## Domain Adaptation of Natural Language Processing Systems (2007)

Citations: | 17 - 1 self |

### BibTeX

@TECHREPORT{Blitzer07domainadaptation,

author = {John Blitzer and Fernando Pereira and Rajeev Alur},

title = {Domain Adaptation of Natural Language Processing Systems},

institution = {},

year = {2007}

}

### OpenURL

### Abstract

My first thanks must go to Fernando Pereira. He was a wonderful advisor, and every aspect of this thesis has benefitted from his insight. At times I was a difficult, even unruly graduate student, and Fernando had patience with all my ideas, whether good or bad. What I’ll miss most, though, is the quick trip to Fernando’s office, coming away with new insights on everything from numerical underflow to the state of the academic community in machine learning and NLP. In addition to Fernando, this thesis was shaped by a great committee. Having Ben Taskar as committee chairman has given me the perfect excuse to interrupt his workday with new, ostensibly-thesis-related machine learning ideas. Mark Liberman and Mitch Marcus brought a much-needed linguistic perspective to a thesis on language, and many of the techniques described are based on work by Tong Zhang, who kindly served as my external committee member. Although he didn’t directly serve on my committee, Shai Ben-David got me started on the theoretical aspects of this work, and chapter 4 grew out of work I co-authored with him. I was also fortunate to have a great academic family. With brothers (and one sister!)

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Citation Context ...features. Given a sentence, the task of a part of speech tagger is to label each word with its grammatical function. The best part of speech taggers encode a sentence label as a chainstructured graph =-=[53, 20, 62]-=-. In this formulation, the part of speech label factors along the cliques of the graph. We will design pivot features for individual cliques and the input features associated with them. Consider the e... |

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Citation Context ...labels themselves have internal structure that we can take advantage of in designing the mapping ζ(x,y). Because of this, models which solve these tasks are often referred to as structured predictors =-=[42, 57, 60]-=-. Methods for structured prediction must factor problems so as to be able to perform computationally efficient inference and to be able to make accurate predictions. When we investigate adapting part ... |

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Citation Context ... “processing” are more similar than either one is to “accomplishments” is necessary to give correct distances here. 2.4.2 Bootstrapping Another paradigm for exploiting unlabeled data is bootstrapping =-=[67, 17, 49, 21, 1, 47]-=-. Bootstrapping methods begin with an initial classifier. They label unlabeled instances with this classifier. Then they choose some subset of the newly-labeled instances to create a new training set ... |

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Citation Context ...features. Given a sentence, the task of a part of speech tagger is to label each word with its grammatical function. The best part of speech taggers encode a sentence label as a chainstructured graph =-=[53, 20, 62]-=-. In this formulation, the part of speech label factors along the cliques of the graph. We will design pivot features for individual cliques and the input features associated with them. Consider the e... |

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Citation Context ...cal problem in text processing, and it serves as a first step in many pipelined systems, including higher-level syntactic processing [21, 47], information extraction [56, 52], and machine translation =-=[66]-=-. Because of their fundamental role, part of speech tagging systems must be deployed in a variety of domains. In this section, we show how to use SCL to adapt a tagger from a standard resource, the Pe... |

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Citation Context ... text processing systems. Here we show that improving a part of speech tagger in a new domain can improve a dependency parser in the new domain as well. We use the parser described by McDonald et al. =-=[48]-=-. That parser assumes that a sentence has been PoS-tagged before parsing, so it is a straightforward match for our experiments here. Accuracy 82 78 74 70 66 62 58 Dependency Parsing for 561 Test Sente... |

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Citation Context ...s. Figure 3.7(b) gives results for 40,000 sentences, and Figure 3.7(c) shows corresponding significance tests, with p < 0.05 being significant. We use a McNemar paired test for labeling disagreements =-=[31]-=-. Even when we use all the 57WSJ training data available, the SCL model significantly improves accuracy over both the supervised and ASO baselines. SCL is designed to improve the accuracies for unkno... |

