Caroline Sporleder

Learn More
We describe the SemEval-2010 shared task on “Linking Events and Their Participants in Discourse”. This task is an extension to the classical semantic role labeling task. While semantic role labeling is traditionally viewed as a sentence-internal task, local semantic argument structures clearly interact with each other in a larger context, e.g., by sharing(More)
This paper describes the Question Answering for Machine Reading (QA4MRE) task at the 2012 Cross Language Evaluation Forum. In the main task, systems answered multiple-choice questions on documents concerned with four different topics. There were also two pilot tasks, Processing Modality and Negation for Machine Reading, and Machine Reading on Biomedical(More)
Being able to identify which rhetorical relations (e.g., contrast or explanation) hold between spans of text is important for many natural language processing applications. Using machine learning to obtain a classifier which can distinguish between different relations typically depends on the availability of manually labelled training data, which is very(More)
This paper presents a probabilistic model for sense disambiguation which chooses the best sense based on the conditional probability of sense paraphrases given a context. We use a topic model to decompose this conditional probability into two conditional probabilities with latent variables. We propose three different instantiations of the model for solving(More)
We propose an unsupervised method for distinguishing literal and non-literal usages of idiomatic expressions. Our method determines how well a literal interpretation is linked to the overall cohesive structure of the discourse. If strong links can be found, the expression is classified as literal, otherwise as idiomatic. We show that this method can help to(More)
We propose a joint model for unsupervised induction of sentiment, aspect and discourse information and show that by incorporating a notion of latent discourse relations in the model, we improve the prediction accuracy for aspect and sentiment polarity on the sub-sentential level. We deviate from the traditional view of discourse, as we induce types of(More)
In this paper we investigate whether paragraphs can be identified automatically in different languages and domains. We propose a machine learning approach which exploits textual and discourse cues and we assess how well humans perform on this task. Our best models achieve an accuracy that is significantly higher than the best baseline and, for most data(More)
We examine the problem of acoustic emanations of printers. We present a novel attack that recovers what a dotmatrix printer processing English text is printing based on a record of the sound it makes, if the microphone is close enough to the printer. In our experiments, the attack recovers up to 72 % of printed words, and up to 95 % if we assume contextual(More)
objects can be assertions, beliefs, facts, or eventualities. Discourse connectives and their arguments are assigned attribution-related features (Prasad et al. 2006) such as SOURCE (writer, other, arbitrary), TYPE (reflecting the nature of the relation between the agent and the abstract object), SCOPAL POLARITY of attribution, and DETERMINACY (indicating(More)