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

Annotation des informations temporelles dans des textes en français.

National audienceLe traitement des informations temporelles est crucial pour la compréhension de textes en langue naturelle. Le langage de spécification TimeML a été conçu afin de permettre le repérage et la normalisation des expressions temporelles et des événements dans des textes écrits en anglais. L'objectif des divers projets TimeML a été de formuler un schéma d'annotation pouvant s'appliquer à du texte libre, comme ce que l'on trouve sur le Web, par exemple. Des efforts ont été faits pour l'application de TimeML à d'autres langues que l'anglais, notamment le chinois, le coréen, l'italien, l'espagnol et l'allemand. Pour le français, il y a eu des efforts allant dans ce sens, mais ils sont encore un peu éparpillés. Dans cet article, nous détaillons nos travaux actuels qui visent à élaborer des ressources complètes pour l'annotation de textes en français selon TimeML - notamment un guide d'annotation, un corpus de référence (Gold Standard) et des modules d'annotation automatique

Annotation des informations temporelles dans des textes en français.

National audienceLe traitement des informations temporelles est crucial pour la compréhension de textes en langue naturelle. Le langage de spécification TimeML a été conçu afin de permettre le repérage et la normalisation des expressions temporelles et des événements dans des textes écrits en anglais. L'objectif des divers projets TimeML a été de formuler un schéma d'annotation pouvant s'appliquer à du texte libre, comme ce que l'on trouve sur le Web, par exemple. Des efforts ont été faits pour l'application de TimeML à d'autres langues que l'anglais, notamment le chinois, le coréen, l'italien, l'espagnol et l'allemand. Pour le français, il y a eu des efforts allant dans ce sens, mais ils sont encore un peu éparpillés. Dans cet article, nous détaillons nos travaux actuels qui visent à élaborer des ressources complètes pour l'annotation de textes en français selon TimeML - notamment un guide d'annotation, un corpus de référence (Gold Standard) et des modules d'annotation automatique

Extracting Temporal and Causal Relations between Events

Structured information resulting from temporal information processing is crucial for a variety of natural language processing tasks, for instance to generate timeline summarization of events from news documents, or to answer temporal/causal-related questions about some events. In this thesis we present a framework for an integrated temporal and causal relation extraction system. We first develop a robust extraction component for each type of relations, i.e. temporal order and causality. We then combine the two extraction components into an integrated relation extraction system, CATENA---CAusal and Temporal relation Extraction from NAtural language texts---, by utilizing the presumption about event precedence in causality, that causing events must happened BEFORE resulting events. Several resources and techniques to improve our relation extraction systems are also discussed, including word embeddings and training data expansion. Finally, we report our adaptation efforts of temporal information processing for languages other than English, namely Italian and Indonesian.Comment: PhD Thesi

Processing temporal information in unstructured documents

Tese de doutoramento, Informática (Ciência da Computação), Universidade de Lisboa, Faculdade de Ciências, 2013Temporal information processing has received substantial attention in the last few years, due to the appearance of evaluation challenges focused on the extraction of temporal information from texts written in natural language. This research area belongs to the broader field of information extraction, which aims to automatically find specific pieces of information in texts, producing structured representations of that information, which can then be easily used by other computer applications. It has the potential to be useful in several applications that deal with natural language, given that many languages, among which we find Portuguese, extensively refer to time. Despite that, temporal processing is still incipient for many language, Portuguese being one of them. The present dissertation has various goals. On one hand, it addresses this current gap, by developing and making available resources that support the development of tools for this task, employing this language, and also by developing precisely this kind of tools. On the other hand, its purpose is also to report on important results of the research on this area of temporal processing. This work shows how temporal processing requires and benefits from modeling different kinds of knowledge: grammatical knowledge, logical knowledge, knowledge about the world, etc. Additionally, both machine learning methods and rule-based approaches are explored and used in the development of hybrid systems that are capable of taking advantage of the strengths of each of these two types of approach.O processamento de informação temporal tem recebido bastante atenção nos últimos anos, devido ao surgimento de desafios de avaliação focados na extração de informação temporal de textos escritos em linguagem natural. Esta área de investigação enquadra-se no campo mais lato da extração de informação, que visa encontrar automaticamente informação específica presente em textos, produzindo representações estruturadas da mesma, que podem depois ser facilmente utilizadas por outras aplicações computacionais. Tem o potencial de ser útil em diversas aplicações que lidam com linguagem natural, dado o caráter quase ubíquo da referência ao tempo cronólogico em muitas línguas, entre as quais o Português. Apesar de tudo, o processamento temporal encontra-se ainda incipiente para bastantes línguas, sendo o Português uma delas. A presente dissertação tem vários objetivos. Por um lado vem colmatar esta lacuna existente, desenvolvendo e disponibilizando recursos que suportam o desenvolvimento de ferramentas para esta tarefa, utilizando esta língua, e desenvolvendo também precisamente este tipo de ferramentas. Por outro serve também para relatar resultados importantes da pesquisa nesta área do processamento temporal. Neste trabalho, mostra- -se como o processamento temporal requer e beneficia da modelação de conhecimento de diversos níveis: gramatical, lógico, acerca do mundo, etc. Adicionalmente, são explorados tanto métodos de aprendizagem automática como abordagens baseadas em regras, desenvolvendo-se sistemas híbridos capazes de tirar partido das vantagens de cada um destes dois tipos de abordagem.Fundação para a Ciência e a Tecnologia (FCT, SFRH/BD/40140/2007