Categorisation by Context1
Dipartimento di Informatica, Università di Pisa, Italy
Sergio Di Marco
Dipartimento di Informatica, Università di Pisa, Italy
Dipartimento di Informatica, Università di Pisa, Italy
Abstract: Assistance in retrieving of documents on the World
Wide Web is provided either by search engines, through keyword based queries,
or by catalogues, which organise documents into hierarchical collections.
Maintaining catalogues manually is becoming increasingly difficult due
to the sheer amount of material on the Web, and therefore it will be soon
necessary to resort to techniques for automatic classification of documents.
Classification is traditionally performed by extracting information for
indexing a document from the document itself. The paper describes the technique
of categorisation by context, which exploits the context perceivable from
the structure of HTML documents to extract useful information for classifying
the documents they refer to. We present the results of experiments with
a preliminary implementation of the technique.
Key Words: information retrieval, Web search, text categorisation,
Categories: H.3.1, H.3.3, H.3.5, H.5.1, I.2.7, I.5.3
Most Web search engines (e.g. Altavista [Altavista],
HotBot [HotBot], Excite [Excite]) perform search
based on the content of documents and provide results as a linear list
of such documents, typically ranked in order of relevance. The often unsatisfactory
aspect of this approach is that the list can be quite long, with many replications,
and without any indication of possible grouping of related material. For
instance, issuing a query with the keyword "garbage", one would
obtain a list of documents that discuss ecological issues interspersed
with documents about garbage collection in programming languages. Splitting
the list of retrieved documents into thematic categories would significantly
facilitate selecting those documents of more interest to the user.
Notable exceptions to this approach are Lycos
[Lycos] and Yahoo [Yahoo], which maintain a categorisation of part of
their search material. Actually Yahoo gave
1 This is an extended version of a paper presented at the
WebNet 98 conference in Orlando, Florida. The paper has received a "Top
Full Paper Award".
up its general search service in favour of Altavista [Altavista] and supports only searches within its own
catalogue. This allows a more focused search restricted to the documents
within a given category and also the results of a query are presented arranged
However both Lycos and Yahoo are based on manual categorisation of documents
performed by a small set of well-trained categorisation technicians (even
though Lycos recently announced the development of an automatic classifier).
It is questionable whether manual classification will be able to scale
well with the growth of the Web, which will reportedly reach over 30 terabytes
within 2 years, a size larger than the whole US Library of Congress.
First, manual classification is slow and expensive, since it relies
on skilled manpower.
Second, the consistency of categorisation is hard to maintain when different
human classifiers are involved. Categorisation is quite a subjective task,
as other content related tasks like document indexing and hypertext authoring.
An experimental study [Cleverdon 84] on manual
indexing for Boolean information retrieval systems has shown that the degree
of overlap in the keywords selected by two similarly trained people to
represent the same document is, on average, no higher than 30%. Similarly,
studies on the task of hypertext authoring (i.e. adding links to relevant,
or content-related, documents) have found a very small degree of agreement
among different human linkers [Ellis 94]. Automatic
(or semi-automatic) hypertext authoring has proven to be a better alternative
Finally, the task of defining the categories to use (hereafter called
catalogue) is also difficult and subjective, and new categories
emerge continuously in many domains. For example, documents relating to
ActiveX technology are hard to categorise: they might fall within operating
systems or within graphics or within object-oriented programming. None
of these categorisations would be satisfactory, since each of them would
miss to establish close connections with other related technologies, like
CORBA, JavaBeans, etc. In this case it seems that a new category is emerging
("Software Components") which is not currently envisaged. In
fact, by browsing the Web, we may discover several pages that contain references
to documents relating to these subjects: each such page in fact determines
a context for these documents. By exploiting these contexts, an agent should
be capable of creating the appropriate category and to discriminate between
documents falling within different categories.
A similar problem arises in the organisation of personal material, for
instance mail and bookmarks [Maarek 96] and [Weiss 96].
In this paper we investigate a novel technique for automatic
categorisation, which may be dubbed categorisation by context,
since it exploits the context surrounding a link in an HTML document to
extract useful information for categorising the document it refer to. This
technique is complementary to the traditional technique of categorisation
by content [Yang 94, Schütze
95, Ng 97], where information for categorising
a document is extracted from the document itself. Such approach may exploit
linguistic analysis to determine relevant portions of the text [Fuhr
91] and then exploits probabilistic or statistical analysis to perform
feature selection [Yang 97] and to weight selected
features. Categorisation by context instead
exploits relevance hints that are directly provided in the structure
of the HTML documents which people build on the Web. Combining a large
number of such hints, a high degree of accuracy can be achieved.
