In this paper we present a summary of the BioCaster
system architecture for Web rumour surveillance, the
rationale for the choices made in the system design
and an empirical evaluation of topic classification
accuracy for a gold-standard of English and Vietnamese news.
Timely surveillance of disease outbreak events of
public health concern currently requires detailed and
time consuming manual analysis by experts. Recently
in addition to traditional information sources, the
World Wide Web (Web) has offered a new modality
in surveillance, but the massive collection of multilingual texts which must be processed in real time
presents an enormous challenge.
Among currently active Web surveillance systems is
the Public Health Agency of Canada’s GPHIN system
[1] and the MiTAP system [2]. Several key issues
remain including the need for increased automation
of relevance detection, extending surveillance to
cover languages in the Asia-Pacific region and the
need for a quantitative evaluation of system accuracy.
We present a new system called BioCaster, based on
a multilingual ontology of terms in six Asia-Pacific
languages [3] whose purpose is (a) to provide the
computable semantics for 18 named entity (NE)
classes, 3 role types and 7 domain relationships in
this domain [4], (b) to bridge the gap between laymen’s terms that are commonly used in newswire and
expert conceptualization, and (c) to mediate translation of equivalent terms in different languages.

The overall target of our system is to classify articles
according to a simple four class standard: reject, publish, check (borderline) and alert. After data is
downloaded from the Internet using an RSS aggregator and cleansed we perform NE and role analysis,
and then topic classification. At this early stage we
aim simply to separate reject articles from everything
else. Further down the pipeline event analysis will be
used to make fine-grained distinctions with a knowledge of modality, negation, temporality etc. We leave
this for future work and focus here on the early stage
tasks. To test the ability of the system to classify topicality correctly we collected 1000 news texts in English and annotated them by hand for terms in the 18
NEclasses, their roles as well as topical relevance.
350 were judged positive. This was repeated for 334
Vietnamese news texts with 167 judged positive.
We compared Naïve Bayes (NB) against Support
Vector Machines (SVM)[6]. On the English corpus
we attained an accuracy with NB of 88.1%. For Vietnamese accuracy was 91.3% using SVM. While the
size of the gold standard did not allow us to achieve
performance closure we believe that the results show
promising levels of performance and furthermore
highlighted interesting trends in the task such as the
contribution made by specific entity types in combination with roles such as case.
The BioCaster system is currently operational on a
cluster computer and downloads in excess of 5000
news reports each day from over 1000 feeds. From
these approximately 40.6 are found to be relevant
each day and made available for online search by
registered users.