Structure and Knowledge in Natural Language Processing

Опубликовано 17 августа 2016, 1:42

Human language exhibits complex structure. To be successful, machine learning approaches to language-related problems must be able to take advantage of this structure. I will discuss several investigations into the relationship between structure and learning, which have led to some surprising conclusions about the role that structure plays in language processing. I will describe some recent efforts related to learning strategies that not only aim to do a good job, but aim to do it quickly. From there, I will consider the question of: where does this structure come from. By taking insights from linguistic typology, I will show that very simple typological information can lead to significant increases in system performance for some simple syntactic problems. Moreover, I will show how this typological information can be mined from raw data. (This talk includes joint work with Jason Eisner, Dan Klein, John Langford, Percy Liang, Daniel Marcu, and some of my students: Jiarong Jiang, Arvind Agarwal, Adam Teichert and Piyush Rai.)