Jason Eisner for our Joint CSE/IST Colloquium Series in Natural Language Processing!

January 26 - Mark the date for our first Joint CSE/IST Colloquium for Spring 2018!

Recovering Syntactic Structure from Surface Features

We show how to predict the basic word-order facts of a novel language, and even obtain approximate syntactic parses, given only a corpus of part-of-speech (POS) sequences.  We are motivated by the longstanding challenge of determining the structure of a language from its superficial features.  While this is usually regarded as an unsupervised learning problem, there are good reasons that generic unsupervised learners are not up to the challenge.  We do much better with a supervised approach where we train a system -- a kind of language acquisition device -- to predict how linguists will annotate a language.  Our system uses a neural network to extract predictions from a large collection of numerical measurements. We train it on a mixture of real treebanks and synthetic treebanks obtained by systematically permuting the real trees, which we can motivate as sampling from an approximate prior over possible human languages.

Jason Eisner is Professor of Computer Science at Johns Hopkins University, where he is also affiliated with the Center for Language and Speech Processing, the Machine Learning Group, the Cognitive Science Department, and the national Center of Excellence in Human Language Technology. His goal is to develop the probabilistic modeling, inference, and learning techniques needed for a unified model of all kinds of linguistic structure. His 100+ papers have presented various algorithms for parsing, machine translation, and weighted finite-state machines; formalizations, algorithms, theorems, and empirical results in computational phonology; and unsupervised or semi-supervised learning methods for syntax, morphology, and word-sense disambiguation. He is also the lead designer of Dyna, a new declarative programming language that provides an infrastructure for AI research. He has received two school-wide awards for excellence in teaching.

Find more about his work here.