Text Classification with Active Learning

Novak, Blaž; Mladenić, Dunja; Grobelnik, Marko

doi:10.1007/3-540-31314-1_48

Cited by 9 publications

(3 citation statements)

References 3 publications

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“…We finally ended up with 1000 articles classified as science, 1000 as technology, 1000 as Intrinsic Science, 457 as Extrinsic Science, 158 as Intrinsic Technology and 802 as Extrinsic Technology. This process is known as 'active learning' (Novak et al, 2006), and it includes manual controls (classifying random samples). We carried out a k-fold cross-validation (k = 5) (Arlot and Celisse, 2010).…”

Section: Automatic Classificationmentioning

confidence: 99%

Ten years of science news: A longitudinal analysis of scientific culture in the Spanish digital press

2015

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This article presents our study of science coverage in the digital Spanish press over the last decade. We employed automated information retrieval procedures to create a corpus of 50,763 text units dealing with science and technology, and used automated text-analysis procedures in order to provide a general picture of the structure, characteristics and evolution of science news in Spain. We found between 6% and 7% of science coverage, a clear high proportion of biomedicine and predominance of science over technology, although we also detected an increase in technological content during the second half of the decade. Analysing the extrinsic and intrinsic features of science culture, we found a predominance of intrinsic features that still need further analysis. Our attempt to use specialised software to examine big data was effective, and allowed us to reach these preliminary conclusions.

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Section: Automatic Classificationmentioning

confidence: 99%

Ten years of science news: A longitudinal analysis of scientific culture in the Spanish digital press

2015

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“…Choosing when to stop the active learning process is most often simply dictated by the number of labels an oracle will provide (Novak et al, 2006) (often referred to as a label budget). There are, however, other approaches based on performance achieved on a hold-out test set (Campbell et al, 2000), although these suffer from the difficulty of getting labelled examples which necessitates the use of active learning in the first place.…”

Section: Related Workmentioning

confidence: 99%

Active learning for text classification with reusability

Hu¹,

Namee

Delany³

2016

Expert Systems with Applications

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Where active learning with uncertainty sampling is used to generate training sets for classification applications, it is sensible to use the same type of classifier to select the most informative training examples as the type of classifier that will be used in the final classification application. There are scenarios, however, where this might not be possible, for example due to computational complexity. Such scenarios give rise to the reusability problem-are the training examples deemed most informative by one classifier type necessarily as informative for a different classifier types? This paper describes a novel exploration of the reusability problem in text classification scenarios. We measure the impact of using different classifier types in the active learning process and in the classification applications that use the results of active learning. We perform experiments on four different text classification problems, using the three classifier types most commonly used for text classification. We find that the reusability problem is a significant issue in text classification; that, if possible, the same classifier type should be used both in the application and during the active learning process; and that, if the ultimate classifier type is unknown, support vector machines should be used in active learning to maximise reusability.

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“…These categories do not need to describe the data in details, the important thing is that they show to the system the user's view of the data -which documents are similar and which are different from the user's perspective. The process of manually marking the documents with categories is time consuming but can be significantly speeded up by the use of active learning [5], [8].…”

Section: Introductionmentioning

confidence: 99%

Background knowledge for ontology construction

Fortuna

Grobelnik

Mladenić

2006

Proceedings of the 15th International Conference on World Wide Web

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Abstract. In this paper we describe a solution for incorporating background knowledge into the OntoGen system for semi-automatic ontology construction. This makes it easier for different users to construct different and more personalized ontologies for the same domain. To achieve this we introduce a word weighting schema to be used in the document representation. The weighting schema is learned based on the background knowledge provided by user. It is than used by OntoGen's machine learning and text mining algorithms.

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Text Classification with Active Learning

Cited by 9 publications

References 3 publications

Ten years of science news: A longitudinal analysis of scientific culture in the Spanish digital press

Ten years of science news: A longitudinal analysis of scientific culture in the Spanish digital press

Active learning for text classification with reusability

Background knowledge for ontology construction

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