Another significant advantage of context-based indexing categorisation
is that it can be applied to multimedia material, including images, audio
and video [Shrihari 95], since it does not depend
on the ability to analyse and index by content the documents to be categorised
Furthermore, the mechanism that we will describe can be used to restructure
a catalogue: in fact classification is performed with respect to a base
catalogue hierarchy. Therefore, supplying a different catalogue hierarchy
will produce a new categorisation. This is quite useful in the non-infrequent
case when one discovers that a certain catalogue is no longer appropriate.
With other techniques, manual or automatic, re-categorisation of a document
set according to a different catalogue requires significant effort and
one tries to avoid it. The technique of categorisation by context provides
an automatic tool for doing it.
In particular, the technique can be applied for creating bridges between
catalogues in different languages, since it allows transferring material
from the catalogue in one language into that for another language, which
usually has a different structure to reflect a different culture.
Categorisation by context leverages on the categorisation activity that
users implicitly perform when they place or refer documents on the Web,
turning categorisation, form an activity delegated to a restricted number
of specialists, into a collaborative effort of a community of users. By
restricting the analysis to the documents used by a group of people, one
can build a categorisation that is tuned to the need of that group.
2 Related Work
Categorisation by content builds internal representations of the content
of the documents to categorise ("indexing") and possibly of the
categories themselves ("category profile extraction"). It then
uses a measure similarity of such representations, such as vector
space retrieval [Salton 75] or fuzzy retrieval
[Bookstein 76], to perform categorisation.
Hypertext links pointing to the document to be categorised have not
been used so far for categorisation, although they have been used as clues
for searching documents [Chalmers 98], and for measuring
the "importance" of a Web site [Brin 98].
Vistabar [Marais 97] is a desktop assistant for
Web browsing that provides a function to retrieve pages that refer to the
current document as a mean to find out what people think about a document.
Vistabar has also a categorisation tool, which uses the Yahoo™
classification hierarchy. A category profile is precomputed for each category
in Yahoo and Vistabar performs a traditional vector space matching on the
weighted word frequencies of a document relative to the corpus, exploiting
however the hierarchy of categorisation to prune the search. Vistabar also
allows sharing categorisation and annotations by a group of users.
Purcell and Shortliffe [Purcell 95] discuss the
shortcomings of traditional retrieval systems and describe a context-based
technique applied in the medical domain.
WebTagger [Keller 97] is a personal bookmarking
service that provides both individuals and groups with a customisable means
of organising and accessing Web-based information resources. In addition,
the service enables users to supply feedback on the utility of these resources
relative to their information needs, and provides dynamically updated ranking
of resources, based on incremental user feedback.
Automatic categorisation is the approach used by Northern
Light [Northern Light] in their new search service, which dynamically
organises search results, creating Custom Search Folders of documents with
similar subjects, sources, or types. Within each folder a new subset of
the original result list is produced containing only more focused results.
Contextual information is exploited in ARC [Chakrabarti
98], a system for automatically compiling a list of authoritative Web
resources on a topic. ARC considers two kinds of pages: authority
pages and hub pages. An authority page contains a lot of information
about a topic. A hub page is one that contains a large number of links
to pages containing information about the topic. The algorithm computes
iteratively two scores for each page p, a hub score h(p)
and an authority score a(p). Each iteration consists of two
steps: (1) replace each a(p) by the weighted sum of the h(p)
values of pages pointing to p; (2) replace each h(p)
by the weighted sum of the a(p) values of pages pointed to
by p. A positive numerical weight w(p, q)
is assigned to each link (from page p to page q) that increases
with the amount of topic-related text in the vicinity of the href
from p to q. This weight is computed from the number of matches
between terms in the topic description and a window of 50 bytes of text
of the href. The topic-related text can be considered contextual information
that the algorithm propagates through links reinforcing the belief that
a page is an authority on a topic.
