Predictive Performance

We describe a set of supervised ma- chine learning experiments centering on the construction of statistical mod- els of WH-questions. These models, which are built from shallow linguis- tic features …...

We propose an efficient way to train maximum entropy language models (MELM) and neural network language models (NNLM). The ad- vantage of the proposed method comes from a more robust …...

We present novel methods to construct com- pact natural language lexicons within a graph- based semi-supervised learning framework, an attractive platform suited for propagating soft labels onto new natural language …...

Context is crucial for identifying argumen- tative relations in text, but many argument mining methods make little use of contex- tual features. This paper presents context- aware argumentative relation mining …...

AbstractWe present a joint modeling approach to identify salient discussion points in spoken meetings as well as to label the discourse relations between speaker turns. A variation of our model …...

AbstractAttention mechanisms have seen wide adoption in neural NLP models. In addition to improving predictive performance, these are often touted as affording transparency: models equipped with attention provide a distribution …...

AbstractMachine learning models depend on the quality of input data. As electronic health records are widely adopted, the amount of data in health care is growing, along with complaints about …...

AbstractIn human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within …...

AbstractBill writing is a critical element of representative democracy. However, it is often overlooked that most legislative bills are derived, or even directly copied, from other bills. Despite the significance …...

AbstractMeasuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of “who did what to whom” micro-records …...

AbstractDropout is a widely used regularization trick to resolve the overfitting issue in large feedforward neural networks trained on a small dataset, which performs poorly on the held-out test subset. …...