Document Classification Through Interactive Supervision of Document and Term Labels

Godbole, Shantanu; Harpale, Abhay; Sarawagi, Sunita; Chakrabarti, Soumen

doi:10.1007/978-3-540-30116-5_19

Cited by 38 publications

(29 citation statements)

References 8 publications

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“…By the creation of one-term pseudo-documents, Godbole et al [60] coerce the notion of feature label uncertainty into a more traditional instance uncertainty framework for text classification tasks. By incorporating the label information on each feature value with unlabeled examples, Druck et al…”

Section: Feature-based Learning and Active Dual Supervisionmentioning

confidence: 99%

Class Imbalance and Active Learning

2013

Imbalanced Learning

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Abstract:The performance of a predictive model is tightly coupled with the data used during training. While using more examples in the training will often result in a better informed, more accurate model; limits on computer memory and real-world costs associated with gathering labeled examples often constrain the amount of data that can be used for training. In settings where the number of training examples is limited, it often becomes meaningful to carefully see just which examples are Q1 selected. In active learning (AL), the model itself places a hands-on role in the selection of examples for labeling from a large pool of unlabeled examples. These examples are used for model training. Numerous studies have demonstrated, both empirically and theoretically, the benefits of AL: Given a fixed budget, a training system that interactively involves the current model in selecting the training examples can often result in a far greater accuracy than a system that simply selects random training examples. Imbalanced settings provide special opportunities and challenges for AL. For example, while AL can be used to build models that counteract the harmful effects of learning under class imbalance, extreme class imbalance can cause an AL strategy to "fail," preventing the selection scheme from choosing any useful examples for labeling. This chapter focuses on the interaction between AL and class imbalance, discussing (i) AL techniques designed specifically for dealing with imbalanced settings, (ii) strategies that leverage AL to overcome the deleterious effects of class imbalance, (iii) how extreme class imbalance can prevent AL systems from selecting useful examples, and alternatives to AL in these cases.

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Section: Feature-based Learning and Active Dual Supervisionmentioning

confidence: 99%

Class Imbalance and Active Learning

2013

Imbalanced Learning

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“…Godbole et al [16] present a document annotation method through interactive supervision of document and term labels. Actually their method is based on active learning of SVM algorithm, which actively collects user opinion on feature representations as well as whole-document labels to minimize the user's annotation efforts.…”

Section: Hierarchy Generationmentioning

confidence: 99%

“…Among them, the most commonly used method is active learning [14,34,45,16]. In active learning, the machine prompts the most informative document for the user to label.…”

Section: Introductionmentioning

confidence: 99%

An integrated system for building enterprise taxonomies

Zhang

Liu

et al. 2007

Inf Retrieval

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Although considerable research has been conducted in the field of hierarchical text categorization, little has been done on automatically collecting labeled corpus for building hierarchical taxonomies. In this paper, we propose an automatic method of collecting training samples to build hierarchical taxonomies. In our method, the category node is initially defined by some keywords, the web search engine is then used to construct a small set of labeled documents, and a topic tracking algorithm with keyword-based content normalization is applied to enlarge the training corpus on the basis of the seed documents. We also design a method to check the consistency of the collected corpus. The above steps produce a flat category structure 1 which contains all the categories for building the hierarchical taxonomy.Next, linear discriminant projection approach is utilized to construct more meaningful intermediate levels of hierarchies in the generated flat set of categories. Experimental results show that the training corpus is good enough for statistical classification methods.

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“…HIClass consists of roughly 5000 lines of C++ code for the backend which communicates through XML with 1000 lines of PHP scripts to manage browser-based front-end user interactions [8].…”

Section: Description Of the Demonstrationmentioning

confidence: 99%

“…We extend active learning to include feature engineering and multi-labeled document labeling conversations. HIClass is an interactive multi-class multi-labeled text classification system that combines the cognitive power of humans with the power of automated learners to make statistically sound classification decisions (details appear in [8]). …”

Section: Introductionmentioning

confidence: 99%

HIClass: Hyper-interactive Text Classification by Interactive Supervision of Document and Term Labels

Godbole

Harpale

Sarawagi

et al. 2004

Lecture Notes in Computer Science

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Abstract. We present the HIClass (Hyper Interactive text Classification) system, an interactive text classification system which combines the cognitive power of humans with the power of automated learners to make statistically sound classification decisions. HIClass is based on active learning principles and has aids for detailed analysis and fine tuning of text classifiers while exerting a low cognitive load on the user.

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Document Classification Through Interactive Supervision of Document and Term Labels

Cited by 38 publications

References 8 publications

Class Imbalance and Active Learning

Class Imbalance and Active Learning

An integrated system for building enterprise taxonomies

HIClass: Hyper-interactive Text Classification by Interactive Supervision of Document and Term Labels

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