We address the text-to-text generation problem of sentence-level paraphrasing — a phenomenon distinct from and more difficult than word- or phrase-level paraphrasing. Our appro...
We investigate incremental word learning with few training examples in a Hidden Markov Model (HMM) framework suitable for an interactive learning scenario with little prior knowle...
While paraphrasing is critical both for interpretation and generation of natural language, current systems use manual or semi-automatic methods to collect paraphrases. We present ...
This paper presents a lightweight method for unsupervised extraction of paraphrases from arbitrary textual Web documents. The method differs from previous approaches to paraphrase...
We describe a novel approach to unsupervised learning of the events that make up a script, along with constraints on their temporal ordering. We collect naturallanguage descriptio...
Michaela Regneri, Alexander Koller, Manfred Pinkal