Paraphrases extracted from parallel corpora by the pivot method (Bannard and Callison-Burch, 2005) constitute a valuable resource for multilingual NLP applications. In this study, we analyse the semantics of unigram pivot paraphrases and use a graph-based sense induction approach to unveil hidden sense distinctions in the paraphrase sets. The comparison of the acquired senses to gold data from ...

The paper presents a new approach to the problem of paraphrase identification. The new approach extends a previously proposed method for the task of textual entailment. The relationship between paraphrases and entailment is discussed to theoretically justify the new approach. The proposed approach is useful because it uses relatively few resources compared to similar systems yet it produces res...

We present a work to evaluate the hypothesis that automatic evaluation metrics developed for Machine Translation (MT) systems have significant impact on predicting semantic similarity scores in Semantic Textual Similarity (STS) task for English, in light of their usage for paraphrase identification. We show that different metrics may have different behaviors and significance along the semantic ...

Paraphrase generation is an important task that has received a great deal of interest recently. Proposed data-driven solutions to the problem have ranged from simple approaches that make minimal use of NLP tools to more complex approaches that rely on numerous language-dependent resources. Despite all of the attention, there have been very few direct empirical evaluations comparing the merits o...

Previous work on Automatic Paraphrase Identification (PI) is mainly based on modeling text similarity between two sentences. In contrast, we study methods for automatically detecting whether a text fragment only appearing in a sentence of the evaluated sentence pair is important or ancillary information with respect to the paraphrase identification task. Engineering features for this new task i...

We present an approach to identifying Twitter paraphrases using simple lexical overlap features. The work is part of ongoing research into the applicability of knowledgelean techniques to paraphrase identification. We utilize features based on overlap of word and character n-grams and train support vector machine (SVM). Our results demonstrate that character and word level overlap features in c...

The lack of readily-available large corpora of aligned monolingual sentence pairs is a major obstacle to the development of Statistical Machine Translation-based paraphrase models. In this paper, we describe the use of annotated datasets and Support Vector Machines to induce larger monolingual paraphrase corpora from a comparable corpus of news clusters found on the World Wide Web. Features inc...

We describe a system which ranks humanprovided paraphrases of noun compounds, where the frequency with which a given paraphrase was provided by human volunteers is the gold standard for ranking. Our system assigns a score to a paraphrase of a given compound according to the number of times it has co-occurred with other paraphrases in the rest of the dataset. We use these co-occurrence statistic...