People browse through documents and base their decisions to follow a
link on its textual description or on its position (in case of image maps
or buttons). At least for textual links, in principle a document should
provide sufficient information to describe a document it refers to. HTML
style guides [Tilton 95] suggest making sure that
the text in a link is meaningful, avoiding common mistakes like using adverbs
or pronouns: "The source code is here" or "Click
this". Even if the link itself is not sufficiently descriptive,
the surrounding text or other parts of the document normally supply enough
descriptive information. If such information is sufficient to decide whether
a document is worth reading, we can assume it is also sufficient to categorise
The technique of categorisation by context consists in extracting contextual
information about documents by analysing the structure of Web documents
that refer to them. The overall architecture of the task of categorisation
by context is described
in [Fig. 1], and consists in spidering Web documents, HTML structure
analysis, URL categorisation, weight combination and catalogue update.
3.1 Spidering and HTML Structure Analysis
This task starts from list of URLs, retrieves each document, analyses
the structure of the document expressed in terms of its HTML tags. For
an introduction to HTML we refer to the HTML
The tags considered are currently: <TITLE>, <Hn>, <UL>,
<DL>, <OL>, <A>. Whenever one of these tags is found,
a context phrase is recorded, which consists of the title within a pair
<Hn> </Hn>, or the first portion of text after a <UL>
or <DL> tag, or the phrase within a <A> tag. When a <A>
tag is found containing an URL, an URL Context Path
(URL: C1: C2: : Cn)
is produced, which consists of the sequence of the context strings so far
(C1: C2: : Cn)
associated to the URL. Therefore C1 is the text in the
anchor of the URL, and the other Ci are the enclosing
contexts in nesting order.
In the analysis tags related to layout or emphasis (<EM>, <B>,
<CENTER>, <FONT> etc.) are discarded.
Figure 1: Architecture of Categorisation
Another possible element for a context is the title of a column or row
in a table: tag <TH>. Such title can be effectively used as a context
for the elements in the corresponding column or row.
For example, consider the following fragment of an HTML page from Yahoo:
<title>Yahoo! - Science:Biology</title> </head>
M.I.T. Biology Hypertextbook</a> - introductory resource including
chemistry, biochemistry, genetics, cell and molecular biology, and immunology.
Biodiversity and Biological Collections</a>
- information about specimens in biological collections, taxonomic authority
directories of biologists, reports by various standards bodies, and more.
Biologist's Control Panel</a> - many biology databases, library and
Biologists Search Palette</a> - a collection of useful search engines
databases on the Internet, accessed through either the Web or gopher.
the following context paths are created:
"M.I.T. Biology Hypertextbook" :
"introductory resource including information on chemistry, biochemistry,
genetics, cell and molecular biology, and immunology" :
"Yahoo! - Science:Biology"
"Biodiversity and Biological Collections"
"information about specimens in biological collections, taxonomic
authority files, directories of biologists, reports by various standards
bodies, and more"
"Yahoo! - Science:Biology" :
"Biologist's Control Panel"
"many biology databases, library and literature links"
"Yahoo! - Science:Biology" :
"Biologists Search Palette"
"a collection of useful search engines for biological databases on the
Internet, accessed through either the Web or gopher"
"Yahoo! - Science:Biology" :
Any URL found during the analysis is passed back to the spidering process,
if it points to a document within the current site and stored for later
analysis if it points to an external site. This allows us to perform a
depth-first visit of a site, collecting any categorisation information
it contains about itself and other sites.
The categorisation task exploits the database of URL Context Path
and the Category Tree within which the URL must be categorised.
The Category Tree consists of a tree (or a DAG), where each node contains
a title, i.e. a single word or phrase, which identifies the category.
The goal of the categorisation is to find the most appropriate categories
under which an URL should be categorised. The output of the categorisation
is a sequence of weights associated to each node in the Category Tree:
URL: N1=w1, N2=w2,
Each weight wi represents a degree of confidence that
the URL should belong to the category represented by node Ni.
The weights from the Context Path for a URL are added with all other
Context Paths for the same URL and normalised. If the weight for a node
is greater than a certain threshold, the URL is categorised under that
The mechanism should allow for categorising an URL under more than one
node, but never in two nodes which are descendant of one another.
