Deep unification(constraint-)based grammars are usually hand-crafted. Scaling such grammars from fragments to unrestricted text is time-consuming and expensive. This problem can be exacerbated in multilingual broad-coverage grammar development scenarios. Cahill et al. (2002, 2004) and O’Donovan et al. (2004) present an automatic f-structure annotation-based methodology to acquire broad-coverage...

Eye diseases such as diabetic retinopathy and macular edema pose a major threat in today’s world they affect significant portion of the global population. Therefore, it is utmost importance to develop robust solutions that can accurately detect these diseases, especially their early stages. However, current methods, based on hand-crafted features devised by experts, are not sufficiently accurat...

This paper presents a lung nodule detection and classification system which utilizes a combination of hand crafted and deep learning features. Hand crafted features were obtained from modified methods of bag of frequencies, and taxonomic indices. We included a robust radius estimation algorithm that resulted in an average error of 1.29 pixels. Hand crafted features were obtained from 3D low dos...

Emotion recognition from speech may play a crucial role in many applications related to human–computer interaction or understanding the affective state of users in certain tasks, where other modalities such as video or physiological parameters are unavailable. In general, a human’s emotions may be recognized using several modalities such as analyzing facial expressions, speech, physiological pa...

Community Question Answering (cQA) forums have become a popular medium for soliciting direct answers to specific questions of users from experts or other experienced users on a given topic. However, for a given question, users sometimes have to sift through a large number of low-quality or irrelevant answers to find out the answer which satisfies their information need. To alleviate this, the p...

Automatic parallelization and reduction of memory requirement for functional programs have been two active research topics. In this paper, we address both issues simultaneously thanks to the polytope model, a framework originally crafted for the parallelization of imperative programs.

In this work, we investigate several neural network architectures for fine-grained entity type classification. Particularly, we consider extensions to a recently proposed attentive neural architecture and make three key contributions. Previous work on attentive neural architectures do not consider hand-crafted features, we combine learnt and hand-crafted features and observe that they complemen...