=Paper=
{{Paper
|id=Vol-1172/CLEF2006wn-ImageCLEF-GobeillEt2006
|storemode=property
|title=Query and Document Translation by Automatic Text Categorization: A Simple Approach to Establish a Strong Textual Baseline for ImageCLEFmed 2006
|pdfUrl=https://ceur-ws.org/Vol-1172/CLEF2006wn-ImageCLEF-GobeillEt2006.pdf
|volume=Vol-1172
|dblpUrl=https://dblp.org/rec/conf/clef/GobeillMR06a
}}
==Query and Document Translation by Automatic Text Categorization: A Simple Approach to Establish a Strong Textual Baseline for ImageCLEFmed 2006==
https://ceur-ws.org/Vol-1172/CLEF2006wn-ImageCLEF-GobeillEt2006.pdf
Query and Document Translation by Automatic
Text Categorization:
A Simple Approach to Establish a Strong Textual
Baseline for ImageCLEFmed 2006
Julien Gobeill, Henning Müller and Patrick Ruch
University and Hospitals of Geneva, Switzerland
{julien.gobeill;henning.mueller;patrick.ruch}@sim.hcuge.ch
Abstract
In this paper, we report on the fusion of simple retrieval strategies with thesaural
resources in order to perform document and query translation for cross–language re-
trieval in a collection of medical cases. The collection contains textual and visual
contents. In this paper, we focus on the textual contents of the collection, which
contains documents in three languages: French, English and German. The fusion of
visual and textual content will also be treated. Unlike most automatic categorization
systems, which rely on training data in order to infer text–to–concept relationships,
our approach can be applied with any controlled vocabulary and does not use any
training data. For the 2006 ImageCLEFmed experiments we use the Medical Subject
Headings (MeSH), a terminology maintained by the National Library of Medicine and
which exists in a dozen languages. The basic idea consists of annotating every textual
content of the collection (documents and queries) with a set of MeSH concepts using
an automatic text categoriser. Thus, allowing an interlingual mapping between queries
and documents. For tuning purposes, the system uses a sample of MEDLINE from
the OHSUMED collection. Our results, confirmed that such a simple approach is com-
petitive with best performing cross-language retrieval methods for such a collection.
Several simple linear approaches were used to combine textual and visual features
Categories and Subject Descriptors
H.3 [Information Storage and Retrieval]: H.3.1 Content Analysis and Indexing; H.3.3 Infor-
mation Search and Retrieval; H.3.4 Systems and Software; H.3.7 Digital Libraries; H.2.3 [Database
Managment]: Languages—Query Languages
General Terms
Measurement, Performance, Experimentation
Keywords
Image retrieval, Text categorization, multimodal retrieval
1 Introduction
Cross–Language Information Retrieval (CLIR) is increasingly relevant as network–based resources
become commonplace. In the medical domain it is of strategic importance in order to fill the gap
1
between clinical records, written in national languages and research reports massively written in
English. Images are also getting increasingly important and varied in the medical domain, and they
become available in digital form. Despite the fact that images are language–independent, they are
most often accompanied by textual notes in various languages and these textual notes can strongly
improve retrieval quality [15]. There are several ways for handling CLIR. Historically, the most
traditional approach to IR in general and to multilingual retrieval in particular, uses a controlled
vocabulary for indexing and retrieval. In this approach, a librarian selects for each document a few
descriptors taken from a closed list of authorised terms. A good example of such a human indexing
is found in the MEDLINE database, where records are manually annotated with Medical Subject
Headings (MeSH). Ontological relations (synonyms, related terms, narrower terms, broader terms)
can be used to help choose the right descriptors, and solve the sense problems of synonyms and
homographs. The list of authorised terms and semantic relations between them are contained in
a thesaurus. A problem remains, however, since concepts expressed by one single term in one
language sometime are expressed by distinct terms in another. We can observe that terminology–
based CLIR is a common approach in well–delimited fields for which multilingual thesauri already
exist (not only in medicine but also in the legal domain, energy, etc.) as well as in multinational
organisations or countries with several official languages. This controlled vocabulary approach is
often associated with Boolean–like engines, and it gives acceptable results but prohibits precise
queries that cannot be expressed with these authorised keywords. The two main problems are:
• it can be difficult for users to think in terms of a controlled vocabulary. Therefore, the use of
these systems – like most Boolean–supported engines — is often performed by professionals
rather than general users;
• this retrieval method ignores the free–text portions of documents during indexing.
