What are the contributions of this paper?
Adapting a seq2seq neural system to event detection and classification for Italian, investigating the quality of existing embeddings for the task, and comparing against a state-of-the-art discrete classifier.
This paper reports on a set of experiments with different word embeddings to initialize a state-of-the-art Bi-LSTM-CRF network for event detection and classification in Italian, following the EVENTI evaluation exercise. The net- work obtains a new state-of-the-art result by improving the F1 score for detection of 1.3 points, and of 6.5 points for classification, by using a single step approach. The results also provide further evidence that embeddings have a major impact on the performance of such architectures.
Current societies are exposed to a continuous flow of information that results in a large production of data (e.g. news articles, micro-blogs, social media posts, among others), at different moments in time. In addition to this, the consumption of information has dramatically changed: more and more people directly access information through social media platforms (e.g. Facebook and Twitter), and are less and less exposed to a diversity of perspectives and opinions. The combination of these factors may easily result in information overload and impenetrable “filter bubbles”. Events, i.e. things that happen or hold as true in the world, are the basic components of such data stream. Being able to correctly identify and classify them plays a major role to develop robust solutions to deal with the current stream of data (e.g. the storyline framework BIBREF0 ), as well to improve the performance of many Natural Language Processing (NLP) applications such as automatic summarization and question answering (Q.A.). Event detection and classification has seen a growing interest in the NLP community thanks to the availability of annotated corpora BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 and evaluation campaigns BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . In the context of the 2014 EVALITA Workshop, the EVENTI evaluation exercise BIBREF11 was organized to promote research in Italian Temporal Processing, of which event detection and classification is a core subtask. Since the EVENTI campaign, there has been a lack of further research, especially in the application of deep learning models to this task in Italian. The contributions of this paper are the followings: i.) the adaptation of a state-of-the-art sequence to sequence (seq2seq) neural system to event detection and classification for Italian in a single step approach; ii.) an investigation on the quality of existing Italian word embeddings for this task; iii.) a comparison against a state-of-the-art discrete classifier. The pre-trained models and scripts running the system (or re-train it) are publicly available. .