=Paper=
{{Paper
|id=Vol-1178/CLEF2012wn-INEX-BeauneEt2012
|storemode=property
|title=ENSM-SE at INEX 2012: Basic Experiments
|pdfUrl=https://ceur-ws.org/Vol-1178/CLEF2012wn-INEX-BeauneEt2012.pdf
|volume=Vol-1178
|dblpUrl=https://dblp.org/rec/conf/clef/BeauneBJ12
}}
==ENSM-SE at INEX 2012: Basic Experiments==
https://ceur-ws.org/Vol-1178/CLEF2012wn-INEX-BeauneEt2012.pdf
ENSM-SE at INEX 2012: Basic Experiments
Philippe Beaune, Michel Beigbeder, and Mihaela Juganaru-Mathieu
École Nationale Supérieure des Mines de Saint-Étienne
Institut Henri Fayol
158 cours Fauriel, F 42023 SAINT ETIENNE CEDEX 2, France
beaune@emse.fr, mbeig@emse.fr, mathieu@emse.fr
1 Introduction
Our objective in the INEX 2012 campaign was to integrate the semantic tags
and the linked data in our proximity retrieval model. This model was sucessfully
used in previous INEX campaigns and obtained good results, particularly in
2007 with the second place in the Ad Hoc Track Focused Task [1], and in 2010
with the first place in the Ad Hoc Track Relevant in Context Task [2]
Though we had several discomfitures with the collection because i) there
were several versions of the collection, the last one available at the end of June,
one week before the initial run submission deadline, ii) the different versions
were difficult to follow because they were not clearly identified, iii) not every
documents were well formed according to the XML format, iv) the provided
DTD gives little information on the actual structure and its semantics, v) the
documents contains many semantic annotations but the underlying ideas used
to generate them are not documented making them difficult to apprehend. We
present in section 2 how we processed the documents to alleviate the problems
with the DTD.
Thus we only have been able to do some basic experiments presented in
section 3. In section 4 we present our work in progress.
2 Collection preparation
The collection comes with 3 164 040 documents, of which 4 749 are not well
formed according to the XML format. We deleted these documents in our ex-
periments as they only represent 0,15% of the collection.
Structure was extremely difficult to apprehend with the provided DTD (wiki-
pedia-lod-xml.dtd) because almost every elements can contain any other one.
Here is a small extract of this DTD:
10
11
12
13
14
15
16
17
18
19
20
Some XML elements (such as wikipedia and list) are well defined because
they could only contain a small number of meaningful elements. But 46 of the
70 XML tags defined in this DTD can contain any content, such as heading,
listitem and paragraph.
With this DTD the following extract can be a part of a valid document:
[...]
[...]
where the structure has no sense using the usual meaning of the words heading,
paragraph and so on.
So we decided to build a new collection where each document validates the
very simple following DTD:
Some elements were deleted, for example yagoproperties and dbpediapro-
perties. For the other elements we only kept their textual content. We also
ignored all the attributes except the attribute @name, whose value was kept as
text. This operation was done with xsltproc and processing the whole collection
lasted more than 17 hours.
We also tried to use TreeTagger[3] but it was too slow to process the whole
collection because each document needed around one second to be processed.
Finally, the collection and its very simple structure was indexed with zettair1
with the light stemmer on, lasting 40 minutes.
3 Runs
Three runs were allowed for participants in INEX 2012. Two of our runs were
produced with zettair, the first one, Emse-085, used a language model with a
Dirichlet smoothing. The second one, Emse-086, used the well known BM25
model with k1 = 1.2, k3 = +∞ and b = 0.75. Both these runs were produced
within 30 seconds for the 140 queries.
The third run, Emse-087 used our proximity model developed for the previ-
ous INEX campaigns [4, 5], and its execution needed 2 minutes and 45 seconds.
For the present we do not have the assessments so no evaluation was per-
formed.
1
http://www.seg.rmit.edu.au/zettair/
4 Perspectives
4.1 Proximity model
Our proximity model works with the following type of structured documents:
document ← (part)+
part ← text
part ← (part)+
part ← title ⊕ (part)+
For plain text our model computes a score based on a fuzzy neighbouring pa-
rameterized function. For a document composed of a concatenation of parts, the
score is the sum of the part scores. For a document/part with a title, title words
are considered as close to any word of the part content.
4.2 First choice
The provided DTD doesn’t permit us to easily construct a collection fulfilling
the above description.The title of the documents was easy to extract, but as
the part titles and the parts themselves are not nested, extracting these titles
to insert them in their corresponding part is not possible in XSLT [6]. So we
considered the XML documents as:
document ← title ⊕ text
and we applied our model in this simplified case.
4.3 Future works
We detected that the tag heading could be the title of parts, but the parts
themselves are not explicit and clearly delimited. We will construct a new col-
lection fulfilling our document model using a high level programming language
using the library libxml and build the nesting based on the assumption that
the attribute @level of the tag heading indicates the actual nesting.
We will also consider the tags yagoproperties and dbpediaproperties as
parts of the newer documents. This work is in progress.
References
1. Fuhr, N., Kamps, J., Lalmas, M., Malik, S., Trotman, A.: Overview of the inex
2007 ad hoc track. In Fuhr, N., Kamps, J., Lalmas, M., Trotman, A., eds.: INEX.
Volume 4862 of Lecture Notes in Computer Science., Springer (2007) 1–23
2. Arvola, P., Geva, S., Kamps, J., Schenkel, R., Trotman, A., Vainio, J.: Overview
of the inex 2010 ad hoc track. In Geva, S., Kamps, J., Schenkel, R., Trotman, A.,
eds.: INEX. Volume 6932 of Lecture Notes in Computer Science., Springer (2010)
1–32
3. Schmid, H.: Probabilistic Part-of-Speech Tagging Using Decision Trees. In: Inter-
national Conference on New Methods in Language Processing. (September 1994)
4. Beigbeder, M., Imafouo, A., Mercier, A.: ENSM-SE at INEX 2009 : Scoring with
proximity and semantic tag information. 6203 (2009) 49–58
5. Beigbeder, M.: Focused retrieval with proximity scoring. In Shin, S.Y., Ossowski,
S., Schumacher, M., Palakal, M.J., Hung, C.C., eds.: SAC, ACM (2010) 1755–1759
6. Møller, A., Olesen, M.O., Schwartzbach, M.I.: Static validation of xsl transforma-
tions. ACM Trans. Program. Lang. Syst. 29(4) (August 2007)