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Citation Context ...the work of Pang et al. [51], which we use as our baseline. Thomas et al. [61] use discourse structure present in congressional records to perform more accurate sentiment classification. Pang and Lee =-=[50]-=- treat sentiment analysis as an ordinal ranking problem. In our work we only show improvement for the basic model, but all of these new techniques also make use of lexical features. Thus we believe th... |

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Citation Context ...labels themselves have internal structure that we can take advantage of in designing the mapping ζ(x,y). Because of this, models which solve these tasks are often referred to as structured predictors =-=[42, 57, 60]-=-. Methods for structured prediction must factor problems so as to be able to perform computationally efficient inference and to be able to make accurate predictions. When we investigate adapting part ... |

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Citation Context ... “processing” are more similar than either one is to “accomplishments” is necessary to give correct distances here. 2.4.2 Bootstrapping Another paradigm for exploiting unlabeled data is bootstrapping =-=[67, 17, 49, 21, 1, 47]-=-. Bootstrapping methods begin with an initial classifier. They label unlabeled instances with this classifier. Then they choose some subset of the newly-labeled instances to create a new training set ... |

162 | Domain adaptation with structural correspondence learning
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Citation Context ...t combining SCL with these methods yields still greater improvements, reducing error due to adaptation by as much as forty percent. The results in this chapter are drawn primarily from Blitzer et al. =-=[16]-=- and Blitzer et al. [15]. 423.1 Adapting a sentiment classification system A sentiment classification system receives as input a document and outputs a label indicating the sentiment (positive or neg... |

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Citation Context ...mial parameters of a generative parsing model to combine a large amount of training data from a source corpus (WSJ), and small amount of training data from a target corpus (Brown). Daume 61and Marcu =-=[28]-=- use an empirical Bayes model to estimate a latent variable model grouping instances into domain-specific or common across both domains. They also jointly estimate the parameters of the common classif... |

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Citation Context ...blem that is very closely-related to domain adaptation is the problem of covariate shift (also called sample selection bias), which has been studied in the machine learning and statistics communities =-=[59, 37]-=-. Here we assume the conditional distributions PrDS [y|x] and PrDT [y|x] are identical, but the instance marginal distributions PrDS [x] and PrDT [x] are different. Several researchers have studied al... |

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Citation Context ...se methods yields still greater improvements, reducing error due to adaptation by as much as forty percent. The results in this chapter are drawn primarily from Blitzer et al. [16] and Blitzer et al. =-=[15]-=-. 423.1 Adapting a sentiment classification system A sentiment classification system receives as input a document and outputs a label indicating the sentiment (positive or negative) of the document. ... |

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Citation Context ...f when adaptation techniques work, as well as how to best exploit the resources we have. This chapter develops a theoretical framework for domain adaptation and comprises the work of Ben-David et al. =-=[10]-=- and Blitzer et al. [14]. We first show how to use this framework to prove bounds on the target error for classifiers which are trained in a source domain. We then demonstrate how to use the bound to ... |

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Citation Context ... [51, 63, 32]. While movie reviews have been the most studied domain, sentiment analysis has been extended to a number of new domains, ranging from stock message boards to congressional floor debates =-=[25, 61]-=-. Research results have been deployed industrially in systems that gauge market reaction and summarize opinion from web pages, discussion boards, and blogs. With such widely-varying domains, researche... |

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Citation Context ... authors have empirically studied a special case of this in which each instance is weighted separately in the loss function, and instance weights are set to approximate the target domain distribution =-=[37, 13, 24, 39]-=-. We give a uniform convergence bound for algorithms that minimize a convex combination of multiple empirical source errors and we show that these algorithms can outperform standard empirical error mi... |

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46 |
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Citation Context ...to split the feature space into multiple 30“views” [17]. Learning in the two views model proceeds by training separate classifiers for each view and requiring that they “agree” on the unlabeled data =-=[26, 1, 2, 29, 55]-=-. In this section, we show how to relate ASO and SCL to new theoretical work on using canonical correlation analysis for multiple view learning [36, 40]. We show that a variant of the ASO optimization... |

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