The categorisation algorithm considers each node in the Category Tree
as a path. For instance, the following part of the Arianna
category tree (Arianna [Arianna] is a search engine
for the Italian Web space that we are using for our experiments):
Affari e Finanza
Banche ed Istituzioni Finanziarie
Eventi e Fiere
Informazioni e Servizi
Pagine delle Aziende
Scuole ed Istituti di Formazione
Distribuzione e Vendita
Eventi e Fiere
Pubblicazioni e Riviste
Scuole, Corsi e Associazioni
Software e Hardware
Notizie ed informazioni
Organizzazioni e Società
Sport Individuali e di Squadra
corresponds to the following paths, translated into English:
Business and Finance: Insurance
Business and Finance: Associations
Business and Finance: Banks
Business and Finance: Events
Business and Finance: Fairs
Business and Finance: News
Business and Finance: Services
Business and Finance: Companies
Business and Finance: Publications
Business and Finance: Schools
Notice that the category Events appears in several categories, therefore
the title of the category is not sufficient to identify the category: the
whole path is necessary to disambiguate among the categories. Notice also
that we split into two paths any title which contains two terms joined
by "e", which literally means "and" but corresponds
to the union of the categories, rather than their intersection.
The categorisation algorithm works as follows: for categorising an URL
it computes first a vector of matching weights for each path in the Category
Tree, then it determines the paths with the best matching vectors, and
finally updates the catalogue.
4.1 Computing path match vectors
Given an URL context path (URL: C1: C2:
... : Cn), the algorithm considers in turn each Ci,
starting from C1. To each level we associate a weight
dl, decreasing from C1 = 1,
for instance with a value 1/log2(n - 1). It may
be worthwhile to adapt these weights take into account the significance
of a tag, for instance a <TITLE> tag may have a slightly higher weight
than its position would imply.
Then we extract from Cl the noun phrases it contains
n0, ... nk, as described in [Section
For each path p in the Category Tree, we create a path match
vector pv with as many fields as the path length, initialised to
Each phrase ni is matched against each title in each
path. If there is a match between ni and the title of
category in the k-th position in path p, with matching weight
mw, then dl x mw is added to pvk.
This process is repeated for each level 1< l < n.
Notice that since there can be several matching, the value in a field
of a path match vector can be greater than one; therefore these values
are normalised before performing comparisons.
4.2 Selecting best matching categories
Any path match vector pv that contains non-zero fields is considered
as a potential candidate. The selection among these candidates
is performed as follows:
- discard any path with 0's in its leading fields. This means that that
we found matches only in some subcategory but not in the top-level categories.
For instance an URL relating to a business event we matched Events,
but not Sports. The URL in fact will match both categories Business
and Event in the Business:Event path.
- among candidates with similar overall score, select the one with longer
path. This forces categorisation under the most specific category.
The selected estimate records are stored in the database, associated
to the URL. When the same URL is reached form a different path, the new
estimates are combined to the previous ones. This will either enforce the
indication of the category for a URL or suggest alternative categories
for the URL.
In order to compute the match between a noun phrase in a context and
a title in the catalogue, we exploit several tools and data structures.
First, since it is likely that noun phrases do not use the exact terms
present in the titles, we must widen somewhat the search. In order to do
this we precompute a neighbourhood of words for each term in a title, exploiting
information from WordNet, which we store in a neighbourhood table. Second,
matching a noun phrase with a title, which in general is also a noun phrase,
requires matching multiple words.
5.1 Neighborhood Table
In the following we will denote with D the set of titles of categories
in the Categorisation Tree, and with DS the set of single words
For each term t in DS we build the set I(t)
of terms in the neighbourhood of t (synonyms, hyponyms, etc.). Moreover,
for each term s in I(t) we denote with w(s, t)
the weight of s with respect to t, which depends on whether
s is a synonym, hyponym,
hypernym or a related term to t. If a term belongs to several
of these classes, w(s, t) will take the largest
of such weights.
We implement the inverted list of I as a hash table TI
whose keys are elements in DS. Since we need to know the matching
weight of each pair of words, it is convenient to store in this table not
just the matching word but also the matching weight. Therefore the table
associates to each word s in the neighbourhood of some term t,
a set of pairs , where t DS.
Table TI can be built as follows:
Let 'sport event' be the title of one category, then sport
event is in D, sport and event are in DS.
