A multifactorial corpus analysis of adjective order in English

2003 ◽  
Vol 8 (2) ◽  
pp. 245-282 ◽  
Author(s):  
Stefanie Wulff
Keyword(s):  
Classification Accuracy ◽  
Corpus Analysis ◽  
Psychological Processes ◽  
Linear Discriminant ◽  
Corpus Linguistic ◽  
Naturally Occurring ◽  
Established Fact ◽  
Semantic Variables ◽  
Multifactorial Approach ◽  
Large Corpus

This paper is concerned with the question of which factors govern prenominal adjective order (AO) in English. In particular, the analysis aims to overcome shortfalls of previous analyses by, firstly, adopting a multifactorial approach integrating all variables postulated in the literature, thereby doing justice to the well-established fact that cognitive and psychological processes are multivariate and complex. Secondly, the phenomenon is investigated on the basis of a large corpus, rendering the results obtained more representative and valid of naturally occurring language than those of previous studies. To this end, corpus-linguistic operationalizations of phonological, syntactic, semantic and pragmatic determinants of AO are devised and entered into a Linear Discriminant Analysis, which determines the relative influence of all variables (semantic variables being most important) and yields a classification accuracy of 78%. Moreover, by means of the operationalizations developed in this analysis, the ordering of yet unanalyzed adjective strings can be predicted with about equal accuracy (73.5%).

Complementing corpus analysis with web-based experimentation in research on World Englishes

2019 ◽  
Vol 40 (1) ◽  
pp. 24-52 ◽  
Author(s):  
Stephanie Horch
Keyword(s):  
Experimental Data ◽  
Open Source Software ◽  
Previous Analysis ◽  
Experimental Methods ◽  
World Englishes ◽  
Corpus Analysis ◽  
Maze Task ◽  
Web Based ◽  
Corpus Linguistic ◽  
Large Corpus

Abstract Usage-based research in linguistics has to a large extent relied on corpus data. However, a feature’s “failure to appear in even a very large corpus (such as the Web) is not evidence for ungrammaticality, nor is appearance evidence for grammaticality” (Schütze and Sprouse 2013: 29). It is therefore advisable to complement corpus-based analyses with experimental data, so as to (ideally) obtain converging evidence. This paper reviews reasons for combining corpus linguistic with psycholinguistic experimental methods, and demonstrates how research on varieties of English can profit from experimentation. For a study of conversion in Asian Englishes, the maze task (Forster, Guerrera, and Elliot 2009; Forster 2010) was implemented with a web-based, open-source software. The results of the experiment dovetail with a previous analysis of the Corpus of Global Web-based English (Davies 2013). These results should encourage researchers not to base findings exclusively on corpus evidence, but corroborate them by means of experimental data.

scholarly journals A model for spectroscopic food sample analysis using data sonification

2021 ◽  
Author(s):  
Hsein Kew
Keyword(s):  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Reduction Method ◽  
Real Life ◽  
Principal Component ◽  
Relevant Information ◽  
Analysis Model ◽  
Linear Discriminant ◽  
Audio Output ◽  
Dimensionality Reduction Method

AbstractIn this paper, we propose a method to generate an audio output based on spectroscopy data in order to discriminate two classes of data, based on the features of our spectral dataset. To do this, we first perform spectral pre-processing, and then extract features, followed by machine learning, for dimensionality reduction. The features are then mapped to the parameters of a sound synthesiser, as part of the audio processing, so as to generate audio samples in order to compute statistical results and identify important descriptors for the classification of the dataset. To optimise the process, we compare Amplitude Modulation (AM) and Frequency Modulation (FM) synthesis, as applied to two real-life datasets to evaluate the performance of sonification as a method for discriminating data. FM synthesis provides a higher subjective classification accuracy as compared with to AM synthesis. We then further compare the dimensionality reduction method of Principal Component Analysis (PCA) and Linear Discriminant Analysis in order to optimise our sonification algorithm. The results of classification accuracy using FM synthesis as the sound synthesiser and PCA as the dimensionality reduction method yields a mean classification accuracies of 93.81% and 88.57% for the coffee dataset and the fruit puree dataset respectively, and indicate that this spectroscopic analysis model is able to provide relevant information on the spectral data, and most importantly, is able to discriminate accurately between the two spectra and thus provides a complementary tool to supplement current methods.

scholarly journals Use of machine learning to identify autism with natural social gaze behavior (Preprint)

2021 ◽  
Author(s):  
Zhong Zhao ◽  
Haiming Tang ◽  
Xiaobin Zhang ◽  
Xingda Qu ◽  
Jianping Lu
Keyword(s):  
Machine Learning ◽  
Eye Tracking ◽  
Classification Accuracy ◽  
Autism Spectrum ◽  
Support Vector ◽  
Gaze Behavior ◽  
Linear Discriminant ◽  
Face To Face ◽  
Area Of Interest ◽  
Social Gaze

