Monolingual text-to-text generation is an emerging research area in Natural Language Processing. One reason for the interest in such generation systems is the possibility to automatically learn text-to-text generation strategies from aligned monolingual corpora. In this context, paraphrase detection can be seen as the task of aligning sentences that convey the same information but yet are written in different forms, thereby building a training set of rewriting examples. In this paper, we propose a new metric for unsupervised detection of paraphrases and test it over a set of standard paraphrase corpora. The results are promising as they outperform state-of-the-art measures developed for similar tasks.
Thesauri, which list the most salient semantic relations between words, have mostly been compiled manually. Therefore, the inclusion of an entry depends on the subjective decision of the lexicographer. As a consequence, those resources are usually incomplete. In this paper, we propose an unsupervised methodology to automatically discover pairs of semantically related words by highlighting their local environment and evaluating their semantic similarity in local and global semantic spaces. This proposal differs from all other research presented so far as it tries to take the best of two different methodologies, i.e. semantic space models and information extraction models. In particular, it can be applied to extract close semantic relations, it limits the search space to few, highly probable options and it is unsupervised.
In this paper, we present a study for extracting and aligning paraphrases in the context of Sentence Compression. First, we justify the application of a new measure for the automatic extraction of paraphrase corpora. Second, we discuss the work done by (Barzilay & Lee, 2003) who use clustering of paraphrases to induce rewriting rules. We will see, through classical visualization methodologies (Kruskal & Wish, 1977) and exhaustive experiments, that clustering may not be the best approach for automatic pattern identification. Finally, we will provide some results of different biology based methodologies for pairwise paraphrase alignment.
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