Table I might look like this:
While table TI could be like this:
5.2 Extracting noun phrases from contexts
Before matching a context with category titles, we extract from the
context the noun phrases it contains. For this task we currently use LTPOS
[Mikheev 98], a lexicon-based part of speech (POS)
tagger with a stochastic disambiguator. For example, the sentence
The World Wide Web has evolved into an impressive open structure
for sharing information
is segmented by LTPOS into a series of words each of which is assigned
a single POS-tag:
The_DT World_NP Wide_NP Web_NN has_VBZ evolved_VBN into_IN an_DT
impressive_JJ open_JJ structure_NN for_IN sharing_VBG information_NN
From such series we consider sequences of adjectives and nouns which
form noun phrases, obtaining:
World Wide Web
impressive open structure
The example shows that tagging avoids the mistake of considering the
term World in itself, which would lead to inappropriate categories,
but rather suggests to consider it as part of a phrase whose main subject
A POS tagger like LTPOS is essential here, since the lexical information
provided by WordNet is not sufficient to determine the proper meaning and
role of a word in the context of a phrase.
5.3 Matching noun phrases
The next step is matching a noun phrase from a context with the category
titles from the catalogue. For instance we might have the noun phrase football
match which should match with the title sport event.
Let s = s0sn be a noun phrase
consisting of a sequence of words si. We must find a
category title t = t0 ... tn
where each si I(ti) and to retrieve
the corresponding weights w(si, ti).
We can construct all possible sequences:
t0 ... tn
exploiting the information in table TI, substituting each si
with any ti such that TI(si).
In order to determine quickly whether a sequence corresponds to a title
and in which position in the path the title appears, we use a hash table
which associates noun phrases to pairs of a path match vector and the corresponding
index. There can be more than one pair since the same title may appear
in more than one place in the category tree.
If no match is found, the process is repeated with s = s1
... sn and so on. The reason for this is that a noun
phrase may be over specific, for instance because of the presence of too
many adjectives. By dropping such adjectives, we generalise the phrase
and try to match it again.
Having found the weights w(si, ti)
for a phrase, there are several ways to combine them to form the overall
weight of the match between the noun phrase and the category title. We
chose to use the average of w(si, ti)
for all i. Such value mw is added to the weight vector of
each path which contains the title, as described in [Section 4.1].
A prototype tool for categorisation by context has been built in order
to verify the validity of the method.
An HTML structure analyser has been built in Perl, derived from the
analyser used in Harvest.
A spidering program has been written in Java™, which uses
the HTML analyser to produce a temporary file of URL Context Paths.
Also in Java, we developed a categoriser program that interfaces to
WordNet [Miller 95] to perform morphing of the words
appearing in the context paths and other linguistic analysis.
We have used the Arianna [Arianna] catalogue
for the experiment, translating into English their names, and we tried
to categorise a portion of Yahoo [Yahoo].
We show the results of categorisation for the first few items present
in the page http://www.yahoo.com/Science/Biology:
- MIT Biology Hypertextbook - introductory resource including information
on chemistry, biochemistry, genetics, cell and molecular biology, and immunology.
- Biodiversity and Biological Collections - information about specimens
in biological collections, taxonomic authority files, directories of biologists,
reports by various standards bodies, and more.
- Biologist's Control Panel - many biology databases, library and literature
- Biologists Search Palette - a collection of useful search engines for
biological databases on the Internet, accessed through either the Web or
Here are candidate paths for the first URL http://esg-www.mit.edu:8001/esgbio.
On the left we display the path and on the right the corresponding match
weight vector, which has an entry for each element of the path representing
the weight of the match for the corresponding title.
science ......................................... 1.0
science : general ............................... 1.0 0.0
science : earth ................................. 1.0 0.0
science : environment ........................... 1.0 0.0
science : psychology ............................ 1.0 0.0
science : mathematics ........................... 1.0 0.0
science : engineering ........................... 1.0 0.0
science : physics ............................... 1.0 0.0
science : chemistry ............................. 1.0 0.0
science : space ................................. 1.0 0.0
science : astronomy ............................. 1.0 0.0
science : computer .............................. 1.0 0.0
science : biology ............................... 1.0 2.13093
science : botany ................................ 1.0 0.0
technology : biology ............................ 0.0 2.13093
science : archaeology ........................... 1.0 0.0
science : agriculture ........................... 1.0 0.0
The grading of candidates for http: //esg-www.mit.edu:8001/esgbio produces
science ......................................... 1.0
science : general ............................... 0.5
science : earth ................................. 0.5
science : environment ........................... 0.5
science : psychology ............................ 0.5
science : mathematics ........................... 0.5
science : engineering ........................... 0.5
science : physics ............................... 0.5
science : chemistry ............................. 0.5
science : space ................................. 0.5
science : astronomy ............................. 0.5
science : computer .............................. 0.5
science : biology ............................... 1.565465
science : botany ................................ 0.5
technology : biology ............................ 1.065465
science : archaeology ........................... 0.5
science : agriculture ........................... 0.5
Here is the result for the categorisation of one URL from page http://www.yahoo.com/Regional/Countries/Italy/Recreation_and_Sports/Sports/Soccer/Clubs_and_Teams.
