Thresholds for vowel formant discrimination using a sentence classification task

2005 ◽  
Vol 118 (3) ◽  
pp. 1929-1930
Author(s):  
Diane Kewley‐Port ◽  
Eric Oglesbee ◽  
Jae Hee Lee
Keyword(s):  
Classification Task ◽  
Vowel Formant ◽  
Sentence Classification

scholarly journals Assessing document and sentence readability in less resourced languages and across textual genres

2014 ◽  
Vol 165 (2) ◽  
pp. 163-193 ◽  
Author(s):  
Felice Dell’Orletta ◽  
Simonetta Montemagni ◽  
Giulia Venturi
Keyword(s):  
Assessment Tool ◽  
High Accuracy ◽  
Document Classification ◽  
Ranking Method ◽  
Classification Task ◽  
Text Readability ◽  
Syntactic Information ◽  
Text Features ◽  
Sentence Classification ◽  
Readability Assessment

In this paper, we tackle three underresearched issues of the automatic readability assessment literature, namely the evaluation of text readability in less resourced languages, with respect to sentences (as opposed to documents) as well as across textual genres. Different solutions to these issues have been tested by using and refining READ‑IT, the first advanced readability assessment tool for Italian, which combines traditional raw text features with lexical, morpho-syntactic and syntactic information. In READ‑IT readability assessment is carried out with respect to both documents and sentences, with the latter constituting an important novelty of the proposed approach: READ‑IT shows a high accuracy in the document classification task and promising results in the sentence classification scenario. By comparing the results of two versions of READ‑IT, adopting a classification‑ versus ranking-based approach, we also show that readability assessment is strongly influenced by textual genre; for this reason a genre-oriented notion of readability is needed. With classification-based approaches, reliable results can only be achieved with genre-specific models: Since this is far from being a workable solution, especially for less resourced languages, a new ranking method for readability assessment is proposed, based on the notion of distance.

scholarly journals Translations as Additional Contexts for Sentence Classification

2018 ◽  
Author(s):  
Reinald Kim Amplayo ◽  
Kyungjae Lee ◽  
Jinyoung Yeo ◽  
Seung-won Hwang
Keyword(s):  
Classification Performance ◽  
The Other ◽  
Classification Task ◽  
Data Sets ◽  
Multiple Data ◽  
Multiple Context ◽  
Multiple Data Sets ◽  
Classification Tasks ◽  
Sentence Classification

In sentence classification tasks, additional contexts, such as the neighboring sentences, may improve the accuracy of the classifier. However, such contexts are domain-dependent and thus cannot be used for another classification task with an inappropriate domain. In contrast, we propose the use of translated sentences as domain-free context that is always available regardless of the domain. We find that naive feature expansion of translations gains only marginal improvements and may decrease the performance of the classifier, due to possible inaccurate translations thus producing noisy sentence vectors. To this end, we present multiple context fixing attachment (MCFA), a series of modules attached to multiple sentence vectors to fix the noise in the vectors using the other sentence vectors as context. We show that our method performs competitively compared to previous models, achieving best classification performance on multiple data sets. We are the first to use translations as domain-free contexts for sentence classification.

scholarly journals Multi-hop assortativities for network classification

2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
Author(s):  
Leonardo Gutiérrez-Gómez ◽  
Jean-Charles Delvenne
Keyword(s):  
Machine Learning ◽  
Scientific Collaboration ◽  
State Of The Art ◽  
Medical Engineering ◽  
Research Field ◽  
Classification Task ◽  
Collaboration Network ◽  
Structural Patterns ◽  
Art Methods

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.

Exploring vowel formant estimation through simulation-based techniques

2020 ◽  
Vol 6 (s1) ◽  
Author(s):  
Tyler Kendall ◽  
Charlotte Vaughn
Keyword(s):  
Predictive Coding ◽  
Linear Predictive Coding ◽  
Fine Grained ◽  
Vowel Formant ◽  
Simulation Based ◽  
Insight Into

AbstractThis paper contributes insight into the sources of variability in vowel formant estimation, a major analytic activity in sociophonetics, by reviewing the outcomes of two simulations that manipulated the settings used for linear predictive coding (LPC)-based vowel formant estimation. Simulation 1 explores the range of frequency differences obtained when minor adjustments are made to LPC settings, and measurement timepoints around the settings used by trained analysts, in order to determine the range of variability that should be expected in sociophonetic vowel studies. Simulation 2 examines the variability that emerges when LPC settings are varied combinatorially around constant default settings, rather than settings set by trained analysts. The impacts of different LPC settings are discussed as a way of demonstrating the inherent properties of LPC-based formant estimation. This work suggests that differences more fine-grained than about 10 Hz in F1 and 15–20 Hz in F2 are within the range of LPC-based formant estimation variability.

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