Jacob Eisenstein

Selected Recent Publications

Reading to Learn: Constructing Features from Semantic Abstracts. Eisenstein, Clarke, Goldwasser and Roth. EMNLP 2009.: Given a machine learning problem, can a system acquire better features by "reading" text written by domain experts? We develop an model that extracts relational features from text, improving learning.
Learning Document-Level Semantic Properties from Free-text Annotations. Branavan, Chen, Eisenstein, and Barzilay. Journal of Artificial Intelligence Research 34, 2009.: Informal "keyphrase" annotations can be used to predict document-level semantics, by modeling the latent annotation paraphrase structure. The resulting system automatically generates pro/con lists from reviews of products and services.
Unsupervised Multilingual Learning for Part-of-Speech Tagging. Naseem, Snyder, Eisenstein and Barzilay. Journal of Artificial Intelligence Research 36, 2009.: Unsupervised part-of-speech tagging works better when applied to multiple languages simultaneously.
Bayesian Unsupervised Topic Segmentation. Eisenstein and Barzilay. 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 learn cue phrases without supervision, by combining them with cohesion in a generative Bayesian framework.

Gestural Cohesion for Topic Segmentation. Eisenstein, Barzilay, and Davis. ACL 2008.: Coherent discourse topics contain internally consistent gestural-forms, paralleling a similar phenomenon in the distribution of lexical items. Automatically extracted gesture features improve unsupervised topic segmentation on dialogues.

Jacob Eisenstein

Selected Recent Publications

Election Prediction

Contact

		Jacob Eisenstein I'm a Postdoctoral Fellow in the Machine Learning Department at Carnegie Mellon University. I graduated from MIT in 2008, and spent a year as a Beckman Fellow at the University of Illinois. I work on machine learning approaches to natural language processing, focusing on connections between language and the external world. I'm especially interested in social media, discourse, non-verbal communication and unsupervised learning. In the past, I worked on intelligent user interfaces. publications \| dissertation \| for non-specialists \| code
Selected Recent Publications Reading to Learn: Constructing Features from Semantic Abstracts. Eisenstein, Clarke, Goldwasser and Roth. EMNLP 2009. Given a machine learning problem, can a system acquire better features by "reading" text written by domain experts? We develop an model that extracts relational features from text, improving learning. Learning Document-Level Semantic Properties from Free-text Annotations. Branavan, Chen, Eisenstein, and Barzilay. Journal of Artificial Intelligence Research 34, 2009. Informal "keyphrase" annotations can be used to predict document-level semantics, by modeling the latent annotation paraphrase structure. The resulting system automatically generates pro/con lists from reviews of products and services. Unsupervised Multilingual Learning for Part-of-Speech Tagging. Naseem, Snyder, Eisenstein and Barzilay. Journal of Artificial Intelligence Research 36, 2009. Unsupervised part-of-speech tagging works better when applied to multiple languages simultaneously. Bayesian Unsupervised Topic Segmentation. Eisenstein and Barzilay. 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 learn cue phrases without supervision, by combining them with cohesion in a generative Bayesian framework. Gestural Cohesion for Topic Segmentation. Eisenstein, Barzilay, and Davis. ACL 2008. Coherent discourse topics contain internally consistent gestural-forms, paralleling a similar phenomenon in the distribution of lexical items. Automatically extracted gesture features improve unsupervised topic segmentation on dialogues. Election Prediction In 2006, I helped lay the groundwork for Nate Silver's media empire by developing a statistical model for election forecasting. It did pretty well. Contact Machine Learning Department Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 15213 jacobe@gmail.com