A detailed task description on the medical image retrieval task can be found in [13]. The
non–medical tasks of ImageCLEF are described in [4].
1.1 Translation–based Approaches
A second approach to multilingual interrogation is to use existing machine translation (MT)
systems to automatically translate the queries [5] or even the entire textual database [16] [12] from
one language to another, thereby transforming the CLIR problem into a mono–lingual information
retrieval (MLIR) problem.
This kind of method would be satisfactory if current MT systems did not make errors. A certain
amount of syntactic error can be accepted without disturbing results of information retrieval
systems but MT errors in translating concepts can prevent relevant documents, indexed on the
missing concepts, from being found. For example, if the word traitement in French is translated by
processing instead of prescription, the retrieval process would yield wrong results. This drawback
is limited in MT systems that use huge transfer lexicons of noun phrases by taking advantage of
frequent co–locations to help disambiguation but in any collection of text ambiguous nouns will
still appear as isolated noun phrases untouched by this approach.
1.2 Using Parallel Resources
A third approach receiving increasing attention is to automatically establish associations between
queries and documents independent of language differences. Seminal researches were using latent
semantic indexing [6]. The general strategy when working with parallel or comparable texts is
the following: if some documents are translated into a second language these documents can be
observed both in the subspace related to the first language and the subspace related to the second
one; using a query expressed in the second language, the most relevant documents in the translated
subset are extracted (usually using a cosine measure of proximity). These relevant documents are
in turn used to extract close untranslated documents in the subspace of the first language. This
approach use implicit dependency links and co–occurrences that better approximate the notion of
concept. Such a strategy has been tested with success on the English-French language pair using a
sample of the Canadian Parliament bilingual corpus. It is reported that for 92% of the English text
documents the closest document returned by the method was its correct French translation. Such
an approach presupposes that the sample used for training is representative of the full database,
and that sufficient parallel/comparable corpora are available or acquired.
Other approaches are usually based on bilingual dictionaries and terminologies, sometimes
combined with parallel corpora. These approaches attempt to infer a word by word transfer
function: they typically begin by deriving a translation dictionary, which is then applied to query
translation. Of interest for comparison with our experiments, the study reported by Eichmann
and al. [7] uses the OHSUMED collection and relies on a transfer lexicon built from the Unified
Medical Language System (UMLS1 ). Finally, [19] described a system which combines thesaurus–
based approaches and machine translation for translating queries in a monolingual collection.
To synthesise, we can consider that the performance of CLIR systems typically ranges between
60% and 90% of the corresponding monolingual run [23]. CLIR ratios above 100% have been
reported [28], however, such results were obtained by computing a weak monolingual baseline.
2 Data and Strategies
The 2006 imageCLEF collection contains a set of 50’000 medical images accompanied by textual
reports (mainly pathology and radiology) that are all well-structure in XML. Some reports describe
an entire case containing several images and other reports describe a single image. In this article
we focus on experiments made on the textual part. A short description will also describe the visual
and multi–modal retrieval parts. The text of the collection contains over 40’000 textual documents
in three languages: French, English and German. There are 30 English queries, translated into
French and German. Because the document collection is multilingual, we decided to map each
document and each query to a set of MeSH concepts, thus transforming a fairly usual text retrieval
task into a category retrieval task.
