Symposium on Machine Learning in Speech and Language Processing (MLSLP)
Bellevue, WA, USA
Machine learning (ML) has already made significant impacts on our daily life. From hand-written digit recognition, spam filtering to ranking search results, machine learning techniques help us build intelligent systems more easily and make computers seem smarter. Nevertheless, current ML techniques support limited set of supervision protocols, making it difficult to transfer human knowledge to machines efficiently without labeling examples explicitly. However, structured tasks, which involve many interdependent decisions for a given example, are expensive to label. Given that many important tasks in natural language processing and information extraction are structured tasks, it is important to develop learning frameworks that can use knowledge resources and other sources of indirect supervision in addition to labeled examples for the current task.
Bibliographic reference. Chang, Ming-Wei / Clarke, James / Goldwasser, Dan / Ratinov, Lev / Srikumar, Vivek / Roth, Dan (2011): "Structured prediction with indirect supervision", In MLSLP-2011.