We present a cross-lingual projection framework for temporal annotations. Automatically obtained TimeML annotations in the English portion of a parallel corpus are transferred to the German translation along a word alignment. Direct projection augmented with shallow heuristic knowledge outperforms the uninformed baseline by 6.64 % F1-measure for events, and by 17.93 % for time expressions. Subsequent training of statistical classifiers on the (imperfect) projected annotations significantly boosts precision by up to 31 % to 83.95 % and 89.52%, respectively. 1

We present SlinkET, a parser for identifying contexts of event modality in text developed within the TARSQI (Temporal Awareness and Reasoning Systems for Question Interpretation) research framework. SlinkET is grounded on TimeML, a specification language for capturing temporal and event related information in discourse, which provides an adequate foundation to handle event modality. SlinkET builds on top of a robust event recognizer, and provides each relevant event with a value that specifies the degree of certainty about its factuality; e.g., whether it has happened or holds (factive or counter-factive), whether it is being reported or witnessed by somebody else (evidential), or if it is introduced as a possibility (modal). It is based on well-established technology in the field (namely, finite-state techniques), and informed with corpus-induced knowledge that relies on basic information, such as morphological features, POS, and chunking. SlinkET is under continuing development and it currently achieves a performance ratio of 70 % F1-measure. 1.

In this paper, we present a linguistic resource that annotates event structures in texts. We consider an event structure as a collection of events that interact with each other in a given situation. We interpret the interactions between events as event relations. In this regard, we propose and annotate a set of six relations that best capture the concept of event structure. These relations are: subevent, reason, purpose, enablement, precedence and related. A document from this resource can encode multiple event structures and an event structure can be described across multiple documents. In order to unify event structures, we also annotate inter- and intra-document event coreference. Moreover, we provide methodologies for automatic discovery of event structures from texts. First, we group the events that constitute an event structure into event clusters and then, we use supervised learning frameworks to classify the relations that exist between events from the same cluster. 1. The Problem Text and human communication abounds with reference to events and their interrelations. Events can determine other events or states, they may culminate in accomplishments or they may cause states to terminate. In texts, events are commonly described by verbs or nominalizations. But verbal

This paper reports on the participation of University of Wolverhampton and University of Alicante at the SemEval-2007 TempEval evaluation exercise. TempEval consisted of three tasks involving the identification of event-time and event-event temporal relations. We participated in all three tasks with TICTAC (Syntactico-Semantic Temporal Annotation Cluster), a system comprising both knowledge based and statistical techniques. Our system achieved the highest strict and relaxed scores for tasks A and B, and the highest relaxed score for task C. 1

This paper presents the annotation guidelines and specifications which have been developed for the creation of the Italian TimeBank, a language resource composed of two corpora manually annotated with temporal and event information. In particular, the adaptation of the TimeML scheme to Italian is described, and a special attention is given to the methodology used for the realization of the annotation specifications, which are strategic in order to create good quality annotated resources and to justify the annotated