BACKGROUND Abnormal gaze behavior is a prominent feature of the autism spectrum disorder (ASD). Previous eye tracking studies had participants watch images (i.e., picture, video and webpage), and the application of machine learning (ML) on these data showed promising results in identify ASD individuals. Given the fact that gaze behavior differs in face-to-face interaction from image viewing tasks, no study has investigated whether natural social gaze behavior could accurately identify ASD. OBJECTIVE The objective of this study was to examine whether and what area of interest (AOI)-based features extracted from the natural social gaze behavior could identify ASD. METHODS Both children with ASD and typical development (TD) were eye-tracked when they were engaged in a face-to-face conversation with an interviewer. Four ML classifiers (support vector machine, SVM; linear discriminant analysis, LDA; decision tree, DT; and random forest, RF) were used to determine the maximum classification accuracy and the corresponding features. RESULTS A maximum classification accuracy of 84.62% were achieved with three classifiers (LDA, DT and RF). Results showed that the mouth, but not the eyes AOI, was a powerful feature in detecting ASD. CONCLUSIONS Natural gaze behavior could be leveraged to identify ASD, suggesting that ASD might be objectively screened with eye tracking technology in everyday social interaction. In addition, the comparison between our and previous findings suggests that eye tracking features that could identify ASD might be culture dependent and context sensitive.

Sex Estimation Using Geometric Morphometrics

2021 ◽  
Author(s):  
Valda Black
Keyword(s):  
Geometric Morphometrics ◽  
Classification Accuracy ◽  
Function Analysis ◽  
Dorsal Surface ◽  
Two Dimensions ◽  
Pubic Bone ◽  
Biological Anthropology ◽  
Sex Estimation ◽  
Linear Discriminant ◽  
Shape Changes

Creating and testing efficient techniques for the sex estimation of modern human skeletal remains has been a significant focus in biological anthropology. It is well established that the innominate, particularly the pubic bone, is a sexually dimorphic part of the human skeleton, but prone to fragmentation. Using modern pubic bones of known age and sex, this study aims to capture shape differences using geometric morphometrics (GMM) to test classification accuracy of segments of the pubic bone. The sample consists of 70 left adult pubic bones from the William M. Bass Donated Skeletal Collection, with 35 males and 35 females of mixed age and population affinity. Landmarks were placed on the dorsal surface of the pubic body and ischiopubic ramus to capture their overall shape in two dimensions, so the study is easily replicable and applicable. The scans were separately run through a generalized Procrustes, principal components (PCA), and canonical linear discriminant function analysis (DFA). The DFA results show high classification accuracy for the pubic body (94% males, 100% females) and the ischiopubic ramus (100% females, 97% males), with the PCA DFA allowing a researcher to explore specific shape changes driving the differentiation between groups. GMM was able to quantify and successfully discriminant the shape changes between males and females for small elements of the pubis, which can be applied to fragmentary remains and future morphological methods.

Bio-Inspired Optimization Algorithms for Arabic Handwritten Characters

2017 ◽  
pp. 897-914 ◽  
Author(s):  
Ahmed.T. Sahlol ◽  
Aboul Ella Hassanien
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Random Forests ◽  
Classification Accuracy ◽  
Processing Time ◽  
Optimization Algorithms ◽  
Nearest Neighbors ◽  
Benchmark Dataset ◽  
K Nearest Neighbors ◽  
Linear Discriminant

There are still many obstacles for achieving high recognition accuracy for Arabic handwritten optical character recognition system, each character has a different shape, as well as the similarities between characters. In this chapter, several feature selection-based bio-inspired optimization algorithms including Bat Algorithm, Grey Wolf Optimization, Whale optimization Algorithm, Particle Swarm Optimization and Genetic Algorithm have been presented and an application of Arabic handwritten characters recognition has been chosen to see their ability and accuracy to recognize Arabic characters. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time. The experiments have been performed using a benchmark dataset, CENPARMI by k-Nearest neighbors, Linear Discriminant Analysis, and random forests. The achieved results show superior results for the selected features when comparing the classification accuracy for the selected features by the optimization algorithms with the whole feature set in terms of the classification accuracy and the processing time.

Equivalence between LDA/QR and Direct LDA

2012 ◽  
pp. 338-353
Author(s):  
Rong-Hua Li ◽  
Shuang Liang ◽  
George Baciu ◽  
Eddie Chan
Keyword(s):  
Discriminant Analysis ◽  
Dimension Reduction ◽  
Linear Discriminant Analysis ◽  
Classification Accuracy ◽  
Qr Decomposition ◽  
Linear Discriminant ◽  
Qr Algorithm ◽  
Special Cases ◽  
Benchmark Datasets ◽  
Pseudo Inverse

