Research Projects
Stopping Criteria for Active Learning
- Description: We are researching principled methods for detecting when to stop the collection of additional training data for building machine learning systems.
- Related Publications: [1], [2], [3], [4], [5], [6], [7]
Forecasting the Performance of Machine Learning Systems
- Description: We are researching how to forecast the performance of machine learning systems.
- Related Publications: [1], [2]
Active Learning to Improve Cost-Effectiveness of Annotation
- Description: We are researching how to selectively sample data for annotation with the aim of maximizing the benefits received from annotation effort.
- Related Publications: [1], [2], [3]
Computer-Assisted Translation Technologies
Computational Deep Understanding of Language
- Description: We are researching how to computationally model nuanced semantics of language such as modality and impacts on machine translation.
- Related Publications: [1], [2], [3]
Data Cleaning
- Description: We are researching how to better automatically or semi-automatically detect and repair errors in data.
- Related Publications: [1], [2], [3], [4]
Machine Learning for the Life Sciences
Filtering Social Media Content for Relevance to Social Unrest
- Description: We are researching how to use machine learning and natural language processing techniques to filter social media content for relevance to social unrest.
- Related Publications: [1]
Translation Lexicon Induction for Historically Unwritten Languages
- Description: We are researching how to induce translation lexicons for historically unwritten languages through bridging loanwords.
- Related Publications: [1]
Using global constraints, reranking, and fast approximations of the Hungarian algorithm to improve cognates detection
- Description: We are researching how to use global constraints, reranking, and fast approximations of the Hungarian algorithm to improve cognates detection.
- Related Publications: [1]