Corpus ID: 14886349

Unsupervised Morpheme Segmentation and Morphology Induction from Text Corpora Using Morfessor 1.0

@inproceedings{Creutz2005UnsupervisedMS,
  title={Unsupervised Morpheme Segmentation and Morphology Induction from Text Corpora Using Morfessor 1.0},
  author={Mathias Creutz and K. Lagus},
  year={2005}
}
In this work, we describe the first public version of the Morfessor software, which is a program that takes as input a corpus of unannotated text and produces a segmentation of the word forms observed in the text. The segmentation obtained often resembles a linguistic morpheme segmentation. Morfessor is not language-dependent. The number of segments per word is not restricted to two or three as in some other existing morphology learning models. The current version of the software essentially… Expand
289 Citations
INDUCING THE MORPHOLOGICAL LEXICON OF A NATURAL LANGUAGE FROM UNANNOTATED TEXT
  • 218
  • PDF
Automatic Morpheme Segmentation and Labeling in Universal Dependencies Resources
  • 3
  • Highly Influenced
  • PDF
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 15 REFERENCES
Morpheme Segmentation Gold Standards for Finnish and English
  • 49
  • PDF
INDUCING THE MORPHOLOGICAL LEXICON OF A NATURAL LANGUAGE FROM UNANNOTATED TEXT
  • 218
  • PDF
Induction of a Simple Morphology for Highly-Inflecting Languages
  • 80
  • PDF
Unsupervised Discovery of Morphemes
  • 355
  • PDF
A General Computational Model For Word-Form Recognition And Production
  • 628
  • Highly Influential
  • PDF
An Efficient, Probabilistically Sound Algorithm for Segmentation and Word Discovery
  • M. Brent
  • Computer Science
  • Machine Learning
  • 2004
  • 304
  • Highly Influential
  • PDF
On lexicon creation for turkish LVCSR
  • 57
  • PDF
Morphological Analysis for Statistical Machine Translation
  • Y. Lee
  • Computer Science
  • HLT-NAACL
  • 2004
  • 212
  • PDF
Unsupervised Segmentation of Words Using Prior Distributions of Morph Length and Frequency
  • 95
  • PDF
A Self-Organizing Japanese Word Segmenter using Heuristic Word Identification and Re-estimation
  • 10
  • PDF
...
1
2
...