Gabriel Doyle

University of California, San Diego | UCSD · Department of Linguistics

We present a method to jointly learn features and weights directly from distributional data in a log-linear framework. Specifically, we propose a non-parametric Bayesian model for learning phonological markedness constraints directly from the distribution of input-output mappings in an Optimality Theory (OT) setting. The model uses an Indian Buffet...

The problem of cross-modal retrieval from multimedia repositories is considered. This problem addresses the design of retrieval systems that support queries across content modalities, for example, using an image to search for texts. A mathematical formulation is proposed, equating the design of cross-modal retrieval systems to that of isomorphic fe...

The problem of joint modeling the text and image components of multimedia documents is studied. The text component is represented as a sample from a hidden topic model, learned with latent Dirichlet allocation, and images are represented as bags of visual (SIFT) features. Two hypotheses are investigated: that 1) there is a benefit to explicitly mod...

Many dierent topic models have been used successfully for a variety of applications. However, even state-of-the-art topic models suer from the important aw that they do not capture the tendency of words to appear in bursts; it is a fundamental property of lan- guage that if a word is used once in a doc- ument, it is more likely to be used again. We...

We apply topic models to financial data to obtain a more accurate view of eco-nomic networks than that supplied by traditional economic statistics. The learned topic models can serve as a substitute for or a complement to more complicated network analysis. Initial results on S&P500 stock market data show that topic models are able to obtain meaning...