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69
Generalized expectation criteria for semi-supervised learning of conditional random fields
- In In Proc. ACL, pages 870 – 878
, 2008
"... This paper presents a semi-supervised training method for linear-chain conditional random fields that makes use of labeled features rather than labeled instances. This is accomplished by using generalized expectation criteria to express a preference for parameter settings in which the model’s distri ..."
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Cited by 108 (11 self)
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This paper presents a semi-supervised training method for linear-chain conditional random fields that makes use of labeled features rather than labeled instances. This is accomplished by using generalized expectation criteria to express a preference for parameter settings in which the model’s distribution on unlabeled data matches a target distribution. We induce target conditional probability distributions of labels given features from both annotated feature occurrences in context and adhoc feature majority label assignment. The use of generalized expectation criteria allows for a dramatic reduction in annotation time by shifting from traditional instance-labeling to feature-labeling, and the methods presented outperform traditional CRF training and other semi-supervised methods when limited human effort is available. 1
Sentiment analysis of blogs by combining lexical knowledge with text classification
- In KDD
, 2009
"... The explosion of user-generated content on the Web has led to new opportunities and significant challenges for companies, that are increasingly concerned about monitoring the discussion around their products. Tracking such discussion on weblogs, provides useful insight on how to improve products or ..."
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Cited by 61 (6 self)
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The explosion of user-generated content on the Web has led to new opportunities and significant challenges for companies, that are increasingly concerned about monitoring the discussion around their products. Tracking such discussion on weblogs, provides useful insight on how to improve products or market them more effectively. An important component of such analysis is to characterize the sentiment expressed in blogs about specific brands and products. Sentiment Analysis focuses on this task of automatically identifying whether a piece of text expresses a positive or negative opinion about the subject matter. Most previous work in this area uses prior lexical knowledge in terms of the sentiment-polarity of words. In contrast, some recent approaches treat the task as a text classification problem, where they learn to classify sentiment based only on labeled training data. In this paper, we present a unified framework in which one can use background lexical information in terms of word-class associations, and refine this information for specific domains using any available training examples. Empirical results on diverse domains show that our approach performs better than using background knowledge or training data in isolation, as well as alternative approaches to using lexical knowledge with text classification.
Optimizing SVMs for Complex Call Classification
, 2003
"... Large margin classifiers such as Support Vector Machines (SVM) or Adaboost are obvious choices for natural language document or call routing. However, how to combine several binary classifiers to optimize the whole routing process and how this process scales when it involves many different decisions ..."
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Cited by 54 (19 self)
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Large margin classifiers such as Support Vector Machines (SVM) or Adaboost are obvious choices for natural language document or call routing. However, how to combine several binary classifiers to optimize the whole routing process and how this process scales when it involves many different decisions (or classes) is a complex problem that has only received partial answers [1, 2]. We propose a global optimization process based on an optimal channel communication model that allows a combination of possibly heterogeneous binary classifiers. As in Markov modeling, computational feasibility is achieved through simplifications and independence assumptions that are easy to interpret. Using this approach, we have managed to decrease the call-type classification error rate for AT&T's How May I Help You (HMIHY ) natural dialog system by 50%.
Document-word co-regularization for semi-supervised sentiment analysis
- IN ICDM. VIKAS SINDHWANI, JIANYING HU, AND ALEXANDRA MOJSILOVIC
, 2008
"... The goal of sentiment prediction is to automatically identify whether a given piece of text expresses positive or negative opinion towards a topic of interest. One can pose sentiment prediction as a standard text categorization problem. However, gathering labeled data turns out to be a bottleneck in ..."
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Cited by 43 (11 self)
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The goal of sentiment prediction is to automatically identify whether a given piece of text expresses positive or negative opinion towards a topic of interest. One can pose sentiment prediction as a standard text categorization problem. However, gathering labeled data turns out to be a bottleneck in the process of building high quality text classifiers. Fortunately, background knowledge is often available in the form of prior information about the sentiment polarity of words in a lexicon. Moreover, in many applications abundant unlabeled data is also available. In this paper, we propose a novel semi-supervised sentiment prediction algorithm that utilizes lexical prior knowledge in conjunction with unlabeled examples. Our method is based on joint sentiment analysis of documents and words based on a bipartite graph representation of the data. We present an empirical study on a diverse collection of sentiment prediction problems which confirms that our semi-supervised lexical models significantly outperform purely supervised and competing semisupervised techniques.
Interactive feature selection
- In Proc. of IJCAI
, 2005
"... We execute a careful study of the effects of feature selection and human feedback on features in active learn-ing settings. Our experiments on a variety of text categorization tasks indicate that there is significant potential in improving classifier performance by feature reweighting, beyond that a ..."
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Cited by 35 (7 self)
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We execute a careful study of the effects of feature selection and human feedback on features in active learn-ing settings. Our experiments on a variety of text categorization tasks indicate that there is significant potential in improving classifier performance by feature reweighting, beyond that achieved via selective sampling alone (standard active learning) if we have access to an oracle that can point to the important (most predictive) fea-tures. Consistent with previous findings, we find that feature selection based on the labeled training set has little effect. But our experiments on human subjects indicate that human feedback on feature relevance can identify a sufficient proportion (65%) of the most relevant features. Furthermore, these experiments show that feature labeling takes much less (about 1/5th) time than document labeling. We propose an algorithm that interleaves labeling features and documents which significantly accelerates active learning. Feature feedback can complement traditional active learning in applications like filtering, personalization, and recommendation. 1.
