Sentence examples for of event extraction from inspiring English sources

Increasingly ambitious community challenges [ 20 22] have been a major factor in the increasing sophistication of event extraction systems, both in terms of the complexity of the information extracted and the coverage of different biological subdomains.

Generally, the task of event extraction is to automatically identify events in free text.

Therefore, in order to improve the representativeness of our event collection in the future, less biased methods of event extraction can be used, such as automatic event detection techniques [57, 58] and/or the integration of more comprehensive sets of seed news sources, as done for Chilean news analysis by Maldonado et al. [10].

This way, the overall performance of event extraction may decrease.

Finally, several concrete applications of event extraction are covered, together with emerging directions of research.

Here, there is potential to improve on the performance of event extraction through completing the event chains that have missing information.

At the core of our approach to event extraction are graph representations of sentence syntax and semantics.

State-of-the-art approaches for event extraction use a multi-class classifier for finding the event types.

We collect data about pathway interactions from the literature by expanding a state-of-the-art system for event extraction, the Turku Event Extraction System (TEES) [ 18].

This is not only beneficial for the end-user, providing clues why events are predicted, but will also be applicable for enhancing the implementation of ML frameworks for event extraction.

Although much effort has been put into detection of events in biomedical literature, we note the highly relevant work of Raghavan et al. (21) for temporal classification of medical events, the work of Miwa et al. (22) for event extraction with complex event classification and the short paper by van der Horn et al. (23) for detection of causal relations.