Soergel describes a general framework for the use of multilingual thesauri in CLIR [25], noting
that a number of operational European systems employ multilingual thesauri for indexing and
searching. However, except for very early work [21], there has been little empirical evaluation of
multilingual thesauri in the context of free–text CLIR, particularly when compared to dictionary
and corpus–based methods. This may be due to the cost of constructing multilingual thesauri, but
this cost is unlikely to be more than that of creating bilingual dictionaries or even realistic parallel
collections. It seems that multilingual thesauri can be built quite effectively by merging existing
monolingual thesauri, as shown by the current development of the Unified Medical Language
System (UMLS) or the SemanticMining Multilingual Lexicon[3].
Our approach to CLIR in MEDLINE exploits the UMLS resources and its multilingual compo-
nents. The core technical component of our cross–language engine is an automatic text categoriser,
which associates a set of MeSH terms to any input text. The experimental design is the following:
1. each document and all queries of the imageCLEF collection are annotated by our automatic
text categoriser, which contains MeSH in French, English, and German;
2. each query is annotated by three MeSH categories and we evaluate the impact of varying the
number of categories for the document collection: three, five and eight categories are tested;
3. the MeSH annotated imageCLEF document collection is indexed using a standard engine.
2.1 MeSH–driven Text Categorization
Automatic text categorization has been studied largely and has led to an impressive amount of
papers. A partial list2 of machine learning approaches applied to text categorization includes naive
1 See http://umlsks.nlm.nih.gov.
2 See http://faure.iei.pi.cnr.it/˜fabrizio/ for an updated bibliography.
Bayes [11], k–nearest neighbours [29], boosting [22], and rule–learning algorithms [1]. However,
most of these studies apply text classification to a small set of classes; usually a few hundred, as
in the Reuters collection [8]. In comparison to this our system is designed to handle large class
sets [20]: retrieval tools used are only limited by the size of the inverted file, but 10 5−6 documents
is still a modest range 3 .
Our approach is data–poor because it only demands a small collection of annotated texts
for fine tuning: instead of inducing a complex model using large training data, our categoriser
indexes the collection of MeSH terms as if they were documents and then it treats the input
as if it was a query to be ranked regarding each MeSH term. The classifier is tuned by using
English abstracts and English MeSH terms. Then, we apply the system on the ImageCLEFmed
collection. For tuning the categoriser, the top 15 returned terms are selected because it is the
average number of MeSH terms per abstract in the OHSUMED collection. When applied on the
ImageCLEFmed collection, the number of categories to be attached to every document will be an
important parameter.
2.2 Collection and Metrics
The mean average precision (MAP): is the main measure for evaluating ad hoc retrieval tasks (for
both monolingual and bilingual runs). Following [9], we also use this measure to tune the automatic
text categorization system. We tune the categorization system on a small set of OHSUMED
abstracts: 1200 randomly selected abstracts were used to select the weighting parameters of the
vector space classifier and the best combination of these parameters with the regular expression–
based classifier.
2.3 Visual retrieval techniques
The technology used for the visual retrieval of images is mainly taken from the Viper 4 project.
Much information about this system is available [26]. Outcome of the Viper project is the GNU
Image Finding Tool, GIFT 5 . This tool is open source and can be used by other participants of
ImageCLEF. A ranked list of visually similar images for every query topic was made available for
participants and will serve as a baseline to measure the quality of submissions. Feature sets used
by GIFT are:
• Local color features at different scales by partitioning the images successively into four
equally sized regions (four times) and taking the mode color of each region as a descriptor;
• global color features in the form of a color histogram, compared by a simple histogram
intersection;
• local texture features by partitioning the image and applying Gabor filters in various scales
and directions, quantised into 10 strengths;
• global texture features represented as a simple histogram of responses of the local Gabor
filters in various directions and scales.
A particularity of GIFT is that it uses many techniques well–known from text retrieval. Visual
features are quantised and the feature space is similar to the distribution of words in texts. A
simple tf/idf weighting is used and the query weights are normalised by the results of the query
itself. The histogram features are compared based on a histogram intersection [27].
