Kenneth Church
Location: (Baltimore, MD)
Personal Research Web Page: https://jshare.johnshopkins.edu/kchurch4/public_html/
Keywords: natural language processing, statistical machine learning, computational linguistics, information extraction, corpora, data mining
Posted on: Thursday, May 5th, 2011
Broad Research Area: AI / Machine Learning / Robotics / Vision, Databases / Information Retrieval / Data Mining, Information Systems / Information Science, Numerical/Scientific Computing / HPC / Data-Intensive Scalable Computing
Research Interests:
For lots of applications like speech recognition and machine translation, it is recommended that the training set should be similar to the test set. But real life isn’t like that. What can we do when the test set doesn’t match the training set? In many practical settings the test document will be unique in lots of unanticipated ways (author, topic, speaker, genre). We need methods that are more robust to mismatches between the training data and the test data. We also need methods that can adapt to the unique properties of the test document.
