Multilingual Epidemic Event Extraction : From Simple Classification Methods to Open Information Extraction ( OIE ) and Ontology (2021)

Abstract: There is an incredible amount of information available in the form of textual documents due to the growth of information sources. In order to get the information into an actionable way, it is common to use information extraction and more specifically the event extraction, it became crucial in various domains even in public health. In this paper, we address the problem of the epidemic event extraction in potentially any language, so that we tested different corpuses on an existed multilingual system for tele-epidemiology: the Data Analysis for Information Extraction in any Language(DANIEL) system. We focused on the influence of the number of documents on the performance of the system, on average results show that it is able to achieve a precision and recall around 82%, but when we resorted to the evaluation by event by checking whether it has been really detected or not, the results are not satisfactory according to this paper’s evaluation. Our idea is to propose a system that uses an ontology which includes information in different languages and covers specific epidemiological concepts, it is also based on the multilingual open information extraction for the relation extraction step to reduce the expert intervention and to restrict the content for each text. We describe a methodology of five main stages: Pre-processing, relation extraction, named entity recognition (NER), event recognition and the matching between the information extracted and the ontology.

## introduction infectious diseases are responsible for the morbidity and mortality and like we see today with the covid-19 pandemic, surveillance provides us with information to improve our knowledge of their epidemiology (space-time dynamics, evolution of clinical and microbiological characteristics), in order to identify an appropriate control and prevention mea-sures. the growth of digital data sources provide an avenue for data-driven surveillance, referred to as epidemic intelligence. the purpose of epidemic intelligence is to detect, analyze and monitor potential health threats over time @xcite . it requires the development of tools dedicated to the collection and processing of unstructured textual data published on the web. information extraction (ie) @xcite is one of the areas of active research in artificial intelligence and made it possible to analyze data from web sources. different tasks of the ie systems have been suggested such as named entity recognition (ner) to identify real-world objects such as names of people, locations, names of diseases, etc. the relation extraction , which aims to find a semantic relation between two entities in a text @xcite . the event extraction is also an important task in the field of ie to detect, from the text, the occurrence of events with specific types, and to extract arguments (i.e. typed participants or attributes) that are associated with an event @xcite . in 2007, the open information extraction has appeared and has introduced a new extraction paradigm unlike the traditional ie methods, relations are automatically detected instead of specifying target relations in advance and it enables a fast extraction over huge datasets @xcite . in this paper, we applied some experiments on the daniel system in order to evaluate its performance especially in the multilingual event extraction in the epidemiological field and we have exploited corpora from several diverse language families namely, english, greek, french, spanish, portuguese, russian, polish, and chinese. on the latter we proposed a new approach which reduces the expert intervention by using a multilingual oie systems for a relation extraction, an automatic ner, and an ontology applied for any epidemiological event. the remainder of this paper is organized as follows. section 2 reviews works related to epidemic surveillance and event extraction systems, section 3 describes the dataset used in the experimental study, in section 4 we present the results. finally, we provide a discussion about our new proposition and a conclusion in section 5. ## related work many systems have been proposed in the domain of ie and which reflects the basis that were taken to build our approach. we made a study on three different approaches that take us to our perspectives for our new system. ## dataset and evaluation we tested the daniel system on three large datasets in different languages (low-or high-resource), the first (daniel-corpus) contains 2089 of relevant and irrelevant files (romain et al., 2013), then we extend the dataset (big-corpus) @xcite to include additional languages so that it covers news articles from several, diverse language families: germanic (english, en), hellenic (greek, el), romance (french, fr), slavic (polish, pl and russian, ru) and sino-tibetan (chinese, zh). it includes 7046 irrelevant files and 1653 relevant files, and the third present the fusion of the two precedent corpora. the statistics of the dataset are presented in we used a roc curve to visualize the performance of the binary classifier. it's a plot of the tpr versus the fpr for every possible classification threshold. a classifier that does a very good job separating the classes will have a roc that hugs the upper left corner of the plot 2 . conversely, a classifier that does a poor job will have a roc language # texts # paragraphs # char.( 10 ## conclusion and perspectives in this paper we have focused on the study of some systems in the epidemiological field such as daniel and biocaster, and an event extraction system which is based on a methodology that opens we are looking for evaluating the results of the test obtained and compare them by another systems like biocaster and daniel.