Supervised Machine Learning Techniques to Detect TimeML Events in French and English

International audienceIdentifying events from texts is an information extraction task necessary for many NLP applications. Through the TimeML specifications and TempEval challenges, it has received some attention in the last years; yet, no reference result is available for French. In this paper, we try to fill this gap by proposing several event extraction systems, combining for instance Conditional Random Fields, language modeling and k-nearest-neighbors. These systems are evaluated on French corpora and compared with state-of-the-art methods on English. The very good results obtained on both languages validate our whole approach

One, no one and one hundred thousand events: Defining and processing events in an inter-disciplinary perspective

We present an overview of event definition and processing spanning 25 years of research in NLP. We first provide linguistic background to the notion of event, and then present past attempts to formalize this concept in annotation standards to foster the development of benchmarks for event extraction systems. This ranges from MUC-3 in 1991 to the Time and Space Track challenge at SemEval 2015. Besides, we shed light on other disciplines in which the notion of event plays a crucial role, with a focus on the historical domain. Our goal is to provide a comprehensive study on event definitions and investigate which potential past efforts in the NLP community may have in a different research domain. We present the results of a questionnaire, where the notion of event for historians is put in relation to the NLP perspective

Multilingual processing of temporal expressions: detection and normalisation

Temporal information processing allows us to identify when an event happened, to temporally relate different events and to create timelines with all the extracted information. To achieve these goals, detecting time expressions in a text (e.g. the week of March 6) and normalising them to a standardised value (e.g. a concrete week of a concrete year) is a fundamental step. In this work, we have developed a modular system for time expression processing in English and Spanish, which consists of a Flair sequence-labelling detector based on neural networks and a normaliser built on the TimeNorm synchronous context-free grammar system. Furthermore, we provide an exhaustive study on how we built our Spanish TimeNorm grammar, which is our main contribution and could be useful to adapt the normalisation system to other inflected languages. We evaluated this approach on the TempEval-3 challenge and obtained state-of-the-art results both in detection and normalisation for English and Spanish

ATT1: Temporal Annotation Using Big Windows and Rich Syntactic and Semantic Features

Abstract In this paper we present the results of experiments comparing (a) rich syntactic and semantic feature sets and (b) big context windows, for the TempEval time expression and event segmentation and classification tasks. We show that it is possible for models using only lexical features to approach the performance of models using rich syntactic and semantic feature sets