Here are candidate paths for the URL http://www.dimi.uniud.it/~cdellagi:
sport ........................................... 2.0
sport : individual .............................. 2.0 0.0
sport : team .................................... 2.0 3.0
sport : club .................................... 2.0 1.0
sport : event ................................... 2.0 0.0
sport : news .................................... 2.0 0.0
The grading of candidates for http://www.dimi.uniud.it/~cdellagi:
sport ........................................... 2.0
sport : individual .............................. 1.0
sport : team .................................... 2.5
sport : club .................................... 1.5
sport : event ................................... 1.0
sport : news .................................... 1.0
One aspect worth commenting is the skewed distribution of the real-valued
numbers that appear in these tables: in fact, the gradings of most of the
candidate paths tend to concentrate around a few real numbers such as 1.0,
2.0 or the like. This is primarily due to the fact that no statistical
analysis (i.e. term weighting based on term frequency within the context)
is performed on contexts, since they are typically short text windows and
it is unlikely that the same term occurs more than once in the same context.
This situation is reminiscent of what happens in term weighting for information
retrieval. Document term weights are often computed by a measure (such
as tf x idf, where tf is the term frequency and idf
is the inverse document frequency) that depends on the frequency of occurrence
of the term in the document. Query term weights are computed instead by
a measure (such as idf) independent of such factor, exactly because
of the small size of queries and the consequently small likelihood of multiple
occurrence of the same term within a query [Salton 88].
The results achieved with the current prototype are quite encouraging.
In most cases, the prototype was able to categorise each URL in the most
appropriate category. The
few exceptions appeared due to limitations of the linguistic tools we
are using: e.g. holes in the WordNet concept tree.
As an experiment to determine the quality of the categorisation, we
asked the system to categorise a subset of the Yahoo! pages according to
the same Yahoo! catalogue. In principle we should have obtained exactly
the original categorisation, and this is what we obtained in most cases.
In a few cases the algorithm produced an even better categorisation, by
placing a document in a more specific subcategory: for instance a journal
on microbiology was categorised under the subcategory of microbiology journals
rather than on the category biology journals where it appeared originally.
7 Evolving Categorisation
There are two possible cases when one might feel the need to extend
the categories in the catalogue:
- when a category grows too much, containing a large number of documents
- when a large number of documents do not find a proper place in the
In order to deal with these problems, the Context Paths should be analysed
In both cases, the Context Path for documents in a single large category
should be searched for terms in the context, which produce partitions of
the URLs. Such partitioning terms should be considered as candidates for
new categories. Since several partitioning may arise, statistical analysis
techniques will be applied to rank the most promising alternatives and
present them to the user who will decide which subcategories to add to
the catalogue. The techniques proposed by [Maarek 96]
could be applied, but to the context path rather than to the content of
the documents, in order to produce possible grouping of documents into
The concept hierarchy of WordNet could also provide suggestions of possible
8 Context-based Retrieval
We propose to extend the Arianna database to retain the Context Paths
produced by the categoriser, the estimate records and the categorisation
information for each URL. Exploiting the categorisation information it
will be possible:
- to display the results of a query to Arianna grouped by categories,
facilitating the selection of the relevant documents
- to restrict a search for documents within certain categories
- to ask within which categories a document appear, helping in finding
We described an approach to the automatic categorisation of documents,
which exploits contextual information extracted from the HTML structure
documents. The preliminary results of our experiments with a prototype
categorisation tool are quite encouraging. We expect that incorporating
further linguistic knowledge in the tool and exploiting information from
a large number of sources, we can achieve effective and accurate automatic
categorisation of Web documents.
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We thank Antonio Converti, Domenico Dato, Antonio Gullì, Luigi
Madella, for their support and help with Arianna and other tools. Fabrizio
Sebastiani provided constructive criticism.
This work has been partly funded by the European Union, project TELEMATICS