This paper proposes a method to correct English verb form errors made by non-native speakers. A basic approach is template matching on parse trees. The proposed method improves on...
We propose a language model based on a precise, linguistically motivated grammar (a hand-crafted Head-driven Phrase Structure Grammar) and a statistical model estimating the proba...
People rarely articulate explicitly what a native speaker of a language is already assumed to know. So to acquire the stereotypical knowledge that underpins much of what is said i...
Discriminative feature-based methods are widely used in natural language processing, but sentence parsing is still dominated by generative methods. While prior feature-based dynam...
Jenny Rose Finkel, Alex Kleeman, Christopher D. Ma...
This paper demonstrates a new method for leveraging unstructured annotations to infer semantic document properties. We consider the domain of product reviews, which are often anno...
S. R. K. Branavan, Harr Chen, Jacob Eisenstein, Re...
We present a novel semi-supervised training algorithm for learning dependency parsers. By combining a supervised large margin loss with an unsupervised least squares loss, a discr...
Large-scale discriminative machine translation promises to further the state-of-the-art, but has failed to deliver convincing gains over current heuristic frequency count systems....
We present a novel approach to weakly supervised semantic class learning from the web, using a single powerful hyponym pattern combined with graph structures, which capture two pr...
We extend the classical single-task active learning (AL) approach. In the multi-task active learning (MTAL) paradigm, we select examples for several annotation tasks rather than f...
Roi Reichart, Katrin Tomanek, Udo Hahn, Ari Rappop...