Singularity problems of scatter matrices in Linear Discriminant Analysis (LDA) are challenging and have obtained attention during the last decade. Linear Discriminant Analysis via QR decomposition (LDA/QR) and Direct Linear Discriminant analysis (DLDA) are two popular algorithms to solve the singularity problem. This paper establishes the equivalent relationship between LDA/QR and DLDA. They can be regarded as special cases of pseudo-inverse LDA. Similar to LDA/QR algorithm, DLDA can also be considered as a two-stage LDA method. Interestingly, the first stage of DLDA can act as a dimension reduction algorithm. The experiment compares LDA/QR and DLDA algorithms in terms of classification accuracy, computational complexity on several benchmark datasets and compares their first stages. The results confirm the established equivalent relationship and verify their capabilities in dimension reduction.

scholarly journals Decoding Attempted Hand Movements in Stroke Patients Using Surface Electromyography

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6763
Author(s):  
Mads Jochumsen ◽  
Imran Khan Niazi ◽  
Muhammad Zia ur Rehman ◽  
Imran Amjad ◽  
Muhammad Shafique ◽  
...  
Keyword(s):  
Neural Networks ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Intraclass Correlation ◽  
Motor Impairment ◽  
Wrist Flexion ◽  
Stroke Patients ◽  
Linear Discriminant

Brain- and muscle-triggered exoskeletons have been proposed as a means for motor training after a stroke. With the possibility of performing different movement types with an exoskeleton, it is possible to introduce task variability in training. It is difficult to decode different movement types simultaneously from brain activity, but it may be possible from residual muscle activity that many patients have or quickly regain. This study investigates whether nine different motion classes of the hand and forearm could be decoded from forearm EMG in 15 stroke patients. This study also evaluates the test-retest reliability of a classical, but simple, classifier (linear discriminant analysis) and advanced, but more computationally intensive, classifiers (autoencoders and convolutional neural networks). Moreover, the association between the level of motor impairment and classification accuracy was tested. Three channels of surface EMG were recorded during the following motion classes: Hand Close, Hand Open, Wrist Extension, Wrist Flexion, Supination, Pronation, Lateral Grasp, Pinch Grasp, and Rest. Six repetitions of each motion class were performed on two different days. Hudgins time-domain features were extracted and classified using linear discriminant analysis and autoencoders, and raw EMG was classified with convolutional neural networks. On average, 79 ± 12% and 80 ± 12% (autoencoders) of the movements were correctly classified for days 1 and 2, respectively, with an intraclass correlation coefficient of 0.88. No association was found between the level of motor impairment and classification accuracy (Spearman correlation: 0.24). It was shown that nine motion classes could be decoded from residual EMG, with autoencoders being the best classification approach, and that the results were reliable across days; this may have implications for the development of EMG-controlled exoskeletons for training in the patient’s home.

scholarly journals Detection of hemophilia carriers during pregnancy

Blood ◽  
1982 ◽  
Vol 60 (6) ◽  
pp. 1407-1410 ◽  
Author(s):  
LW Hoyer ◽  
CA Carta ◽  
MJ Mahoney
Keyword(s):  
Genetic Counseling ◽  
Prenatal Care ◽  
Classification Accuracy ◽  
Hemophilia A ◽  
Antenatal Diagnosis ◽  
Sufficient Accuracy ◽  
Blood Samples ◽  
Linear Discriminant ◽  
Normal Women ◽  
Hemophilia Carriers

Abstract The accuracy of hemophilia A carrier detection during pregnancy has been determined using combined measurement of VIII:CAg and VIIIR:Ag. These immunoassays detect determinants that are sufficiently stable in plasma that the assays could be done on frozen samples that had been obtained when women were seen for antenatal diagnosis studies (carrier women) or for routine prenatal care (controls). A linear discriminant was calculated that best separated the data for 32 normal women and 25 obligate carriers of the hemophilia gene. Twenty-three of 25 carriers (92%) and all 32 control women were correctly identified in this analysis. The overall classification accuracy (55/57, 96%) is comparable to that obtained by VIII:C and VIIIR:Ag measurements using freshly drawn blood samples in nonpregnant individuals. This study demonstrates that hemophilia A carriers can be detected during pregnancy with sufficient accuracy that the information may be used for genetic counseling.

scholarly journals EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity—A Machine Learning Approach

2020 ◽  
Vol 9 (12) ◽  
pp. 3934
Author(s):  
Jeong-Youn Kim ◽  
Hyun Seo Lee ◽  
Seung-Hwan Lee
Keyword(s):  
Resting State ◽  
Symptom Severity ◽  
Classification Accuracy ◽  
Brain Regions ◽  
Brain Network ◽  
Clustering Coefficient ◽  
Forward Selection ◽  
Linear Discriminant ◽  
Network Analyses ◽  
Local Clustering Coefficient

A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms.

scholarly journals An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jian Kui Feng ◽  
Jing Jin ◽  
Ian Daly ◽  
Jiale Zhou ◽  
Yugang Niu ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Classification Accuracy ◽  
Channel Selection ◽  
Selection Method ◽  
Selection Methods ◽  
Eeg Signals ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Novel Method ◽  
Electroencephalogram Eeg

Background. Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method. To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results. The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.

Sign in / Sign up

Export Citation Format

Share Document