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Artif Intell Med. 2015 Oct;65(2):131-43. doi: 10.1016/j.artmed.2015.06.005. Epub 2015 Jul 17.

Multilingual event extraction for epidemic detection.

Author information

1
Groupe de Recherche en Informatique, Image et Instrumentation, University of Caen Lower-Normandy, boulevard Maréchal Juin, 14032 Caen, France; Laboratoire d'Informatique de Nantes Atlantique, University of Nantes, 2 rue de la Houssinière, 44322 Nantes, France.
2
Groupe de Recherche en Informatique, Image et Instrumentation, University of Caen Lower-Normandy, boulevard Maréchal Juin, 14032 Caen, France; Department of Organizational Behavior, Faculty of Business and Economics, Quartier Dorigny, University of Lausanne, 1015 Lausanne, Switzerland.
3
Groupe de Recherche en Informatique, Image et Instrumentation, University of Caen Lower-Normandy, boulevard Maréchal Juin, 14032 Caen, France; Laboratoire Informatique, Image et Interaction, University of La Rochelle, Avenue Michel Crépeau, 17042 La Rochelle, France. Electronic address: antoine.doucet@univ-lr.fr.
4
Groupe de Recherche en Informatique, Image et Instrumentation, University of Caen Lower-Normandy, boulevard Maréchal Juin, 14032 Caen, France.

Abstract

OBJECTIVE:

This paper presents a multilingual news surveillance system applied to tele-epidemiology. It has been shown that multilingual approaches improve timeliness in detection of epidemic events across the globe, eliminating the wait for local news to be translated into major languages. We present here a system to extract epidemic events in potentially any language, provided a Wikipedia seed for common disease names exists.

METHODS:

The Daniel system presented herein relies on properties that are common to news writing (the journalistic genre), the most useful being repetition and saliency. Wikipedia is used to screen common disease names to be matched with repeated characters strings. Language variations, such as declensions, are handled by processing text at the character-level, rather than at the word level. This additionally makes it possible to handle various writing systems in a similar fashion.

MATERIAL:

As no multilingual ground truth existed to evaluate the Daniel system, we built a multilingual corpus from the Web, and collected annotations from native speakers of Chinese, English, Greek, Polish and Russian, with no connection or interest in the Daniel system. This data set is available online freely, and can be used for the evaluation of other event extraction systems.

RESULTS:

Experiments for 5 languages out of 17 tested are detailed in this paper: Chinese, English, Greek, Polish and Russian. The Daniel system achieves an average F-measure of 82% in these 5 languages. It reaches 87% on BEcorpus, the state-of-the-art corpus in English, slightly below top-performing systems, which are tailored with numerous language-specific resources. The consistent performance of Daniel on multiple languages is an important contribution to the reactivity and the coverage of epidemiological event detection systems.

CONCLUSIONS:

Most event extraction systems rely on extensive resources that are language-specific. While their sophistication induces excellent results (over 90% precision and recall), it restricts their coverage in terms of languages and geographic areas. In contrast, in order to detect epidemic events in any language, the Daniel system only requires a list of a few hundreds of disease names and locations, which can actually be acquired automatically. The system can perform consistently well on any language, with precision and recall around 82% on average, according to this paper's evaluation. Daniel's character-based approach is especially interesting for morphologically-rich and low-resourced languages. The lack of resources to be exploited and the state of the art string matching algorithms imply that Daniel can process thousands of documents per minute on a simple laptop. In the context of epidemic surveillance, reactivity and geographic coverage are of primary importance, since no one knows where the next event will strike, and therefore in what vernacular language it will first be reported. By being able to process any language, the Daniel system offers unique coverage for poorly endowed languages, and can complete state of the art techniques for major languages.

KEYWORDS:

Early event detection; Epidemic surveillance; Multilingual information access; Poorly endowed languages; Tele-epidemiology

PMID:
26228941
DOI:
10.1016/j.artmed.2015.06.005
[Indexed for MEDLINE]

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