scholarly journals When lexical statistics and the grammar conflict: Learning and repairing weight effects on stress

Language ◽  
2019 ◽  
Vol 95 (4) ◽  
pp. 612-641 ◽  
Author(s):  
Guilherme Duarte Garcia
Keyword(s):  
Lexical Statistics

scholarly journals Emergent Hidden Grammar: Stochastic Patterning in Korean Accentuation of Novel Words

2010 ◽  
Vol 36 (1) ◽  
pp. 203
Author(s):  
Hyun-ju Kim
Keyword(s):  
Empirical Evidence ◽  
Implicit Knowledge ◽  
Corpus Study ◽  
Lexical Statistics ◽  
Structure Sensitive ◽  
Probabilistic Structure ◽  
Final Syllable ◽  
Novel Words

This study presents empirical evidence that the accent patterns in novel words do not originate from analogy to phonetically similar familiar words. Rather, the accent pattern of novel words reflects statistical patterning in the lexicon. A corpus study showed that lexical distribution of North Kyungsang Korean (NKK) accent patterns is phonologically patterned: penultimate accent is common where all the syllables are light; final accent is more frequent where the final syllable is heavy. Lexical statistics revealed probabilistic structure-sensitive patterning in the lexicon even if exceptions obscure the patterning. This study will show that the accent patterns in novel words actually match this statistical patterning in the lexicon. This indicates that NKK speakers have implicit knowledge of the statistical patterning and apply it to novel words which lack lexical specification.

SYNTAX AND LEXICAL STATISTICS IN ANAPHORA RESOLUTION

1995 ◽  
Vol 9 (6) ◽  
pp. 633-644 ◽  
Author(s):  
IDO DAGAN ◽  
JOHN JUSTESON ◽  
SHALOM LAPPIN ◽  
HERBERT LEASS ◽  
AMNON RIBAK
Keyword(s):  
Anaphora Resolution ◽  
Lexical Statistics

Emerging approaches in literature-based discovery: techniques and performance review

2017 ◽  
Vol 32 ◽  
Author(s):  
Yakub Sebastian ◽  
Eu-Gene Siew ◽  
Sylvester O. Orimaye
Keyword(s):  
Traditional Approach ◽  
Computational Techniques ◽  
Paradigm Shifts ◽  
Lexical Statistics ◽  
Knowledge Based ◽  
Research Areas ◽  
Discovery Research ◽  
Literature Based Discovery ◽  
And Performance ◽  
Technical Features

AbstractLiterature-based discovery systems aim at discovering valuable latent connections between previously disparate research areas. This is achieved by analyzing the contents of their respective literatures with the help of various intelligent computational techniques. In this paper, we review the progress of literature-based discovery research, focusing on understanding their technical features and evaluating their performance. The present literature-based discovery techniques can be divided into two general approaches: the traditional approach and the emerging approach. The traditional approach, which dominate the current research landscape, comprises mainly of techniques that rely on utilizing lexical statistics, knowledge-based and visualization methods in order to address literature-based discovery problems. On the other hand, we have also observed the births of new trends and unprecedented paradigm shifts among the recently emerging literature-based discovery approach. These trends are likely to shape the future trajectory of the next generation literature-based discovery systems.

scholarly journals The Malay Lexicon Project: A database of lexical statistics for 9,592 words

2010 ◽  
Vol 42 (4) ◽  
pp. 992-1003 ◽  
Author(s):  
Melvin J. Yap ◽  
Susan J. Rickard Liow ◽  
Sajlia Binte Jalil ◽  
Siti Syuhada Binte Faizal
Keyword(s):  
Lexical Statistics

scholarly journals The Surfeit of the Stimulus: Analytic Biases Filter Lexical Statistics in Turkish Laryngeal Alternations

Language ◽  
2011 ◽  
Vol 87 (1) ◽  
pp. 84-125 ◽  
Author(s):  
Michael Becker ◽  
Nihan Ketrez ◽  
Andrew Nevins
Keyword(s):  
Lexical Statistics

scholarly journals Explaining sonority projection effects

Phonology ◽  
2011 ◽  
Vol 28 (2) ◽  
pp. 197-234 ◽  
Author(s):  
Robert Daland ◽  
Bruce Hayes ◽  
James White ◽  
Marc Garellek ◽  
Andrea Davis ◽  
...  
Keyword(s):  
Speech Perception ◽  
Speech Production ◽  
Computational Models ◽  
Lexical Statistics ◽  
Model Based ◽  
Onset Clusters

AbstractThe term sonority projection refers to behavioural distinctions speakers make between unattested phonological sequences on the basis of sonority. For example, among onset clusters, the well-formedness relation [bn]>[lb] is observed in speech perception, speech production and non-word acceptability (Davidson 2006, 2007, Berent et al.2007, Albright, ms). We begin by replicating the sonority projection effects in a non-word acceptability study. Then we evaluate the extent to which sonority projection is predicted by existing computational models of phonotactics (Coleman & Pierrehumbert 1997, Hayes & Wilson 2008, inter alia). We show that a model based only on lexical statistics can explain sonority projection in English without a pre-existing sonority sequencing principle. To do this, a model must possess (i) a featural system supporting sonority-based generalisations, and (ii) a context representation including syllabification or equivalent information.

Defying the stimulus: acquisition of complex onsets in Polish

Phonology ◽  
2017 ◽  
Vol 34 (2) ◽  
pp. 269-298 ◽  
Author(s):  
Gaja Jarosz
Keyword(s):  
Computational Models ◽  
Computational Analysis ◽  
The Other ◽  
Lexical Statistics ◽  
Phonological Features ◽  
Modelling Studies ◽  
Onset Clusters ◽  
Korean Speakers

Behavioural findings indicate that English, Mandarin and Korean speakers exhibit gradient sonority sequencing preferences among unattested initial clusters. While some have argued these results support an innate principle, recent modelling studies have questioned this conclusion, showing that computational models capable of inducing generalisations using abstract phonological features can detect these preferences from lexical statistics in the three languages. This paper presents a computational analysis of the development of initial clusters in Polish, which arguably presents a stronger test of these models. We show that (i) the statistics of Polish contradict the Sonority Sequencing Principle (SSP), favouring sonority plateaus, (ii) models that succeed in the other languages do not predict SSP preferences for Polish and (iii) children nonetheless exhibit sensitivity to the SSP, favouring onset clusters with larger sonority rises.

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