Vanessa Wei Feng

A Linear-Time Bottom-Up Discourse Parser with Constraints and Post-Editing

Text-level discourse parsing remains a challenge. The current state-of-the-art overall accuracy in relation assignment is 55.73%, achieved by Joty et al. (2013). However, their model has a high order of time complexity, and thus cannot be applied in practice. In this work, we develop a much faster model whose time complexity is linear in the number of sentences. Our model adopts a greedy bottom-up approach, with two linear-chain CRFs applied in cascade as local classifiers. To enhance the accuracy of the pipeline, we add additional constraints in the Viterbi decoding of the first CRF. In addition to efficiency, our parser also significantly outperforms the state of the art. Moreover, our novel approach of post-editing, which modifies a fully-built tree by considering information from constituents on upper levels, can further improve the accuracy.

Changes in Style in Authors with Alzheimer's Disease

Even in its very early stages, Alzheimers disease leads to changes in language that can be detected by computational analysis. These changes may include a reduced, vaguer, and more abstract vocabulary, and reduced syntactic complexity. But do these changes affect an authors essential style? We experiment with a number of standard features for authorship attribution and authorship verification to see whether they recognize late works written by authors known to have had Alzheimers disease as being by the same author as their earlier works. The authors whom we study are Iris Murdoch and Agatha Christie. Our control author (without Alzheimers) is P. D. James. Our results were equivocal, as different frameworks yielded contrary results, but an SVM classifier was able to make age discriminations, or nearly so, for all three authors, thereby casting doubt on the underlying axiom that an authors essential style is invariant in the absence of cognitive decline.

Patterns of local discourse coherence as a feature for authorship attribution

We define a model of discourse coherence based on Barzilay and Lapata’s entity grids as a stylometric feature for authorship attribution. Unlike standard lexical and character-level features, it operates at a discourse (cross-sentence) level. We test it against and in combination with standard features on nineteen booklength texts by nine nineteenth-century authors. We find that coherence alone performs often as well as and sometimes better than standard features, though a combination of the two has the highest performance overall. We observe that despite the difference in levels, there is a correlation in performance of the two kinds of features.