The unsupervised morphology processing in the emerging mutant languages has the advantage over the human/supervised processing of being more agiler. The main drawback is, however, their accuracy. This article describes an unsupervised morphemes identification approach based on an intuitive and formal definition of event dependence. The input is no more than a plain text of the targeted language. Although the original objective of this work was classical Arabic, the test was conducted on an English set as well. Tests on these two languages show a very acceptable precision and recall. A deeper refinement of the output allowed 89% precision and 78% recall on Arabic
This work presents an algorithm for the unsupervised learning, or induction, of a simple morphology ...
In this paper we describe a method to morphologically segment highly agglutinating and inflectional ...
This paper contributes an approach for expressing non-concatenative morphological phenomena, such as...
We present a simple algorithm that is also psychologically plausible to perform un-supervised learni...
We present two methods for unsupervised segmentation of words into morpheme-like units. The model ...
We present two methods for unsupervised segmentation of words into morpheme-like units. The model ut...
We describe a simple method of unsupervised morpheme segmentation of words in an unknown language. A...
Morphemes are not independent units and attached to each other based on morphotactics. However, they...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
Abstract. We extend the unsupervised morpheme segmentation method Morfessor Baseline to account for ...
In this thesis, we mainly investigate the influence of using unsupervised morphological segmentation...
| openaire: EC/H2020/771113/EU//FoTranIn our submission to the SIGMORPHON 2022 Shared Task on Morphe...
Most state-of-the-art systems today produce morphological analysis based only on orthographic patter...
We present a language-independent and unsupervised algorithm for the segmenta-tion of words into mor...
Word segments are relevant cues for the au-tomatic acquisition of semantic relationships from morpho...
This work presents an algorithm for the unsupervised learning, or induction, of a simple morphology ...
In this paper we describe a method to morphologically segment highly agglutinating and inflectional ...
This paper contributes an approach for expressing non-concatenative morphological phenomena, such as...
We present a simple algorithm that is also psychologically plausible to perform un-supervised learni...
We present two methods for unsupervised segmentation of words into morpheme-like units. The model ...
We present two methods for unsupervised segmentation of words into morpheme-like units. The model ut...
We describe a simple method of unsupervised morpheme segmentation of words in an unknown language. A...
Morphemes are not independent units and attached to each other based on morphotactics. However, they...
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Comp...
Abstract. We extend the unsupervised morpheme segmentation method Morfessor Baseline to account for ...
In this thesis, we mainly investigate the influence of using unsupervised morphological segmentation...
| openaire: EC/H2020/771113/EU//FoTranIn our submission to the SIGMORPHON 2022 Shared Task on Morphe...
Most state-of-the-art systems today produce morphological analysis based only on orthographic patter...
We present a language-independent and unsupervised algorithm for the segmenta-tion of words into mor...
Word segments are relevant cues for the au-tomatic acquisition of semantic relationships from morpho...
This work presents an algorithm for the unsupervised learning, or induction, of a simple morphology ...
In this paper we describe a method to morphologically segment highly agglutinating and inflectional ...
This paper contributes an approach for expressing non-concatenative morphological phenomena, such as...