Classification of Futuristic Technologies Described in Speculative Fiction Novels

Speculative fiction works of literature are full of unreal futuristic technologies meant to amuse and interest the reader in a potential or alternate future. Some readings of such novels occasionally reveal that through time and the progression of technology in the real world, we have achieved some of the ideas put forth in these works of fiction. To properly prepare for a world of changing and developing technology, it is of great interest to use these fictional ideas to potentially predict the advancement of technology in real life. This project aims to create a machine learning system to analyze text passages introducing such futuristic technologies and classify them into categories related to their features and usage

Proposition-based summarization with a coherence-driven incremental model

Summarization models which operate on meaning representations of documents have been neglected in the past, although they are a very promising and interesting class of methods for summarization and text understanding. In this thesis, I present one such summarizer, which uses the proposition as its meaning representation. My summarizer is an implementation of Kintsch and van Dijk's model of comprehension, which uses a tree of propositions to represent the working memory. The input document is processed incrementally in iterations. In each iteration, new propositions are connected to the tree under the principle of local coherence, and then a forgetting mechanism is applied so that only a few important propositions are retained in the tree for the next iteration. A summary can be generated using the propositions which are frequently retained. Originally, this model was only played through by hand by its inventors using human-created propositions. In this work, I turned it into a fully automatic model using current NLP technologies. First, I create propositions by obtaining and then transforming a syntactic parse. Second, I have devised algorithms to numerically evaluate alternative ways of adding a new proposition, as well as to predict necessary changes in the tree. Third, I compared different methods of modelling local coherence, including coreference resolution, distributional similarity, and lexical chains. In the first group of experiments, my summarizer realizes summary propositions by sentence extraction. These experiments show that my summarizer outperforms several state-of-the-art summarizers. The second group of experiments concerns abstractive generation from propositions, which is a collaborative project. I have investigated the option of compressing extracted sentences, but generation from propositions has been shown to provide better information packaging