Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks [Paper] [Poster] [Source code]
A key challenge in entity linking is making effective use of contextual information to disambiguate mentions that might refer to different entities in different contexts. We present a model that uses convolutional neural networks to capture semantic correspondence between a mention’s context and a proposed target entity. These convolutional networks operate at multiple granularities to exploit various kinds of topic information, and their rich parameterization gives them the capacity to learn which n-grams characterize different topics. We combine these networks with a sparse linear model to achieve state-of-the-art performance on multiple entity linking datasets, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).
Accepted to NAACL 2016.
DJ: Distributed JIT [Documentation] [Source code] [Preprint paper]
DJ automatically rewrites Java programs that are written for a single host to be able to operate in a distributed environment. The system works by rewriting Java bytecode and providing a runtime environment which supports features such as remote memory access, distributed locks, etc. Finally, the programs distributed state is controlled by a “JIT” which is separate from the core DJ system, as such, it can be switched by a user to change the behavior of the distributed program.
Targeted issues of scheduling High Performance Computing (HPC) applications in Cloud Computing environments. Developed a Gang Scheduler for the XEN hypervisor. This ensures that only one virtual machine is running for any set of competing resources.
Post project: Developed an extremely simplified Gang scheduler for the Linux Kernel as a point for comparison with XEN. (source)