3 In text categorization based on learning methods, the scalability issue is twofold: it concerns both the ability
of these data–driven systems to work with large concept sets, and their ability to learn and generalise regularities
for rare events: [9] shows how the frequency of concepts in the collection is a major parameter for learning systems.
4 http://viper.unige.ch/
5 http://www.gnu.org/software/gift/
System or Relevant Prec. at MAP
parameters retrieved Rec. = 0
RegEx 3986 .7128 .1601
lnc.atn 3838 .7733 .1421
anc.atn 3813 .7733 .1418
ltc.atn 3788 .7198 .1341
ltc.lnn 2946 .7074 .111
Table 1: Categorization results. For the VS engine, tf.idf parameters are provided: the first
triplet indicates the weighting applied to the “document”, i.e. the concept, while the second is for
the“query”, i.e. the abstract. The total number of relevant terms is 15193.
3 Methods
In this section, we present the basic classifiers and their combination for the categorization task.
Three main modules constitute the skeleton of our system: the regular expression (RegEx) compo-
nent, the vector space (VS) component, and the visual retrieval part. Each of the basic classifiers
implements known approaches to document retrieval. The first tool is based on a regular expres-
sion pattern matcher [10], it is expected to perform well when applied on very short documents
such as keywords: MeSH terms do not contains more than 5 tokens. The second classifier is based
on a vector space engine6 . This second tool is expected to provide high recall in contrast to the
regular expression–based tool, which should privilege precision. The former component uses to-
kens as indexing units and can be merged with a thesaurus, while the latter uses stems (Porter).
Table 1 shows the results of each classifier.
Regular expressions and MeSH thesaurus. The regular expression search tool is applied
on the canonic MeSH collection augmented with the MeSH thesaurus (120’020 synonyms). In this
system, string normalisation is mainly performed by the MeSH terminological resources when the
thesaurus is used. Indeed, the MeSH provides a large set of related terms, which are mapped to a
unique MeSH representative in the canonic collection. The related terms gather morpho-syntactic
variants, strict synonyms, and a last class of related terms, which mixes up generic and specific
terms: for example, Inhibition is mapped to Inhibition (Psychology). The system cuts the ab-
stract into 5–token–long phrases and moves the window through the abstract: the edit–distance
is computed between each of these 5 token sequences and each MeSH term. Basically, the manu-
ally crafted finite–state automata allow two insertions or one deletion within a MeSH term, and
ranks the proposed candidate terms based on these basic edit operations: insertion costs 1, while
deletion costs 2. The resulting pattern matcher behaves like a term proximity scoring system [17],
but is restricted to a 5–token matching window.
Vector space classifier. The vector space module is based on a general IR engine with the
tf.idf 7 weighting schema. The engine uses a list of 544 stop words.
As for setting the weighting factors, we observed that cosine normalisation was especially ef-
fective for our task. This is not surprising, considering the fact that cosine normalisation performs
well when documents have a similar length [24]. As for the respective performance of each basic
classifier, table 1 shows that the RegEx system performs better than any tf.idf schema used by
the VS engine, so the pattern matcher provides better results than the vector space engine for
automatic text categorization. However, we also observe in table 1 that the VS system gives bet-
ter precision at high ranks (P recisionat Recall=0 or mean reciprocal rank ) than the RegEx system:
this difference suggests that merging the classifiers could be effective. The idf factor also seems
to be an important parameter. As shown in table 1 the four best weighting schemas use the idf
6 The easyIR engine, and the automatic categorization toolkit are available on the author’s pages.
7 We use the SMART representation for expressing statistical weighting factors: a formal description can be
found in [18].
Weighting function Relevant Prec. at MAP
concepts.abstracts retrieved Rec. = 0
Hybrids: tf.idf + RegEx
ltc.lnn 4308 .8884 .1818
lnc.lnn 4301 .8784 .1813
nc.ntn 4184 .8746 .1806
anc.ntn 4184 .8669 .1795
atn.ntn 3763 .9143 .1794
Table 2: Combining VS with RegEx.
factor. This observation suggests that even in a controlled vocabulary, the idf factor is able to
discriminate between content– and non–content–bearing features (such as syndrome and disease).
