Paraphrase Recognition is a task of growing interest in the natural language process (NLP) research during the last years. This task aims to detect if two sentences have the same meaning. Paraphrase relationship described in this work is not the definition given by the common knowledge, but is more natural language oriented since it is driven by a lot of background human knowledge. This type of...

The task of paraphrasing is inherently familiar to speakers of all languages. Moreover, the task of automatically generating or extracting semantic equivalences for the various units of language— words, phrases, and sentences—is an important part of natural language processing (NLP) and is being increasingly employed to improve the performance of several NLP applications. In this article, we at...

This paper describes an approach for paraphrase detection in Malayalam sentences developed as part of FIRE 2016 Shared Task on Paraphrase detection in Indian Languages. The task of paraphrasedetection is finding a sentence with the same meaning of another sentence expressed using same or different words. This detection is done by a semantic approach which is language dependent. Individual words...

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of word-level embeddings is very well studied, novel This obtained by simple in context solving paraphrase generation task. If use sequential encoder-decoder model generating paraphrase, would like generated to be semantically close original sentence. One way ensure adding constraints true and unrelate...

Paraphrase plagiarism is a significant and widespread problem and research shows that it is hard to detect. Several methods and automatic systems have been proposed to deal with it. However, evaluation and comparison of such solutions is not possible because of the unavailability of benchmark corpora with manual examples of paraphrase plagiarism. To deal with this issue, we present the novel de...

We treat paraphrase identification as an ordering task. We construct a corpus of 250 sets of five sentences, with each set containing a reference sentence and four paraphrase candidates, which are annotated on a scale of 1 to 5 for paraphrase proximity. We partition this corpus into 1000 pairs of sentences in which the first is the reference sentence and the second is a paraphrase candidate. We...

Previous work on paraphrase extraction using parallel or comparable corpora has generally not considered the documents’ discourse structure as a useful information source. We propose a novel method for collecting paraphrases relying on the sequential event order in the discourse, using multiple sequence alignment with a semantic similarity measure. We show that adding discourse information boos...

The goal of paraphrase identification is to decide whether two given text fragments have the same meaning. Of particular interest in this area is the identification of paraphrases among short texts, such as SMS and Twitter. In this paper, we present idiomatic expressions as a new domain for short-text paraphrase identification. We propose a technique, utilizing idiom definitions and continuous ...

This paper presents a novel approach to the problem of paraphrase identification. Although paraphrases often make use of synonymous or near synonymous terms, many previous approaches have either ignored or made limited use of information about similarities between word meanings. We present an algorithm for paraphrase identification which makes extensive use of word similarity information derive...

We propose a novel sentential paraphrase acquisition method. To build a wellbalanced corpus for Paraphrase Identification, we especially focus on acquiring both non-trivial positive and negative instances. We use multiple machine translation systems to generate positive candidates and a monolingual corpus to extract negative candidates. To collect nontrivial instances, the candidates are unifor...