A major challenge in paraphrase research is the lack of parallel corpora. In this paper, we present a new method to collect large-scale sentential paraphrases from Twitter by linking tweets through shared URLs. The main advantage of our method is its simplicity, as it gets rid of the classifier or human in the loop needed to select data before annotation and subsequent application of paraphrase identification algorithms in the previous work. We present the largest human-labeled paraphrase corpus to date of 51,524 sentence pairs and the first cross-domain benchmarking for automatic paraphrase identification. In addition, we show that more than 30,000 new sentential paraphrases can be easily and continuously captured every month at 70 precision, and demonstrate their utility for downstream NLP tasks through phrasal paraphrase extraction. We make our code and data freely available.
 We present a new and unique paraphrase resource, which contains meaningpreserving transformations between informal user-generated text. Sentential paraphrases are extracted from a comparable corpus of temporally and topically related messages on Twitter which often express semantically identical information through distinct surface forms. We demonstrate the utility of this new resource on the task of paraphrasing and normalizing noisy text, showing improvement over several state-of-the-art paraphrase and normalization systems 1 . This paper presents PATTY: a large resource for textual patterns that denote binary relations between entities. The patterns are semantically typed and organized into a subsumption taxonomy. The PATTY system is based on efficient algorithms for frequent itemset mining and can process Web-scale corpora. It harnesses the rich type system and entity population of large knowledge bases. The PATTY taxonomy comprises 350,569 pattern synsets. Random-sampling-based evaluation shows a pattern accuracy of 84.7 . PATTY has 8,162 subsumptions, with a random-sampling-based precision of 75 . The PATTY resource is freely available for interactive access and download. In this paper, we propose an unsupervised method for discovering inference rules from text, such as "X is author of Y a X wrote Y", "X solved Y a X found a solution to Y", and "X caused Y a Y is triggered by X". Inference rules are extremely important in many fields such as natural language processing, information retrieval, and artificial intelligence in general. Our algorithm is based on an extended version of Harris' Distributional Hypothesis, which states that words that occurred in the same contexts tend to be similar. Instead of using this hypothesis on words, we apply it to paths in the dependency trees of a parsed corpus.  Compared to the edited genres that have played a central role in NLP research, microblog texts use a more informal register with nonstandard lexical items, abbreviations, and free orthographic variation. When confronted with such input, conventional text analysis tools often perform poorly. Normalization — replacing orthographically or lexically idiosyncratic forms with more standard variants — can improve performance. We propose a method for learning normalization rules from machine translations of a parallel corpus of microblog messages. To validate the utility of our approach, we evaluate extrinsically, showing that normalizing English tweets and then translating improves translation quality (compared to translating unnormalized text) using three standard web translation services as well as a phrase-based translation system trained on parallel microblog data.   We present an approach for automatically learning synonyms from a corpus of paraphrased tweets. The synonyms are learned by using shallow parse chunks to create candidate synonyms and their context windows, and the synonyms are substituted back into a paraphrase detection system that uses machine translation metrics as features for a classifier. We find a 2.29 improvement in F1 when we train and test on the paraphrase training set, demonstrating the importance of discovering high quality synonyms. We also find 9.8 better coverage of the paraphrase corpus using our synonyms rather than larger, existing synonym resources, demonstrating the power of extracting synonyms that are representative of the topics in the test set. In the last few years, the interest of the research community in micro-blogs and social media services, such as Twitter, is growing exponentially. Yet, so far not much attention has been paid on a key characteristic of micro-blogs: the high level of information redundancy. The aim of this paper is to systematically approach this problem by providing an operational definition of redundancy. We cast redundancy in the framework of Textual Entailment Recognition. We also provide quantitative evidence on the pervasiveness of redundancy in Twitter, and describe a dataset of redundancy-annotated tweets. Finally, we present a general purpose system for identifying redundant tweets. An extensive quantitative evaluation shows that our system successfully solves the redundancy detection task, improving over baseline systems with statistical significance.