A Survey of Paraphrasing and Textual Entailment Methods

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of Informatics, Athens University of Economics and Business, Greece, 201

Automatic extraction of paraphrastic phrases from medium size corpora

This paper presents a versatile system intended to acquire paraphrastic phrases from a representative corpus. In order to decrease the time spent on the elaboration of resources for NLP system (for example Information Extraction, IE hereafter), we suggest to use a machine learning system that helps defining new templates and associated resources. This knowledge is automatically derived from the text collection, in interaction with a large semantic network

Bootstrapping Lexical Choice via Multiple-Sequence Alignment

An important component of any generation system is the mapping dictionary, a lexicon of elementary semantic expressions and corresponding natural language realizations. Typically, labor-intensive knowledge-based methods are used to construct the dictionary. We instead propose to acquire it automatically via a novel multiple-pass algorithm employing multiple-sequence alignment, a technique commonly used in bioinformatics. Crucially, our method leverages latent information contained in multi-parallel corpora -- datasets that supply several verbalizations of the corresponding semantics rather than just one. We used our techniques to generate natural language versions of computer-generated mathematical proofs, with good results on both a per-component and overall-output basis. For example, in evaluations involving a dozen human judges, our system produced output whose readability and faithfulness to the semantic input rivaled that of a traditional generation system.Comment: 8 pages; to appear in the proceedings of EMNLP-200

Paraphrastic Reformulations in Spoken Corpora

International audienceOur work addresses the automatic detection of paraphrastic reformulation in French spoken corpora. The proposed approach is syn-tagmatic. It is based on specific markers and the specificities of the spoken language. Manual multi-dimensional annotation performed by two annotators provides fine-grained reference data. An automatic method is proposed in order to decide whether sentences contain or not paraphras-tic relations. The obtained results show up to 66.4% precision. Analysis of the manual annotations indicates that few paraphrastic segments show morphological modifications (inflection, derivation or compounding) and that the syntactic equivalence between the segments is seldom respected, as these usually belong to different syntactic categories