Classic supervised learning makes the closed-world assumption that the classes seen in testing must have appeared in training. However, this assumption is often violated in real-world applications. For example, in a social media site, new topics emerge constantly and in e-commerce, new categories of products appear daily. A model that cannot detect new/unseen topics or products is hard to function well in such open environments. A desirable model working in such environments must be able to (1) reject examples from unseen classes (not appeared in training) and (2) incrementally learn the new/unseen classes to expand the existing model. This is called open-world learning (OWL). This paper proposes a new OWL method based on meta-learning. The key novelty is that the model maintains only a dynamic set of seen classes that allows new classes to be added or deleted with no need for model re-training. Each class is represented by a small set of training examples. In testing, the meta-classifier only uses the examples of the maintained seen classes (including the newly added classes) on-the-fly for classification and rejection. Experimental results with e-commerce product classification show that the proposed method is highly effective.
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Open Set Text Classification using Convolutional Neural Networks
In a closed world setting, classifiers are trained on examples from a number of classes and tested with unseen examples belonging to the same set of classes. However, in most real-world scenarios, a trained classifier is likely to come across novel examples that do not belong to any of the known classes. Such examples should ideally be categorized as belonging to an unknown class. The goal of an open set classifier is to anticipate and be ready to handle test examples of classes unseen during training. The classifier should be able to declare that a test example belongs to a class it does not know, and possi- bly, incorporate it into its knowledge as an example of a new class it has encoun- tered. There is some published research in open world image classification, but open set text classification remains mostly un- explored. In this paper, we investigate the suitability of Convolutional Neural Net- works (CNNs) for open set text classifi- cation. We find that CNNs are good fea- ture extractors and hence perform better than existing state-of-the-art open set clas- sifiers in smaller domains, although their open set classification abilities in general still need to be investigated.
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- Award ID(s):
- 1659788
- NSF-PAR ID:
- 10059464
- Date Published:
- Journal Name:
- International Conference on Natural Language Processing, 2017
- Page Range / eLocation ID:
- 466-475
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
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