Fernando C Pereira

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https://sites.google.com/site/fernandopereira/Home
Publications168
h-index70
Citations28,480
Highly Influential Citations2,771
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Biographies, Bollywood, Boom-boxes and Blenders: Domain Adaptation for Sentiment Classification
TLDR
We extend to sentiment classification the recently proposed structural correspondence learning 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. Expand
The information bottleneck method
TLDR
We define the relevant information in a signal $x\in X$ as being the information that this signal provides about another signal $y\in \Y$. Expand
A theory of learning from different domains
TLDR
We address the first question by bounding a classifier’s target error in terms of its source error and the divergence between the two domains. Expand
Online Large-Margin Training of Dependency Parsers
TLDR
We present an effective training algorithm for linearly-scored dependency parsers that implements online large-margin multi-class training (Crammer and Singer, 2003) on top of efficient parsing techniques for dependency trees (Eisner, 1996). Expand
Domain Adaptation with Structural Correspondence Learning
TLDR
We introduce structural correspondence learning to automatically induce correspondences among features from different domains by modeling their correlations with pivot features. Expand
Non-Projective Dependency Parsing using Spanning Tree Algorithms
TLDR
We formalize weighted dependency parsing as searching for maximum spanning trees (MSTs) in directed graphs using Chu-Liu-Edmonds MST algorithm, yielding an O(n2) parsing algorithm. Expand
Analysis of Representations for Domain Adaptation
TLDR
We formalize the tradeoffs inherent in designing a representation for domain adaptation and give a new justification for a recently proposed model. Expand
Maximum Entropy Markov Models for Information Extraction and Segmentation
TLDR
This paper presents a new Markovian sequence model, closely related to HMMs, 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. Expand
Shallow Parsing with Conditional Random Fields
TLDR
We show here how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task. Expand
Weighted finite-state transducers in speech recognition
TLDR
We survey the use of weighted finite-state transducers (WFSTs) in speech recognition. Expand
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