Anne-Lyse Minard

SemEval-2015 Task 4: TimeLine: Cross-Document Event Ordering

This paper describes the outcomes of the TimeLine task (Cross-Document Event Ordering), that was organised within the Time and Space track of SemEval-2015. Given a set of documents and a set of target entities, the task consisted of building a timeline for each entity, by detecting, anchoring in time and ordering the events involving that entity. The TimeLine task goes a step further than previous evaluation challenges by requiring participant systems to perform both event coreference and temporal relation extraction across documents. Four teams submitted the output of their systems to the four proposed subtracks for a total of 13 runs, the best of which obtained an F1-score of 7.85 in the main track (timeline creation from raw text).

The EVALITA 2016 Event Factuality Annotation Task (FactA)

This paper describes the design and reports the results of two questionnaires. The first of these questionnaires was created to collect information about the interest of industrial companies in the field of Italian text/speech analytics towards the evaluation campaign EVALITA; the second to gather comments and suggestions for the future of the evaluation and of its final workshop from the participants and the organizers of the campaign on the last two editions (2011 and 2014). Novelties introduced in the organization of EVALITA 2016 on the basis of the questionnaires results are also reported.

The Scope and Focus of Negation: A Complete Annotation Framework for Italian

In this paper we present a complete framework for the annotation of negation in Italian, which accounts for both negation scope and negation focus, and also for language-specific phenomena such as negative concord. In our view, the annotation of negation complements more comprehensive Natural Language Processing tasks, such as temporal information processing and sentiment analysis. We applied the proposed framework and the guidelines built on top of it to the annotation of written texts, namely news articles and tweets, thus producing annotated data for a total of over 36,000 tokens.