Jacob Eisenstein

My primary research interest is learning computational models of the communicative properties of hand gestures. I use machine learning to identify patterns of gesture that can be leveraged to improve performance on natural language understanding tasks. More broadly, I'm interested in structured learning techniques for language processing, especially hierarchical Bayesian models. Other interests include intelligent and multimodal user interfaces.

For non-specialists, here is a short description of my research that I wrote for my grandparents.

Selected Recent Publications

J. Eisenstein and R. Barzilay. Bayesian Unsupervised Topic Segmentation. EMNLP 2008.

A new method to segment text and speech transcripts into topically-coherent units, using both lexical cohesion and cue phrases. First paper to show that cue phrases can be used without supervision, by combining them with cohesion in a generative Bayesian framework.

B. Snyder and T. Naseem and J. Eisenstein and R. Barzilay. Unsupervised Multilingual Learning for POS Tagging. EMNLP 2008.

Unsupervised part-of-speech tagging works better when applied to multiple languages simultaneously.

J. Eisenstein, R. Barzilay, and R. Davis. Discourse Topic and Gestural Form. AAAI 2008.

A quantitative analysis of the influence of speaker and topic on gestural form. Using low-level interest point features, it is possible to show that multiple speakers use similar gestures when describing the same topic.

J. Eisenstein, R. Barzilay, and R. Davis. Gestural Cohesion for Topic Segmentation. ACL 2008.

Coherent discourse topics contain internally consistent gestural forms, paralleling similar work on the distribution of lexical items. Automatically extracted gesture features are used to improve unsupervised topic segmentation on dialogues.

S. R. K. Branavan, H. Chen, J. Eisenstein, and R. Barzilay. Learning Document-Level Semantic Properties from Free-text Annotations. ACL 2008.

Unstructured text and keyphrase annotations predict document-level semantics in a joint Bayesian framework. This can be used to automatically generate pro/con lists from product reviews.

J. Eisenstein, R. Barzilay, and R. Davis. Modeling Gesture Salience as a Hidden Variable for Coreference Resolution and Keyframe Extraction. Journal of Artificial Intelligence Research, volume 31, 353-398.

Describes the use of a conditional hidden variable model for gesture salience in coreference resolution. The model improves performance on coreference resolution, and the estimates of gesture salience can be transferred directly to select keyframes containing interesting gestures.

Here is a full list of my publications.

And here is my PhD thesis

Software

Election Prediction

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