This paper introduces GAF, a grounded annotation framework to represent events in a formal context that can represent information from both textual and extra-textual sources. GAF makes a clear distinction between mentions of events in text and their formal representation as instances in a semantic layer. Instances are represented by RDF compliant URIs that are shared across different research d...

This paper introduces a flexible and generic approach to define customised style rules for labels in conceptual models. A rule-based language is presented which can express style rules in a flexible and context-specific way. The formalised style rules are used to analyse and evaluate textual labels of model elements. To analyse a textual label, a combination of standardised natural language pro...

This work presents a recommender system that analyzes textual messages sent during a communication session in a private Web chat, identifies the context of each message and recommends items from a Digital Library. Recommendations are directly made to users in the chat screen and are decided by a software system through a proactive paradigm, without any request of the users. A domain ontology, c...

In this paper, we discuss different methods which use meta information and richer context that may accompany source language input to improve machine translation quality. We focus on category information of input text as meta information, but the proposed methods can be extended to all textual and non-textual meta information that might be available for the input text or automatically predicted...

This study investigates the degree to which textual complexity indices applied on students’ online contributions, corroborated with a longitudinal anal‐ ysis performed on their weekly posts, predict academic performance. The source of student writing consists of blog and microblog posts, created in the context of a project-based learning scenario run on our eMUSE platform. Data is collected fro...

This paper describes our experiments on Textual Entailment in the context of the Fourth Recognising Textual Entailment (RTE-4) Evaluation Challenge at TAC 2008 contest. Our system uses a Machine Learning approach with AdaBoost to deal with the RTE challenge. We perform a lexical, syntactic, and semantic analysis of the entailment pairs. From this information we compute a set of semantic-based d...

we present an eye-tracking study in the context of Massive open online Course (MooC) videos. we propose to use a pre-test as a way of priming students about the video content before they watch the video. In this study, we used two versions of the same pre-test: textual and schema based. The results show that priming has an effect on the learning gain as well as the gaze patterns of students as ...

The paper introduces a concept of attributed context-sensitive graph grammars. The graph grammars are a graphical generalization of the textual grammars and can thus be used to specify the syntax of graphical programming or modeling languages. The attributed graph grammars extend the basic graph grammars with definitions of attributes and the associated attribute evaluation rules. By analogy to...

In this work we investigate methods to enable the detection of a specific type of textual entailment (strict entailment), starting from the preliminary assumption that these relations are often clearly expressed in texts. Our method is a statistical approach based on what we call textual entailment patterns, prototypical sentences hiding entailment relations among two activities. We experimente...