微软研究院
Advancing how machines understand and generate human language across speech, text, and translation.
1. Project Introduction “Everyday we are faced with a sea of acronyms, ever changing group structures, and fast-tracked projects.” Currently, collation and curation of corporate knowledge is a painstaking manual process. We seek to move…
The Conceptualization model aims to map text format entities into semantic concept categories with some probabilities, which may depend on the context texts of the entities. As an example, “Microsoft” could be automatically mapped to…
Universal Dependencies offer a homogeneous syntactic representation across languages fostering multilingual NLP research. However, dependency structures lack a transparent syntax-semantics interface like CCG, making it hard to derive target-application specific semantics in a synchronous compositional…
Deep (recurrent) neural networks has been shown to successfully learn complex mappings between arbitrary length input and output sequences, within the effective framework of encoder-decoder networks. We investigate the extensions of this sequence to sequence…
