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Document classification

Known as: Topic spotting, Text categorisation, Classification 
Document classification or document categorization is a problem in library science, information science and computer science. The task is to assign a… Expand
Wikipedia

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2019
Review
2019
We present FLAIR, an NLP framework designed to facilitate training and distribution of state-of-the-art sequence labeling, text… Expand
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Review
2019
Review
2019
In recent years, there has been an exponential growth in the number of complex documents and texts that require a deeper… Expand
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Review
2019
Review
2019
Time Series Classification (TSC) is an important and challenging problem in data mining. With the increase of time series data… Expand
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Review
2018
Review
2018
Although various techniques have been proposed to generate adversarial samples for white-box attacks on text, little attention… Expand
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Highly Cited
2016
Highly Cited
2016
We propose a hierarchical attention network for document classification. Our model has two distinctive characteristics: (i) it… Expand
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Highly Cited
2014
Highly Cited
2014
  • Quoc V. Le, Tomas Mikolov
  • ICML
  • 2014
  • Corpus ID: 2407601
Many machine learning algorithms require the input to be represented as a fixed-length feature vector. When it comes to texts… Expand
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Highly Cited
2004
Highly Cited
2004
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training… Expand
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Highly Cited
2001
Highly Cited
2001
We implemented versions of the SVM appropriate for one-class classification in the context of information retrieval. The… Expand
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Highly Cited
2000
Highly Cited
2000
From the publisher: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning… Expand
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Highly Cited
2000
Highly Cited
2000
In this paper we present a simple linear-time centroid-based document classification algorithm, that despite its simplicity and… Expand
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