Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical texts and classify them into predefined categories. State-of-the-art DNR approaches heavily rely on hand-crafted features and domain-specific resources which are difficult to collect and tune. For this reason, this paper investigates the effecti...

The recent progress in sparse coding and deep learning has made unsupervised feature learning methods a strong competitor to hand-crafted descriptors. In computer vision, success stories of learned features have been predominantly reported for object recognition tasks. In this paper, we investigate if and how feature learning can be used for material recognition. We propose two strategies to in...

In this paper we present a lightweight stemmer for Gujarati using a hybrid approach. Instead of using a completely unsupervised approach, we have harnessed linguistic knowledge in the form of a hand-crafted Gujarati suffix list in order to improve the quality of the stems and suffixes learnt during the training phase. We used the EMILLE corpus for training and evaluating the stemmer’s performan...

Techniques for automatically training modules of a natural language generator have recently been proposed, but a fundamental concern is whether the quality of utterances produced with trainable components can compete with hand-crafted template-based or rulebased approaches. In this paper We experimentally evaluate a trainable sentence planner for a spoken dialogue system by eliciting subjective...

At RoboCup, teams of autonomous robots or software softbots compete in simulated soccer matches to demonstrate cooperative robotics techniques in a very difficult, real-time, noisy environment. At the IJCAI/RoboCup97 softbot competition, all entries but ours used human-crafted cooperative decision-making behaviors. We instead entered a softbot team whose high-level decision making behaviors had...

We show how carefully crafted random matrices can achieve distance-preserving dimensionality reduction, accelerate spectral computations, and reduce the sample complexity of certain kernel methods.

Automatic modulation recognition (AMR) has become increasingly important in the field of signal processing, especially with advancements intelligent communication systems. Deep Learning (DL) technologies have been incorporated into AMR and they shown outstanding performances against conventional methods. The robustness DL-based methods under varying noise regimes is one major concerns for wides...

Reinforcement learning gives a way to learn under what circumstances to perform which actions. However, this approach lacks a formal framework for specifying hand-crafted restrictions, for specifying the effects of the system actions, or for specifying the user simulation. The information state approach, in contrast, allows system and user behavior to be specified as update rules, with precondi...

Reinforcement learning gives a way to learn under what circumstances to perform which actions. However, this approach lacks a formal framework for specifying hand-crafted restrictions, for specifying the effects of the system actions, or for specifying the user simulation. The information state approach, in contrast, allows system and user behavior to be specified as update rules, with precondi...