We address the question of which syntactic representation is best suited for role-semantic analysis of English in the FrameNet paradigm. We compare systems based on dependencies a...
We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the ...
We investigate the utility of supertag information for guiding an existing dependency parser of German. Using weighted constraints to integrate the additionally available informat...
Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages, and may shed light on how humans learn to segment...
Sharon Goldwater, Thomas L. Griffiths, Mark Johnso...
We derive an optimal learning rule in the sense of mutual information maximization for a spiking neuron model. Under the assumption of small fluctuations of the input, we find a s...