3,475 research outputs found
DOC: Deep Open Classification of Text Documents
Traditional supervised learning makes the closed-world assumption that the
classes appeared in the test data must have appeared in training. This also
applies to text learning or text classification. As learning is used
increasingly in dynamic open environments where some new/test documents may not
belong to any of the training classes, identifying these novel documents during
classification presents an important problem. This problem is called open-world
classification or open classification. This paper proposes a novel deep
learning based approach. It outperforms existing state-of-the-art techniques
dramatically.Comment: accepted at EMNLP 201
Lifelong Learning CRF for Supervised Aspect Extraction
This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain markedly better than the traditional CRF without using this prior
knowledge. The key innovation is that even after CRF training, the model can
still improve its extraction with experiences in its applications.Comment: Accepted at ACL 2017. arXiv admin note: text overlap with
arXiv:1612.0794
Open-world Learning and Application to Product Classification
Classic supervised learning makes the closed-world assumption, meaning that
classes seen in testing must have been seen in training. However, in the
dynamic world, new or unseen class examples may appear constantly. A model
working in such an environment must be able to reject unseen classes (not seen
or used in training). If enough data is collected for the unseen classes, the
system should incrementally learn to accept/classify them. This learning
paradigm is called open-world learning (OWL). Existing OWL methods all need
some form of re-training to accept or include the new classes in the overall
model. In this paper, we propose a meta-learning approach to the problem. Its
key novelty is that it only needs to train a meta-classifier, which can then
continually accept new classes when they have enough labeled data for the
meta-classifier to use, and also detect/reject future unseen classes. No
re-training of the meta-classifier or a new overall classifier covering all old
and new classes is needed. In testing, the method only uses the examples of the
seen classes (including the newly added classes) on-the-fly for classification
and rejection. Experimental results demonstrate the effectiveness of the new
approach.Comment: accepted by The Web Conference (WWW 2019) Previous title: Learning to
Accept New Classes without Trainin
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