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We argue that coherence relations (relations between propositions, such as Concession or Purpose) are signalled more frequently and by more means than is generally believed. We examine how coherence relations in text are indicated by all possible textual signals, and whether every relation is signalled. To that end, we conducted a corpus study on the RST Discourse Treebank, a corpus of newspaper articles annotated for rhetorical (or coherence) relations. Results from our corpus study show that most relations in text (over 90%) are signalled and also that most signalled relations (over 80%) are indicated not only by discourse markers (and, but, if, since), but also by a wide variety of signals other than discourse markers, such as reference, lexical, semantic, syntactic and graphical features. These findings suggest that signalling of coherence relations is much more sophisticated than previously thought.
We present a lexicon-based approach to extracting sentiment from text. The Semantic Orientation CALculator (SO-CAL) uses dictionaries of words annotated with their semantic orientation (polarity and strength), and incorporates intensification and negation. SO-CAL is applied to the polarity classification task, the process of assigning a positive or negative label to a text that captures the text's opinion towards its main subject matter. We show that SO-CAL's performance is consistent across domains and on completely unseen data. Additionally, we describe the process of dictionary creation, and our use of Mechanical Turk to check dictionaries for consistency and reliability.