Classifier fusion. The hybrid system combines the regular expression classifier with the
vector–space classifier. Unlike [9] we do not merge our classifiers by linear combination, because
the RegEx module does not return a scoring consistent with the vector space system. Therefore
the combination does not use the RegEx’s edit distance, and instead it uses the list returned by
the vector space module as a reference list (RL), while the list returned by the regular expression
module is used as boosting list (BL), which serves to improve the ranking of terms listed in RL.
A third factor takes into account the length of terms: both the number of characters (L 1 ) and
the number of tokens (L2 , with L2 > 3) are computed, so that long and compound terms, which
appear in both lists, are favoured over single and short terms. We assume that the reference list
has good recall, and we do not set any threshold on it. For each concept t listed in the RL, the
combined Retrieval Status Value (cRSV , equation 1) is:
RSVV S (t) · Ln(L1 (t) · L2 (t) · k) if t ∈ BL,
cRSVt = (1)
RSVV S (t) otherwise.
The value of the k parameter is set empirically. Table 2 shows that the optimal tf.idf param-
eters (lnc.atn) for the basic VS classifier does not provide the optimal combination with RegEx.
Measured by MAP. The optimal combination is obtained with ltc.lnn settings (.1818) 8 , whereas
atn.ntn maximises the P recisionat Recall=0 (.9143).
3.1 Cross–Language Categorization and Indexing
To translate the ImageCLEFmed textual content (queries and documents), we transform the
English MeSH mapping tool described above, attributes MeSH terms to English abstracts. Thus,
the English, French, and German version of the MeSH are simply merged in the categoriser. We
use the weighting schema and system combination (ltc.lnn + RegEx) as described above. Then,
the annotated collection is indexed using the vector–space engine used by the categoriser. For the
document indexing, we rely on weighting schemas based on pivoted normalisation: because the
documents have a very variable length in the collection such a factor can be important. A slightly
modified version of dtu.dtn [2], which has shown some effectiveness for the TREC Genomics, is
used for full–text indexing and retrieval. The English stop word list is merged with a French and
a German stop word list. Porter stemming is used for all documents.
3.2 Visual and multimodal retrieval
For the visual retrieval, one quantisation of four grey levels was used that has shown to be efficient
[14]. To combine visual and textual runs we choose English as the main language and a number of
8 For the augmented term frequency factor (noted a, which is defined by the function α + β × (tf /max(tf )), the
value of the parameters is α = β = 0.5.
five terms. Visual inspection of some results indicated that this might lead to good results. The
combination is simply done linearly by normalising the output of visual and textual results and
then adding them up in various ratios. A second approach for a multimodal combination was to
take the results from the visual retrieval side and increase the value of those results in the first
1000 images that also appear in the visual results by simple re–ranking.
4 Results and Discussion
We submitted several runs including runs combining textual and visual features. The visual runs
have a low overall performance but do perform well on the visual topics. The mixed runs had a
problem in the combination part and are in large part broken. The text retrieval was based on
the cases and thus needed to be expanded towards
4.1 Textual retrieval results
The runs were generated with the very same strategy but using respectively, eight, five and three
MeSH categories to annotate the document collection. The runs were generated automatically
and do not use visual features. For each of them, queries were expanded using the top three
MeSH categories provided by the categoriser. Following previous experiments dedicated to query
translation [19], the optimal number of categories for query translation is around two or three.
Run MAP Run MAP Run MAP
GE-8EN 0.2255 GE-8DE 0.0574 GE-8FR 0.0417
GE-5EN 0.1967 GE-5DE 0.0416 GE-5FR 0.0346
GE-3EN 0.1913 GE-3DE 0.0433 GE-6FR 0.0323
Table 3: MAP of textual runs.
Evaluations are computed by retrieving the first 1’000 documents for each query. In Table 3,
we observe that the maximum MAP is reached when eight MeSH terms are selected per document.