A Non-negative Matrix Tri-factorization Approach to Sentiment Classification with Lexical Prior Knowledge
"... Sentiment classification refers to the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a subject at hand. The proliferation of user-generated web content such as blogs, discussion forums and online review sites has made it possible to pe ..."
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Cited by 33 (4 self)
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Sentiment classification refers to the task of automatically identifying whether a given piece of text expresses positive or negative opinion towards a subject at hand. The proliferation of user-generated web content such as blogs, discussion forums and online review sites has made it possible to perform large-scale mining of public opinion. Sentiment modeling is thus becoming a critical component of market intelligence and social media technologies that aim to tap into the collective wisdom of crowds. In this paper, we consider the problem of learning high-quality sentiment models with minimal manual supervision. We propose a novel approach to learn from lexical prior knowledge in the form of domain-independent sentimentladen terms, in conjunction with domaindependent unlabeled data and a few labeled documents. Our model is based on a constrained non-negative tri-factorization of the term-document matrix which can be implemented using simple update rules. Extensive experimental studies demonstrate the effectiveness of our approach on a variety of real-world sentiment prediction tasks. 1
Active learning with feedback on both features and instances
- Journal of Machine Learning Research
, 2006
"... We extend the traditional active learning framework to include feedback on features in addition to labeling instances, and we execute a careful study of the effects of feature selection and human feedback on features in the setting of text categorization. Our experiments on a variety of categorizati ..."
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Cited by 30 (0 self)
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We extend the traditional active learning framework to include feedback on features in addition to labeling instances, and we execute a careful study of the effects of feature selection and human feedback on features in the setting of text categorization. Our experiments on a variety of categorization tasks indicate that there is significant potential in improving classifier performance by feature re-weighting, beyond that achieved via membership queries alone (traditional active learning) if we have access to an oracle that can point to the important (most predictive) features. Our experiments on human subjects indicate that human feedback on feature relevance can identify a sufficient proportion of the most relevant features (over 50 % in our experiments). We find that on average, labeling a feature takes much less time than labeling a document. We devise an algorithm that interleaves labeling features and documents which significantly accelerates standard active learning in our simulation experiments. Feature feedback can complement traditional active learning in applications such as news filtering, e-mail classification, and personalization, where the human teacher can have significant knowledge on the relevance of features.
An interactive algorithm for asking and incorporating feature feedback into support vector machines
- Proc SIGIR, ACM
, 2007
"... Standard machine learning techniques typically require ample training data in the form of labeled instances. In many situations it may be too tedious or costly to obtain sufficient labeled data for adequate classifier performance. However, in text classification, humans can easily guess the relevanc ..."
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Cited by 24 (1 self)
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Standard machine learning techniques typically require ample training data in the form of labeled instances. In many situations it may be too tedious or costly to obtain sufficient labeled data for adequate classifier performance. However, in text classification, humans can easily guess the relevance of features, that is, words that are indicative of a topic, thereby enabling the classifier to focus its feature weights more appropriately in the absence of sufficient labeled data. We will describe an algorithm for tandem learning that begins with a couple of labeled instances, and then at each iteration recommends features and instances for a human to label. Tandem learning using an “oracle ” results in much better performance than learning on only features or only instances. We find that humans can emulate the oracle to an extent that results in performance (accuracy) comparable to that of the oracle. Our unique experimental design helps factor out system error from human error, leading to a better understanding of when and why interactive feature selection works. 1.
Multiclass learning by probabilistic embeddings
- In Advances in Neural Information Processing Systems 15
, 2002
"... We describe a new algorithmic framework for learning multiclass catego-rization problems. In this framework a multiclass predictor is composed of a pair of embeddings that map both instances and labels into a common space. In this space each instance is assigned the label it is nearest to. We outlin ..."
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Cited by 19 (0 self)
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We describe a new algorithmic framework for learning multiclass catego-rization problems. In this framework a multiclass predictor is composed of a pair of embeddings that map both instances and labels into a common space. In this space each instance is assigned the label it is nearest to. We outline and analyze an algorithm, termed Bunching, for learning the pair of embeddings from labeled data. A key construction in the analysis of the algorithm is the notion of probabilistic output codes, a generaliza-tion of error correcting output codes (ECOC). Furthermore, the method of multiclass categorization using ECOC is shown to be an instance of Bunching. We demonstrate the advantage of Bunching over ECOC by comparing their performance on numerous categorization problems. 1
A unified approach to active dual supervision for labeling features and examples
- In European conference on Machine learning and knowledge discovery in databases
, 2010
"... Abstract. When faced with the task of building accurate classifiers, active learning is often a beneficial tool for minimizing the requisite costs of human annotation. Traditional active learning schemes query a human for labels on intelligently chosen examples. However, human effort can also be exp ..."
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Cited by 18 (3 self)
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Abstract. When faced with the task of building accurate classifiers, active learning is often a beneficial tool for minimizing the requisite costs of human annotation. Traditional active learning schemes query a human for labels on intelligently chosen examples. However, human effort can also be expended in collecting alternative forms of annotation. For example, one may attempt to learn a text classifier by labeling words associated with a class, instead of, or in addition to, documents. Learning from two different kinds of supervision adds a challenging dimension to the problem of active learning. In this paper, we present a unified approach to such active dual supervision: determining which feature or example a classifier is most likely to benefit from having labeled. Empirical results confirm that appropriately querying for both example and feature labels significantly reduces overall human effort—beyond what is possible through traditional one-dimensional active learning. 1
