This paper presents a two-stage approach to summarizing multiple contrastive viewpoints in opinionated text. In the first stage, we use an unsupervised probabilistic approach to m...
This paper studies the effects of training data on binary text classification and postulates that negative training data is not needed and may even be harmful for the task. Tradit...
Inducing a grammar directly from text is one of the oldest and most challenging tasks in Computational Linguistics. Significant progress has been made for inducing dependency gram...
Part-of-speech (POS) tag distributions are known to exhibit sparsity -- a word is likely to take a single predominant tag in a corpus. Recent research has demonstrated that incorp...
The problem of automatically classifying the gender of a blog author has important applications in many commercial domains. Existing systems mainly use features such as words, wor...
While a significant amount of research has been devoted to textual entailment, automated entailment from conversational scripts has received less attention. To address this limita...
Several recent discourse parsers have employed fully-supervised machine learning approaches. These methods require human annotators to beforehand create an extensive training corp...
Hugo Hernault, Danushka Bollegala, Mitsuru Ishizuk...
We show that the standard beam-search algorithm can be used as an efficient decoder for the global linear model of Zhang and Clark (2008) for joint word segmentation and POS-taggi...
We present a novel approach for (written) dialect identification based on the discriminative potential of entire words. We generate Swiss German dialect words from a Standard Germ...
This paper addresses the problem of learning to map sentences to logical form, given training data consisting of natural language sentences paired with logical representations of ...
Tom Kwiatkowksi, Luke S. Zettlemoyer, Sharon Goldw...