We present a model of morphological seg- mentation that jointly learns to segment and restore orthographic changes, e.g., funniest → fun-y-est. We term this form of analysis canon- ical segmentation and contrast it with the tra- ditional surface segmentation, which segments a surface form into a sequence of substrings, e.g., funniest → funn-i-est. We derive an im- portance sampling algorithm for approximate inference in the model and report experimental results on English, German and Indonesian
Morphological analysis (finding the component morphemes of a word and tagging morphemes with part-of...
This paper presents an algorithm for the unsuper-vised learning of a simple morphology of a nat-ural...
National audienceWe describe a method that automatically segments words into morphs. The algorithm o...
Recent theories of morphological processing have been dominated by the notion that morphologically c...
Most state-of-the-art systems today produce morphological analysis based only on orthographic patter...
Most state-of-the-art systems today produce morphological analysis based only on ortho-graphic patte...
We present labeled morphological segmentation—an alternative view of morphological processing that u...
We present a simple algorithm that is also psychologically plausible to perform un-supervised learni...
This article surveys work on Unsupervised Learning of Morphology. We define Unsupervised Learning of...
We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, th...
In this paper, we introduce a trie-structured Bayesian model for unsupervised morphological segmenta...
The field of statistical natural language processing has been turning toward morpholog-ically rich l...
International audienceMathematical Morphology (MM) offers a generic theoretical framework for data p...
| openaire: EC/H2020/771113/EU//FoTranIn our submission to the SIGMORPHON 2022 Shared Task on Morphe...
Many Uralic languages have a rich morphological structure, but lack tools of morphological analysis ...
Morphological analysis (finding the component morphemes of a word and tagging morphemes with part-of...
This paper presents an algorithm for the unsuper-vised learning of a simple morphology of a nat-ural...
National audienceWe describe a method that automatically segments words into morphs. The algorithm o...
Recent theories of morphological processing have been dominated by the notion that morphologically c...
Most state-of-the-art systems today produce morphological analysis based only on orthographic patter...
Most state-of-the-art systems today produce morphological analysis based only on ortho-graphic patte...
We present labeled morphological segmentation—an alternative view of morphological processing that u...
We present a simple algorithm that is also psychologically plausible to perform un-supervised learni...
This article surveys work on Unsupervised Learning of Morphology. We define Unsupervised Learning of...
We discuss data-driven morphological segmentation, in which word forms are segmented into morphs, th...
In this paper, we introduce a trie-structured Bayesian model for unsupervised morphological segmenta...
The field of statistical natural language processing has been turning toward morpholog-ically rich l...
International audienceMathematical Morphology (MM) offers a generic theoretical framework for data p...
| openaire: EC/H2020/771113/EU//FoTranIn our submission to the SIGMORPHON 2022 Shared Task on Morphe...
Many Uralic languages have a rich morphological structure, but lack tools of morphological analysis ...
Morphological analysis (finding the component morphemes of a word and tagging morphemes with part-of...
This paper presents an algorithm for the unsuper-vised learning of a simple morphology of a nat-ural...
National audienceWe describe a method that automatically segments words into morphs. The algorithm o...