A Two-Stage Algorithm for Domain Adaptation with Application to Sentiment Transfer Problems
Lecture notes in computer sciencePublished 1 January 2010
Qiong Wu, Songbo Tan, Miyi Duan, Xueqi Cheng
Citations7
SJR quartileQ2
SJR score0.35
SNIP0.55
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
A two-stage algorithm for domain adaptation that shares the information between the source domain and the target domain to get some most confidently labeled documents in the targetdomain, and exploits them to label the target-domain data via following the intrinsic structure revealed by thetarget domain.
Abstract
Classification systems are typically domain-specific, and the performance decreases sharply when transferred from one domain to another domain. Building these systems involves annotating a large amount of data for every domain, which needs much human labor. So, a reasonable way is to utilize labeled data in one existing (or called source) domain for classification in target domain. To address this problem, we propose a two-stage algorithm for domain adaptation. At the first transition stage, we share the information between the source domain and the target domain to get some most confidently labeled documents in the target domain, and at the second transmission stage, we exploit them to label the target-domain data via following the intrinsic structure revealed by the target domain. The experimental results on sentiment data indicate that the proposed approach could improve the performance of domain adaptation dramatically.
Keywords
Computer Science
Journal of the Royal Statistical Society Series B (Statistical Methodology)Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm
49,657 Citations1977A. P. Dempster, N. M. Laird +1 more
IEEE Transactions on Information TheoryNearest neighbor pattern classification
16,134 Citations1967Thomas M. Cover, Peter E. Hart
The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Machine LearningInduction of Decision Trees
14,815 Citations1986J. R. Quinlan
This paper summarizes an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, which is described in detail.
Lecture notes in computer scienceText categorization with Support Vector Machines: Learning with many relevant features
7,925 Citations1998Thorsten Joachims
SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of di-erent learning tasks, eliminating the need for manual parameter tuning.
Thumbs up?
6,987 Citations2002Bo Pang, Lillian Lee +1 more
This work considers the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative, and concludes by examining factors that make the sentiment classification problem more challenging.
Machine LearningText Classification from Labeled and Unlabeled Documents using EM
2,749 Citations2000Kamal Nigam, Andrew Kachites McCallum +2 more
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents, and presents two extensions to the algorithm that improve classification accuracy under these conditions.
Transductive Inference for Text Classification using Support Vector Machines
2,717 Citations1999Thorsten Joachims
An analysis of why TSVMs are well suited for text classi(cid:12)cation is presented, and an algorithm for training TSVMs e(cid:14)-ciently, handling 10,000 examples and more is proposed.
Journal of Artificial Intelligence ResearchDomain Adaptation for Statistical Classifiers
831 Citations2006Hal Daumé, Daniel Marcu
This work introduces a statistical formulation of this problem in terms of a simple mixture model and presents an instantiation of this framework to maximum entropy classifiers and their linear chain counterparts and leads to improved performance on three real world tasks on four different data sets from the natural language processing domain.
MPG.PuRe (Max Planck Society)Ranking on Data Manifolds
673 Citations2003Dengyong Zhou, Jason Weston +3 more
A simple universal ranking algorithm for data lying in the Euclidean space, such as text or image data, to rank the data with respect to the intrinsic manifold structure collectively revealed by a great amount of data.
Recent Advances in Natural Language ProcessingCustomizing Sentiment Classifiers to New Domains: a Case Study
370 Citations2005Anthony Aue, Michael Gamon
Four different approaches to customizing a sentiment classification system to a new target domain in the absence of large amounts of labeled data are surveyed and their advantages, disadvantages and performance are discussed.
Scholarworks (University of Massachusetts Amherst)Representation and Learning in Information Retrieval
361 Citations1991David Lewis
A new theoretical model for text classification systems, including systems for document retrieval, automated indexing, electronic mail filtering, and similar tasks, is introduced, suggesting that the poor statistical characteristics of a syntactic indexing phrase representation negate its desirable semantic characteristics.
Comparative experiments on sentiment classification for online product reviews
318 Citations2006Hang Cui, Vibhu O. Mittal +1 more
A series of experiments with different machine learning algorithms are discussed in order to experimentally evaluate various trade-offs, using approximately 100K product reviews from the web.
