The information bottleneck method
- Naftali Tishby, Fernando C Pereira, W. Bialek
- Computer ScienceArXiv
- 24 April 2000
The variational principle provides a surprisingly rich framework for discussing a variety of problems in signal processing and learning, as will be described in detail elsewhere.
A theory of learning from different domains
- Shai Ben-David, John Blitzer, K. Crammer, Alex Kulesza, Fernando C Pereira, Jennifer Wortman Vaughan
- Computer ScienceMachine-mediated learning
- 1 May 2010
A classifier-induced divergence measure that can be estimated from finite, unlabeled samples from the domains and shows how to choose the optimal combination of source and target error as a function of the divergence, the sample sizes of both domains, and the complexity of the hypothesis class.
Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
- John Blitzer, Mark Dredze, Fernando C Pereira
- Computer ScienceAnnual Meeting of the Association for…
- 1 June 2007
This work extends to sentiment classification the recently-proposed structural correspondence learning (SCL) algorithm, reducing the relative error due to adaptation between domains by an average of 30% over the original SCL algorithm and 46% over a supervised baseline.
Analysis of Representations for Domain Adaptation
- Shai Ben-David, John Blitzer, K. Crammer, Fernando C Pereira
- Computer ScienceNIPS
- 4 December 2006
The theory illustrates the tradeoffs inherent in designing a representation for domain adaptation and gives a new justification for a recently proposed model which explicitly minimizes the difference between the source and target domains, while at the same time maximizing the margin of the training set.
Online Large-Margin Training of Dependency Parsers
- Ryan T. McDonald, K. Crammer, Fernando C Pereira
- Computer ScienceAnnual Meeting of the Association for…
- 25 June 2005
An effective training algorithm for linearly-scored dependency parsers that implements online large-margin multi-class training on top of efficient parsing techniques for dependency trees is presented.
Domain Adaptation with Structural Correspondence Learning
- John Blitzer, Ryan T. McDonald, Fernando C Pereira
- Computer ScienceConference on Empirical Methods in Natural…
- 22 July 2006
This work introduces structural correspondence learning to automatically induce correspondences among features from different domains in order to adapt existing models from a resource-rich source domain to aresource-poor target domain.
Non-Projective Dependency Parsing using Spanning Tree Algorithms
- Ryan T. McDonald, Fernando C Pereira, Kiril Ribarov, Jan Hajic
- Computer ScienceHuman Language Technology - The Baltic Perspectiv
- 6 October 2005
Using this representation, the parsing algorithm of Eisner (1996) is sufficient for searching over all projective trees in O(n3) time and is extended naturally to non-projective parsing using Chu-Liu-Edmonds (Chu and Liu, 1965; Edmonds, 1967) MST algorithm, yielding an O( n2) parsing algorithm.
Maximum Entropy Markov Models for Information Extraction and Segmentation
- A. McCallum, D. Freitag, Fernando C Pereira
- Computer ScienceInternational Conference on Machine Learning
- 29 June 2000
A new Markovian sequence model is presented that allows observations to be represented as arbitrary overlapping features (such as word, capitalization, formatting, part-of-speech), and defines the conditional probability of state sequences given observation sequences.
Shallow Parsing with Conditional Random Fields
- Fei Sha, Fernando C Pereira
- Computer ScienceNorth American Chapter of the Association for…
- 27 May 2003
This work shows how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model.
Weighted finite-state transducers in speech recognition
- M. Mohri, Fernando C Pereira, M. Riley
- Computer ScienceComputer Speech and Language
- 2002
WFSTs provide a common and natural representation for hidden Markov models (HMMs), context-dependency, pronunciation dictionaries, grammars, and alternative recognition outputs, and general transducer operations combine these representations flexibly and efficiently.
...
...