Skip to search formSkip to main content
You are currently offline. Some features of the site may not work correctly.

Text segmentation

Known as: Chinese word segmentation, Word segmentation, Word splitting 
Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. The term applies both to mental… Expand
Wikipedia

Related topics

Related topics

15 relations
Cluster analysisDelimiterDocument classificationHidden Markov model
Expand

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2016
Highly Cited
2016
Neural Machine Translation of Rare Words with Subword Units
Neural machine translation (NMT) models typically operate with a fixed vocabulary, but translation is an open-vocabulary problem… Expand
  • figure 1
  • table 2
  • table 1
  • table 3
  • table 4
Is this relevant?
Highly Cited
2009
Highly Cited
2009
Text line and word segmentation of handwritten documents
In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 7
Is this relevant?
Highly Cited
2007
Highly Cited
2007
Text mining techniques for patent analysis
Patent documents contain important research results. However, they are lengthy and rich in technical terminology such that it… Expand
  • table 1
  • table 2
  • figure 1
  • table 3
  • figure 2
Is this relevant?
Highly Cited
2006
Highly Cited
2006
Contextual Dependencies in Unsupervised Word Segmentation
Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages… Expand
  • figure 1
  • figure 2
  • figure 3
  • table 1
  • table 2
Is this relevant?
Highly Cited
2004
Highly Cited
2004
Statistical Models for Text Segmentation
This paper introduces a new statistical approach to automatically partitioning text into coherent segments. The approach is based… Expand
Is this relevant?
Highly Cited
2002
Highly Cited
2002
A Critique and Improvement of an Evaluation Metric for Text Segmentation
The Pk evaluation metric, initially proposed by Beeferman, Berger, and Lafferty (1997), is becoming the standard measure for… Expand
Is this relevant?
Highly Cited
2001
Highly Cited
2001
A Statistical Model for Domain-Independent Text Segmentation
We propose a statistical method that finds the maximum-probability segmentation of a given text. This method does not require… Expand
  • figure 1
  • table 2
  • table 3
Is this relevant?
Highly Cited
2000
Highly Cited
2000
Advances in domain independent linear text segmentation
This paper describes a method for linear text segmentation which is twice as accurate and over seven times as fast as the state… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2000
Highly Cited
2000
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to… Expand
  • figure 1
  • table 1
  • table 2
  • table 4
Is this relevant?
Highly Cited
1999
Highly Cited
1999
Using Maximum Entropy for Text Classification
This paper proposes the use of maximum entropy techniques for text classification. Maximum entropy is a probability distribution… Expand
  • table 2
  • figure 1
  • figure 2
  • figure 3
Is this relevant?
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License
ACCEPT & CONTINUE