Description: This paper discusses learning to identify emotions in text. Abstract: This paper describes experiments concerned with the automatic analysis of emotions in text. We describe the construction of a large data set annotated for six basic emotions: Anger, Disgust, Fear, Joy, Sadness, and Surprise, and we propose and evaluate several knowledge-based and corpus-based methods for the automatic identification of these emotions in text.

Description: This paper discusses explorations in the automatic recognition of deceptive language. Abstract: In this paper, we present initial experiments in the recognition of deceptive language. We introduce three data sets of true and lying texts collected for this purpose, and the authors show that automatic classification is a viable technique to distinguish between truth and falsehood as expressed in language. We also introduce a method for class-based feature analysis, which sheds some light on the features that are characteristic for deceptive text.

Description: This paper discusses linguistically motivated features for enhanced back-of-the-book indexing. Abstract: In this paper, we present a supervised method for back-of-the-book index construction. We introduce a novel set of features that goes beyond the typical frequency-based analysis, including features based on discourse comprehension, syntactic patterns, and information drawn from an online encyclopedia. In experiments carried out on a book collection, the method was found to lead to an improvement of roughly 140% as compared to an existing state-of-the-art supervised method.

Description: This paper discusses a logic programming framework for semantic interpretation with WordNet and PageRank. Abstract: This paper describes applications of Logic Programming to Natural Language processing in combination with graph-algorithms and statistical methods. Google's PageRank and similar fast-converging recursive graph algorithms have provided practical means to statistically rank vertices of large graphs like the World Wide Web. By combining a fast Java-based PageRank implementation with a Prolog base inferential layer, running on top of an optimized WordNet graph, the authors describe applications to word sense disambiguation and evaluate their accuracy in comparison with human annotated corpus data.

Description: This paper discusses the main lines of research in deriving efficient Word Sense Disambiguation. Abstract: In the past few years, we have witnessed a tremendous growth of the World Wide Web, both in terms of number of Web pages accessible online - resulting in what represents today the largest publicly available corpus, and in terms of number of Web users - who now these two main dimensions - pages and users - has opened the doors to a realm of new approaches to data-hungry and knowledge-hungry language processing applications. Among these, Word Sense Disambiguation is one of the applications that has the potential of benefiting the most from the large amounts of Web-based data and from the availability of inexpensive Web user supervision. In this paper, the author discusses the main lines of research in deriving efficient Word Sense Disambiguation methods that exploit: (1) the Web as a corpus - which represents a view of the Web seen as an enormous collection of Web pages; and (2) the Web as collective mind - where the Web is regarded as a large group of Web users who can contribute their knowledge to the process of identifying word meanings.