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.

natural language generation

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 very 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. In this thesis, we focus on paraphrase and textual entailment recognition, as well as paraphrase generation. We propose three paraphrase and textual entailment recognition methods, experimentally evaluated on existing benchmarks. The key idea is that by capturing similarities at various abstractions of the inputs, we can recognize paraphrases and textual entailment reasonably well. Additionally, we exploit WordNet and use features that operate on the syntactic level of the language expressions. The best

Monolingual alignment is frequently required for natural language tasks that involve similar or comparable sentences. We present a new model for monolingual alignment in which the score of an alignment decomposes over both the set of aligned phrases as well as a set of aligned dependency arcs. Optimal alignments under this scoring function are decoded using integer linear programming while model parameters are learned using standard structured prediction approaches. We evaluate our joint aligner on the Edinburgh paraphrase corpus and show significant gains over a Meteor baseline and a state-of-the-art phrase-based aligner. TITLE AND ABSTRACT IN FRENCH Un modèle de phrases et de dépendances pour l’alignement des paraphrases L’alignement monolingue s’impose fréquemment dans les tâches de langue naturelle qui comprennent des phrases similaires. Nous présentons un nouveau modèle pour l’alignement monolingue dans lequel le score d’un alignement tient compte de l’ensemble de phrases alignées et d’un ensemble d’arcs de dépendance alignés. Cette fonction de score donne des alignements en utilisant l’optimisation linéaire, et nous effectuons l’apprentissage des paramètres du modèle avec des méthodes standardes de prédiction structurée. Nous évaluons notre système mixte par rapport au corpus de paraphrases d’Edinburgh et nous démonstron un avantage significatif par rapport á Meteor et á un système de pointe fondé sur l’alignement des phrases.