14,650 research outputs found
Methods and algorithms for unsupervised learning of morphology
This is an accepted manuscript of a chapter published by Springer in Computational Linguistics and Intelligent Text Processing. CICLing 2014. Lecture Notes in Computer Science, vol 8403 in 2014 available online: https://doi.org/10.1007/978-3-642-54906-9_15
The accepted version of the publication may differ from the final published version.This paper is a survey of methods and algorithms for unsupervised learning of morphology. We provide a description of the methods and algorithms used for morphological segmentation from a computational linguistics point of view. We survey morphological segmentation methods covering methods based on MDL (minimum description length), MLE (maximum likelihood estimation), MAP (maximum a posteriori), parametric and non-parametric Bayesian approaches. A review of the evaluation schemes for unsupervised morphological segmentation is also provided along with a summary of evaluation results on the Morpho Challenge evaluations.Published versio
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Minimally supervised induction of morphology through bitexts
textA knowledge of morphology can be useful for many natural language processing systems. Thus, much effort has been expended in developing accurate computational tools for morphology that lemmatize, segment and generate new forms. The most powerful and accurate of these have been manually encoded, such endeavors being without exception expensive and time-consuming. There have been consequently many attempts to reduce this cost in the development of morphological systems through the development of unsupervised or minimally supervised algorithms and learning methods for acquisition of morphology. These efforts have yet to produce a tool that approaches the performance of manually encoded systems.
Here, I present a strategy for dealing with morphological clustering and segmentation in a minimally supervised manner but one that will be more linguistically informed than previous unsupervised approaches. That is, this study will attempt to induce clusters of words from an unannotated text that are inflectional variants of each other. Then a set of inflectional suffixes by part-of-speech will be induced from these clusters. This level of detail is made possible by a method known as alignment and transfer (AT), among other names, an approach that uses aligned bitexts to transfer linguistic resources developed for one language–the source language–to another language–the target. This approach has a further advantage in that it allows a reduction in the amount of training data without a significant degradation in performance making it useful in applications targeted at data collected from endangered languages. In the current study, however, I use English as the source and German as the target for ease of evaluation and for certain typlogical properties of German. The two main tasks, that of clustering and segmentation, are approached as sequential tasks with the clustering informing the segmentation to allow for greater accuracy in morphological analysis.
While the performance of these methods does not exceed the current roster of unsupervised or minimally supervised approaches to morphology acquisition, it attempts to integrate more learning methods than previous studies. Furthermore, it attempts to learn inflectional morphology as opposed to derivational morphology, which is a crucial distinction in linguistics.Linguistic
Morphology-inspired Unsupervised Gland Segmentation via Selective Semantic Grouping
Designing deep learning algorithms for gland segmentation is crucial for
automatic cancer diagnosis and prognosis, yet the expensive annotation cost
hinders the development and application of this technology. In this paper, we
make a first attempt to explore a deep learning method for unsupervised gland
segmentation, where no manual annotations are required. Existing unsupervised
semantic segmentation methods encounter a huge challenge on gland images: They
either over-segment a gland into many fractions or under-segment the gland
regions by confusing many of them with the background. To overcome this
challenge, our key insight is to introduce an empirical cue about gland
morphology as extra knowledge to guide the segmentation process. To this end,
we propose a novel Morphology-inspired method via Selective Semantic Grouping.
We first leverage the empirical cue to selectively mine out proposals for gland
sub-regions with variant appearances. Then, a Morphology-aware Semantic
Grouping module is employed to summarize the overall information about the
gland by explicitly grouping the semantics of its sub-region proposals. In this
way, the final segmentation network could learn comprehensive knowledge about
glands and produce well-delineated, complete predictions. We conduct
experiments on GlaS dataset and CRAG dataset. Our method exceeds the
second-best counterpart over 10.56% at mIOU.Comment: MICCAI 2023 Accepte
Computer Aided ECG Analysis - State of the Art and Upcoming Challenges
In this paper we present current achievements in computer aided ECG analysis
and their applicability in real world medical diagnosis process. Most of the
current work is covering problems of removing noise, detecting heartbeats and
rhythm-based analysis. There are some advancements in particular ECG segments
detection and beat classifications but with limited evaluations and without
clinical approvals. This paper presents state of the art advancements in those
areas till present day. Besides this short computer science and signal
processing literature review, paper covers future challenges regarding the ECG
signal morphology analysis deriving from the medical literature review. Paper
is concluded with identified gaps in current advancements and testing, upcoming
challenges for future research and a bullseye test is suggested for morphology
analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on
computer as a tool, 1-4 July 2013, Zagreb, Croati
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