This result suggests that a large number of categories can be selected to annotate a document,
although it must be observed that the precision of the system is low beyond the top one or two
categories. It means that annotating a document with several potentially irrelevant concepts does
not hurt the matching power of our interlingual concepts! This result is somehow consistent with
known observations made on query expansion: a certain number of inappropriate expansion is
acceptable and still can improve retrieval effectiveness of modern search engines. This statement
should be emphasised in the case of the ImageCLEFmed collection, which can be regarded as a
small collection (about 40’000 cases for 50’000 images), where recall plays a more important role
than in larger collections, which can rely on information redundancy with a less important role
for recall.
The English retrieval results were the second best group results of all participants. For lan-
guages other than English it seems to be much harder to obtain very good results as the majority
of documents in the collection is in English.
4.2 Visual and multi-modal runs
Table 4 shows the results for our visual run and the best mixed runs. The visual run is performance–
wise in the middle of the submissions and the best purely visual runs are approximately 30% better.
GIFT performance better in early precision than other systems with a higher MAP. For the visual
topics the results are very satisfactory whereas semantic topics do not perform well.
One big problem shows up in all the mixed runs submitted. They are not as good as the
underlying textual runs even when only a very small percentage of visual information is used. One
problem that might have caused this is the use of a wrong file for the textual runs. The English
Run MAP
GE-GIFT 0.0467
GE-vt10 0.12
GE-vt20 0.1097
Table 4: MAP of visual runs.
runs perform much better than French and German runs, and so mixing up these two can cause
such trouble. We need to further investigate into this to find the reasons and allow for better
multimodal image retrieval.
5 Conclusion and Future Work
We have presented a cross language information retrieval engine for the ImageCLEFmed image
retrieval task, which capitalises from the availability of a multilingual controlled vocabulary to
translate user requests and documents. The system relies on a text categoriser, which maps
queries into a set of predefined concepts. For the ImageCLEFmed 2006 collection, optimal preci-
sion is obtained when selecting three MeSH categories per query and eight MeSH categories per
documents, whereas a larger number leads to best MAP. Visual retrieval shows to work well on the
visual topics but fails on the semantic topics. Further experiments are needed to determine the
best number of concepts. The use of a dynamic threshold will be evaluated in the future. Another
problem is the combination of visual and textual features that really needs further analysis beyond
simple linear combinations.
Acknowledgements
The study has also been partially supported by the Swiss National Foundation (Grants 3200–
065228 and 205321–109304/1), the European Union (SemanticMining Network of Excellence,
INFS–CT–2004–507505) via an OFES Grant (No 03.0399, cf. http://www.genisis.ch/˜natlang/semm/).
References
[1] C Apté, F Damerau, and S Weiss. Automated learning of decision rules for text categorization.
ACM Transactions on Information Systems (TOIS), 12(3):233–251, 1994.
[2] A Aronson, D Demner-Fushman, S Humphrey, J Lin, H Liu, P Ruch, M Ruiz, L Smith,
L Tanabe, and J Wilbur. Fusion of Knowledge-intensive and Statistical Approaches for
Retrieving and Annotating Textual Genomics Documents. In TREC 2005, 2006.
[3] R Baud, M Nystrom, L Borin, R Evans, S Schulz, and P Zweigenbaum. Interchanging lexical
information for a multilingual dictionary. AMIA Symposium Proceedings, 2005.
[4] P Clough, M Grubinger, T Deselaers, A Hanbury, and H. Müller. Overview of the ImageCLEF
2006 photo retrieval and object annotation tasks. In CLEF working notes, Alicante, Spain,
Sep. 2006.
[5] M Davis. Free resources and advanced alignment for cross-language text retrieval. In In
proceedings of The Sixth Text Retrieval Conference (TREC6), 1998.
[6] S Dumais, T Letsche, M Littman, and T Landauer. Automatic cross-language retrieval
using latent semantic indexing. In D Hull and D Oard, editors, AAAI Symposium on Cross-
Language Text and Speech Retrieval, 1997.
