terms
in those documents too. Our work exploits the idea of LCA in the N-levels model.
In that model, LCA is integrated into two representation levels: keyword and
word meaning. The challenge lies in the idea that the LCA hypothesis could also
be applied to the word meaning level, in which meanings are involved instead of
terms. The original measure of co-occurrence degree is extended to encompass
the weight of a generic feature (keyword or word meaning) rather than just a
term.
We modify the orginal formula introducing two new factors θ and γ (in bold
in following formulae):
log10 (co(f, qi ) + 1) · idf (f )
codegree(f, qi ) = (1)
log10 (n)
codegree is computed starting from the degree of co-occurrence of the feature f
and the query feature qi (co(f, qi )), but it takes also into account the frequency
of f in the whole collection (idf (f )) and normalizes this value with respect to
n, the number of documents in the top-ranked set.
X
co(f, qi ) = tf (f, d) · tf (qi , d) · θ (2)
d∈S
log10 NNf
idf (f ) = min(1.0, ) (3)
5.0
where tf (f, d) and tf (qi , d) are the frequencies in d of f and qi respectively, S is
the set of top-ranked documents, N is the number of documents in the collection
and Nf is the number of documents containing the feature f . For each level, we
retrieve the n top-ranked documents for a query q and then we rank the feature
belonging to those documents by computing the function lca, as follows:
Y
lca(f, q) = (δ + γ · codegree(f, qi ))idf (qi ) (4)
qi ∈q
θ and γ transfer the importance of a query term into the weight of words it co-
occurs with. In fact, θ takes into account the frequency of a query term (qf ) in
the original query (θ = 1+log(qf (qi ))), while γ takes into account a boost factor
associated with a specific query term (γ = 1 + log(boost(qi ))). lca is used to rank
the list of features that occur in the top-ranked documents, δ is a smoothing
factor, while the power is used to raise the impact of rare features. The new
query q ∗ is given by the sum of the original query q and the expanded query q 0 ,
where q 0 = (wf1 , ..., wfk ) and wfi = 1.0 − 0.9i
k is the weight of the i-th feature fi .
Hence, the new query is re-executed to obtain the final list of ranked documents
for each level. Differently from the original work, we applied LCA to the top
ranked documents rather than passages1 .
3 Setting the scene
We evaluate our technique on the CLEF Ad-Hoc Robust Task collection [1].
The CLEF collection is composed by 166,717 documents and 160 topics. In this
collection both documents and topics are disambiguated by the task organiz-
ers. Topics are structured in three fields: T itle, Description and N arrative.
All query fields are exploited in the search phase with a different boost factor:
T itle = 8, Description = 2 and N arrative = 1. We use the Okapi BM25 [5] as
local similarity functions for both meaning and keyword levels. In particular, we
adopt the BM25-based strategy which takes into account multi-field documents.
Documents in CLEF collection are represented by two fields: HEADLINE and
TEXT. The multi-field representation reflects this structure. We set the BM25
parameters as follows: b = 0.7 in both levels, k1 = 3.25 and 3.50 in keyword
and meaning levels respectively. We tested several n, k, and δ values, and we
set n, k = 10 and δ = 0.1. To compute the global ranking function we adopt the
CombSUM [3] strategy, giving a weight of 0.8 to the keyword level and 0.2 to
the meaning level. All parameters (boosting factors, BM25 and global ranking
function) are set after a tuning phase over a set of training topics provided by
organizers. In order to compare our approach we consider the Mean Average
Precision (MAP) and the Geometric Mean Average Precision (GMAP).
4 Results and Remarks
We performed two experiments in which one level at a time is considered and
then the two lists are merged producing a single list of ranked documents. We
explored two strategies involving LCA: The first strategy (lca) is based on the
formula proposed in [6]. In the second strategy (lca-n), we took into account also
the meaning level and we decided to expand only synsets referring to nouns.
The second strategy tries to overcome a limit of Word Sense Disambiguation
algorithms which, in general, have better performance with nouns. The latter
strategy (lca-n-θγ) is based on lca-n, but with the introduction of θ and γ factors.
The results of our evaluation are depicted in Table 1.
1
In the original work, passages are parts of document text of about 300 words
Table 1. Results on CLEF Ad-Hoc Robust collection
Run MAP GMAP
keyword .4207 .1900
one-level (no-expansion)
synset .3119 .1197
no-expansion .4253 .1973
n-levels lca-n .4304 .1945
lca-n-θγ .4532 .2114
While the synset level alone is not able to reach the performance of the
keyword level, the combination of these two levels without expansion strategies
(no-expansion) improves performance in both MAP and GMAP. All lca strate-
gies exploited in this paper outperform our baseline (no-expansion). However, it
is worth to highlight here that the expansion on synset level produces slightly
better results with respect to the standard metod lca when it involves only nouns
(lca-n). The introduction of θ and γ parameters results in the best performance.
This result supports the claim that the weight of query terms is important also
to weigh the expansion terms. Future work will include the comparison in the
N-levels model of the proposed approach with other PRF, such as Rocchio, Di-
vergence from Randomness and Kullback-Leibler language modeling.
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