We introduce Biomedical Event Extraction as Sequence Labeling (BEESL), a joint endto-end neural information extraction model.BEESL recasts the task as sequence labeling, taking advantage of a multi-label aware encoding strategy and jointly modeling the intermediate tasks via multi-task learning.BEESL is fast, accurate, end-to-end, and unlike current methods does not require any external knowledge base or preprocessing tools.BEESL outperforms the current best system (Li et al., 2019) on the Genia 2011 benchmark by 1.57% absolute F1 score reaching 60.22% F1, establishing a new state of the art for the task.Importantly, we also provide first results on biomedical event extraction without gold entity information.Empirical results show that BEESL's speed and accuracy makes it a viable approach for large-scale real-world scenarios.1

Biomedical event extraction is a crucial task in order to automatically extract information from the increasingly growing body of biomedical literature. Despite advances in the methods in recent years, most event extraction systems are still evaluated in-domain and on complete event structures only. This makes it hard to determine the performance of intermediate stages of the task, such as edge detection, across different corpora. Motivated by these limitations, we present the first cross-domain study of edge detection for biomedical event extraction. We analyze differences between five existing gold standard corpora, create a standardized benchmark corpus, and provide a strong baseline model for edge detection. Experiments show a large drop in performance when the baseline is applied on out-of-domain data, confirming the need for domain adaptation methods for the task. To encourage research efforts in this direction, we make both the data and the baseline available to the research community: https://www.cosbi.eu/cfx/9985.

Bioinformatics is one of the key application of this century. The attention received by all the current media is astonishing and both academics and industries are carrying on researches in many fields related to computational methods applied to life sciences. In this paper we present some results obtained within an Italian funded research project called ESCOGITARE. We present the design and implementation of a grid-based architecture for scientific workflow management that, differently from others, allows the dynamic discovery of existing Web services in combination to ad-hoc developed ones. The paper presents the main features of current scientific workflow management systems. Using a real-world case study (taken by the agricultural research domain), we show how we overcome the limitations of current approaches.