scholarly journals Learning with Properties: Restrictiveness and Typological Structure*

2020 ◽  
Vol 8 ◽  
Author(s):  
Natalie DelBusso
Keyword(s):  
Learning Algorithm ◽  
Property Values ◽  
Property Analysis ◽  
Level Information ◽  
Typological Structure ◽  
Phonological System

A learner's task is to find the most restrictive grammar consistent with the data of their language. This paper develops an OT learning algorithm that incorporates typological-level information from Property Analysis to increase restrictiveness and successfully learn subset languages. Based on Tesar's (2014) Output-Driven Learner (ODL), Property-ODL (PODL) uses ERCs taken from property values encoding specific markedness > faithfulness rankings. PODL was tested in a learning simulation for the phonological system in Tesar (2014), Paka, which presents the challenging case of languages in paradigmatic subset relations. In ODL, these require additional methods to be learned. PODL eliminates the need for these in learning the paradigmatic subsets and overall reduces the use of less-tested methods in learning the grammars of the typology. 

The Final-over-Final Condition, Stringency, and Typological Structure

2020 ◽  
Vol 51 (4) ◽  
pp. 765-784
Author(s):  
Natalie DelBusso
Keyword(s):  
Internal Structure ◽  
Optimality Theory ◽  
Theoretical Explanation ◽  
Structure Parameter ◽  
Property Theory ◽  
Empirical Generalization ◽  
Property Analysis ◽  
Typological Structure

The Final-over-Final Condition (FOFC; Biberauer, Holmberg, and Roberts 2014 , et seq.) describes an empirical generalization about possible crosslinguistic word orders. This article presents an Optimality Theory account that derives FOFC using constraints in a stringency relationship. It analyzes the resulting typology through Property Theory ( Alber, DelBusso, and Prince 2016 , Alber and Prince in preparation ). A property analysis explicates the internal structure of the typological space, showing how it explains the condition and how the same structure occurs more generally in stringency systems. The theoretical explanation is compared with that in another theory of typological structure, Parameter Hierarchies ( Roberts 2012 ).

scholarly journals The Holographic Principle: Typological Analysis Using Lower Dimensions

2018 ◽  
Vol 5 ◽  
Author(s):  
Nazarre Merchant ◽  
Martin Krämer
Keyword(s):  
Property Values ◽  
Holographic Principle ◽  
Complete Analysis ◽  
Property Theory ◽  
Property Analysis ◽  
Two Systems

A moderately complex factorial typology may consist of hundreds of languages which can opaquely encode linguistically salient categories and generalizations. We propose in this paper that complex typologies can be decomposed and understood using what we call the holographic principle: a large typology can be projected onto simplified versions of itself which can be completely understood using Property Theory (Alber & Prince 2016). The simplified versions can then be re-incorporated into the original in such a way that the properties of the simple are maintained and provide a framework for analyzing the full system.In this paper, we demonstrate this using two systems, a basic stringency system (BSS), and a coda stringency system (CSS). We show how a complete analysis of BSS, using Property Theory, provides fundamental insights into the more complicated CSS which BSS is a simplification of. A property analysis is a set of properties that divide the languages of the typology in such a way that each language and its grammar can be identified uniquely by its property values. Such an analysis identifies the crucial rankings among constraints that distinguish all grammars of the typology so that languages that share property values share extensional traits. 

scholarly journals Corruption in the Times of Pandemia

2020 ◽  
Author(s):  
Jorge Gallego ◽  
Mounu Prem ◽  
Juan F. Vargas
Keyword(s):  
Learning Algorithm ◽  
Public Procurement ◽  
Public Spending ◽  
Difference In Differences ◽  
Cost Overruns ◽  
The Public ◽  
Average Value ◽  
Level Information ◽  
The Times ◽  
Identification Strategy

The public health and economic crisis caused by the COVID-19 pandemic has pushed governments to substantially and swiftly increase spending. Consequently, public procurement rules have been relaxed in many places to expedite transactions. However, this may also create opportunities for inefficiency and corruption. Using contract-level information on public spending from Colombia’s e-procurement platform, and a difference-in-differences identification strategy, we find that municipalities classified by a machine learning algorithm as more prone to corruption react to the spending surge by using a larger proportion of discretionary non-competitive contracts and increasing their average value, especially to procure crisis-related items. Additionally, in places that rank higher on our corruption scale, contracts signed during the emergency are more likely to have cost overruns, be awarded to campaign donors, and exhibit implementation inefficiencies. Our evidence suggests that these negative shocks may increase waste and corruption, and thus governments should bolster instances of monitoring and oversight.

The Nature of Deviant Articulation

1973 ◽  
Vol 38 (2) ◽  
pp. 156-161 ◽  
Author(s):  
Laurence B. Leonard
Keyword(s):  
Clinical Implications ◽  
Phonological System

This paper examines the credibility of deviant articulation as a less mature phonological system and as an individual phonological system with its own rules. Evidence is presented suggesting that both types of deviant phonological systems may occur in the articulatory defective population. The clinical implications of each type of deviant system are presented.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Design of English text-to-speech conversion algorithm based on machine learning

2020 ◽  
pp. 1-12
Author(s):  
Li Dongmei
Keyword(s):  
Machine Learning ◽  
Speech Synthesis ◽  
Feature Recognition ◽  
Learning Algorithm ◽  
Morphological Structure ◽  
English Text ◽  
Text To Speech ◽  
Part Of Speech ◽  
Modern Computer ◽  
Conversion Algorithm

English text-to-speech conversion is the key content of modern computer technology research. Its difficulty is that there are large errors in the conversion process of text-to-speech feature recognition, and it is difficult to apply the English text-to-speech conversion algorithm to the system. In order to improve the efficiency of the English text-to-speech conversion, based on the machine learning algorithm, after the original voice waveform is labeled with the pitch, this article modifies the rhythm through PSOLA, and uses the C4.5 algorithm to train a decision tree for judging pronunciation of polyphones. In order to evaluate the performance of pronunciation discrimination method based on part-of-speech rules and HMM-based prosody hierarchy prediction in speech synthesis systems, this study constructed a system model. In addition, the waveform stitching method and PSOLA are used to synthesize the sound. For words whose main stress cannot be discriminated by morphological structure, label learning can be done by machine learning methods. Finally, this study evaluates and analyzes the performance of the algorithm through control experiments. The results show that the algorithm proposed in this paper has good performance and has a certain practical effect.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

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