Stochastic text generation

Text Classification Using Stochastic Keyword Generation

This paper considers improving the performance of text classification, when summaries of the texts, as well as the texts themselves, are available during learning. Summaries can be more accurately classified than texts, so the question is how to effectively use the summaries in learning. This paper proposes a new method for addressing the problem, using a technique referred to as ’stochastic ke...

Text Generation

Text Generation

This repor t consists of two documents describing the state of the art of computer generat ion of natural language text. Both were prepared by a panel of individuals who are active in research on text generation. The first document assesses the state of the art, identifying four kinds of technical developments which will shape the art in the coming decade: linguistically justified grammars , kn...

Text-to-text generation for question answering

When answering questions, major challenges are (a) to carefully determine the content of the answer and (b) phrase it in a proper way. In IMIX, we focus on two text-to-text generation techniques to accomplish this: content selection and sentence fusion. Using content selection, we can extend answers to an arbitrary length, providing not just a direct answer but also related information so to be...

Text Generation from Keywords

We describe a method for generating sentences from “keywords” or “headwords”. This method consists of two main parts, candidate-text construction and evaluation. The construction part generates text sentences in the form of dependency trees by using complementary information to replace information that is missing because of a “knowledge gap” and other missing function words to generate natural ...