Transferring naive bayes classifiers for text classification
313 Citations2007Wenyuan Dai, Gui-Rong Xue +2 more
This paper proposes a novel transfer-learning algorithm for text classification based on an EM-based Naive Bayes classifiers and shows that the algorithm outperforms the traditional supervised and semi-supervised learning algorithms when the distributions of the training and test sets are increasingly different.
Expert Systems with ApplicationsAn effective refinement strategy for KNN text classifier
275 Citations2005S TAN
This paper proposes a new refinement strategy, which is called as DragPushing, for the K-Nearest Neighbors Classifier, and shows that DragP pushing achieved a significant improvement on the performance of the KNN Classifier.
Lecture notes in computer scienceKnowledge Discovery in Databases: PKDD 2007
232 Citations2007Kok, Joost N, Skowron, Andrzej +4 more
Lecture notes in computer scienceAdapting Naive Bayes to Domain Adaptation for Sentiment Analysis
190 Citations2009Songbo Tan, Xueqi Cheng +2 more
The experimental results indicate that proposed approach could improve the performance of base classifier dramatically, and even provide much better performance than the transfer-learning baseline, i.e. the Naive Bayes Transfer Classifier (NTBC).
Construction of neural nets using the radon transform
167 Citations1989Carroll, Dickinson
The authors present a method for constructing a feedforward neural net implementing an arbitrarily good approximation to any L/sub 2/ function over (-1, 1)/sup n/.
When Specialists and Generalists Work Together: Overcoming Domain Dependence in Sentiment Tagging
165 Citations2008Alina Andreevskaia, Sabine Bergler
A sentiment annotation system that integrates a corpus-based classifier trained on a small set of annotated in-domain data and a lexicon-based system trained on WordNet, and a new system consisting of the ensemble of two classifiers with precision-based vote weighting that provides significant gains in accuracy and recall.
Transfer learning from multiple source domains via consensus regularization
122 Citations2008Ping Luo, Fuzhen Zhuang +3 more
This work proposes a consensus regularization framework for transfer learning from multiple source domains to a target domain, in which a local classifier is trained by considering both local data available in a source domain and the prediction consensus with the classifiers from other source domains.
A two-stage approach to domain adaptation for statistical classifiers
115 Citations2007Jing Jiang, ChengXiang Zhai
This paper presents a two-stage approach to domain adaptation, where at the first generalization stage, the author looks for a set of features generalizable across domains, and at the second adaptation stage, heuristics to pick up useful features specific to the target domain.
A novel scheme for domain-transfer problem in the context of sentiment analysis
113 Citations2007Songbo Tan, Gaowei Wu +2 more
The experimental results demonstrate that proposed scheme can significantly boost the accuracy of the base sentiment classifier on new domain.
A novel refinement approach for text categorization
106 Citations2005Songbo Tan, Xueqi Cheng +3 more
The results indicate that in each case, the refined classifiers achieve significant performance improvement over the base classifiers used, and the performance of the Refined Centroid Classifier implemented is comparable, if not better, to that of state-of-the-art support vector machine (SVM)-based classifier, but offers a much lower computational cost.
Graph ranking for sentiment transfer
65 Citations2009Qiong Wu, Songbo Tan +1 more
A novel approach is proposed, which integrates the sentiment orientations of documents into the graph-ranking algorithm using the accurate labels of old- domain documents as well as the "pseudo" labels of new-domain documents.
Lecture notes in computer scienceBridged Refinement for Transfer Learning
57 Citations2007Dikan Xing, Wenyuan Dai +2 more
A novel algorithm, namely bridged refinement, to take the shift of distribution into consideration is proposed, which corrects the labels predicted by a shift-unaware classifier towards a target distribution and takes the mixture distribution of the training and test data as a bridge to better transfer from the training data to the test data.
Lecture notes in computer scienceLearning from Labeled and Unlabeled Documents: A Comparative Study on Semi-Supervised Text Classification
13 Citations2000Carsten Lanquillon
This paper compares three commonly applied text classifiers in the light of semi-supervised learning, namely a linear support vector machine, a similarity-based tfidf and a Naive Bayes classifier.