[7] D Eichmann, M Ruiz, and P Srinivasan. Cross–language information retrieval with the UMLS
metathesaurus. In SIGIR Conference, pages 72–80, Melbourne, Australia, 1998.
[8] P Hayes and S Weinstein. A system for content-based indexing of a database of news stories.
Proceedings of the Second Annual Conference on Innovative Applications of Intelligence, 1990.
[9] L Larkey and W Croft. Combining classifiers in text categorization. In SIGIR, pages 289–297.
ACM Press, New York, US, 1996.
[10] U Manber and S Wu. GLIMPSE: A tool to search through entire file systems. In Proceedings
of the USENIX Winter 1994 Technical Conference, pages 23–32, San Fransisco CA USA,
17-21 1994.
[11] A McCallum and K Nigam. A comparison of event models for naive bayes text classification,
1998.
[12] J McCarley. Should we translate the documents or the queries in cross-language information
retrieval. ACL, 1999.
[13] H Müller, T Deselaers, T Lehmann, P Clough, and W Hersh. Overview of the ImageCLEFmed
2006 medical retrieval and annotation tasks. In CLEF working notes, Alicante, Spain, Sep.
2006.
[14] Henning Müller, Antoine Geissbuhler, and Patrick Ruch. ImageCLEF 2004: Combining image
and multi–lingual search for medical image retrieval. In Cross Language Evaluation Forum
(CLEF 2004), Springer Lecture Notes in Computer Science (LNCS), Bath, England, 2005.
[15] Henning Müller, Nicolas Michoux, David Bandon, and Antoine Geissbuhler. A review of
content–based image retrieval systems in medicine – clinical benefits and future directions.
International Journal of Medical Informatics, 73:1–23, 2004.
[16] D Oard and P Hackett. Document translation for cross-language text retrieval at the uni-
versity of Maryland. In In Proceedings of The Sixth Text Retrieval Conference (TREC6),
1998.
[17] Y Rasolofo and J Savoy. Term proximity scoring for keyword-based retrieval systems. In
ECIR, pages 101–116, 2003.
[18] P Ruch. Using contextual spelling correction to improve retrieval effectiveness in degraded
text collections. COLING 2002, 2002.
[19] P Ruch. Query translation by text categorization. COLING 2004, 2004.
[20] P Ruch, R Baud, and A Geissbühler. Learning-Free Text Categorization. LNAI 2780, pages
199–208, 2003.
[21] G Salton. Automatic processing of foreign language documents. JASIS, 21(3):187–194, 1970.
[22] R Schapire and Y Singer. BoosTexter: A boosting-based system for text categorization.
Machine Learning, 39(2/3):135–168, 2000.
[23] P Schäuble and P Sheridan. Cross-language information retrieval (CLIR) track overview. In
In Proceedings of The Sixth Text Retrieval Conference (TREC6), 1998.
[24] A Singhal, C Buckley, and M Mitra. Pivoted document length normalization. ACM-SIGIR,
pages 21–29, 1996.
[25] D Soergel. Multilingual thesauri in crosslanguage text and speech retrieval. In D Hull and
D Oard, editors, AAAI Symposium on Cross-Language Text and Speech Retrieval, 1997.
[26] David McG. Squire, Wolfgang Müller, Henning Müller, and Thierry Pun. Content–based
query of image databases: inspirations from text retrieval. Pattern Recognition Letters (Se-
lected Papers from The 11th Scandinavian Conference on Image Analysis SCIA ’99), 21(13-
14):1193–1198, 2000. B.K. Ersboll, P. Johansen, Eds.
[27] Michael J. Swain and Dana H. Ballard. Color indexing. International Journal of Computer
Vision, 7(1):11–32, 1991.
[28] J Xu, A Fraser, and R Weischedel. Cross-lingual retrieval at bbn. In TREC, 2001.
[29] Y Yang. An evaluation of statistical approaches to text categorization. Journal of Information
Retrieval, 1:67–88, 1999.