A New Russian Paraphrase Corpus. Paraphrase Identification and Classification Based on Different Prediction Models

摘要

Our main objectives are constructing a paraphrase corpus for Russian and developing of the paraphrase identification and classification models based on this corpus. The corpus consists of pairs of news headlines from different media agencies which are extracted and analyzed in real time. Paraphrase candidates are extracted using an unsupervised matrix similarity metric: if the metric value satisfies a certain threshold, the corresponding pair of sentences is included in the corpus. These pairs of sentences are further annotated via crowdsourcing. We provide a user-friendly online interface for crowdsourced annotation which is available at http://paraphraser.ru. There are 7480 annotated sentence pairs in the corpus at the moment, and there are still more to come. The types and the features of these sentence pairs are not introduced to the annotators. We adopt a 3-classes classification of paraphrases and distinguish precise paraphrases (conveying the same meaning), loose paraphrases (conveying similar meaning) and non-paraphrases (conveying different meaning).