The goal in domain adaptation is to train a model using labeled data sampled from a domain different from the target domain on which the model will be deployed. We exploit unlabel...
Domain adaptation is a fundamental learning problem where one wishes to use labeled data from one or several source domains to learn a hypothesis performing well on a different, y...
This paper presents Domain Relevance Estimation (DRE), a fully unsupervised text categorization technique based on the statistical estimation of the relevance of a text with respe...
Parsing and Tagging are very important tasks in Natural Language Processing. Parsing amounts to searching the correct combination of grammatical rules among those compatible with a...
For our participation in CLEF, the Berkeley group participated in the monolingual, multilingual and GIRT tasks. To help enrich the CLEF relevance set for future training, we prepa...
Fredric C. Gey, Hailing Jiang, Vivien Petras, Aita...