scholarly journals Modeling Maltese Noun Plural Classes without Morphemes

2020 ◽  
Author(s):  
Jessica Nieder ◽  
Fabian Tomaschek ◽  
Enum Cohrs ◽  
Ruben van de Vijver
Keyword(s):  
Neural Networks ◽  
Mental Lexicon ◽  
Classification Model ◽  
Discriminative Learning ◽  
Plural Noun ◽  
Model Classification ◽  
Computational Implementation ◽  
Semitic Language ◽  
Word Forms ◽  
Complex Words

Word and Paradigm morphology proposes that morphologically complex words are stored as wholes in the mental lexicon, and by doing so it avoids problems that are related to the notion of the morpheme. However, it is not yet clear to what degree it is possible to computationally model classification and production of complex word forms without morphemes.We take up this question by modeling the classification and production of the Maltese noun plural system. Maltese is a Semitic language that has two broad classes of plurals: concatenative ones and non-concatenative ones. We model the classification in two models, and the production in a third one. The first model for classification, the Tilburg Memory Based Learner (TiMBL), is a computational implementation of exemplar models. TiMBL is impartial as to the existence of morphemes in the mental lexicon, and allows us to directly compare the classification with and without morphemes. It turns out that the classification with and without morphemes of Maltese nouns is equally good. The second classification model is the Naive Discriminative Learner (NDL). NDL is a computational implementation of discriminative learning. It can be understood as an implementation of the Word and Paradigm model and thus eschews morphemes. It differs from TiMBL in its assumptions about learning, and they way in which the classifications are obtained. NDL, too, classifies Maltese nouns well. A classification task is very different from a production task, and therefore we used neural networks to model the production of plurals. In these models we address the question whether the production of a plural noun for a given singular can be modeled without recourse to morphemes. We used two neural networks architectures (LSTM and GRU) that have been applied to linguistic phenomena, and find that they are able to correctly produce plurals, without making use of morphemes.We conclude that the Maltese noun plural system can be modeled on the basis of whole words alone without morphemes. These results, therefore, support the Word and Paradigm theory of the mental lexicon.

scholarly journals Paradigmatic Relations Interact During the Production of Complex Words: Evidence From Variable Plurals in Dutch

2021 ◽  
Vol 12 ◽  
Author(s):  
Tim Zee ◽  
Louis ten Bosch ◽  
Ingo Plag ◽  
Mirjam Ernestus
Keyword(s):  
Mental Lexicon ◽  
Corpus Studies ◽  
Alternative Variant ◽  
Growing Body ◽  
Different Types ◽  
Word Forms ◽  
Strong Representation ◽  
Complex Words ◽  
Potential Interactions

A growing body of work in psycholinguistics suggests that morphological relations between word forms affect the processing of complex words. Previous studies have usually focused on a particular type of paradigmatic relation, for example the relation between paradigm members, or the relation between alternative forms filling a particular paradigm cell. However, potential interactions between different types of paradigmatic relations have remained relatively unexplored. This paper presents two corpus studies of variable plurals in Dutch to test hypotheses about potentially interacting paradigmatic effects. The first study shows that generalization across noun paradigms predicts the distribution of plural variants, and that this effect is diminished for paradigms in which the plural variants are more likely to have a strong representation in the mental lexicon. The second study demonstrates that the pronunciation of a target plural variant is affected by coactivation of the alternative variant, resulting in shorter segmental durations. This effect is dependent on the representational strength of the alternative plural variant. In sum, by exploring interactions between different types of paradigmatic relations, this paper provides evidence that storage of morphologically complex words may affect the role of generalization and coactivation during production.

scholarly journals Modeling morphological priming in German with naive discriminative learning

2019 ◽  
Author(s):  
R. H. Baayen ◽  
Eva Smolka
Keyword(s):  
Discriminative Learning ◽  
Connectionist Models ◽  
Semantic Similarity Measure ◽  
Computational Implementation ◽  
Morphological Priming ◽  
Semantic Compositionality ◽  
Set Up ◽  
Complex Words ◽  
Human Association ◽  
Prime Target

Both localist and connectionist models, based on experimental results obtained for English and French, assume that the degree of semantic compositionality of a morphologically complex word is reflected in how it is processed. Since priming experiments using English and French morphologically related prime-target pairs reveal stronger priming when complex words are semantically transparent (e.g., refill–fill ) compared to semantically more opaque pairs (e.g., restrain–strain), localist models set up connections between complex words and their stems only for semantically transparent pairs. Connectionist models have argued that the effect of transparency should arise as an epiphenomenon in PDP networks. However, for German, a series of studies has revealed equivalent priming for both transparent and opaque prime-target pairs, which suggests mediation of lexical access by the stem, independent of degrees of semantic compositionality. This study reports a priming experiment that replicates equivalent priming for transparent and opaque pairs. We show that these behavioral results can be straightforwardly modeled by a computational implementation of Word and Paradigm Morphology (WPM), Naive Discriminative Learning (NDL). Just as wpm, ndl eschews the theoretical construct of the morpheme. Ndl succeeds in modeling the German priming data by inspecting the extent to which a discrimination network pre-activates the target lexome from the orthographic properties of the prime. Measures derived from an ndl network, complemented with a semantic similarity measure derived from distributional semantics, predict lexical decision latencies with somewhat improved precision compared to classical measures such as word frequency, prime type, and human association ratings. We discuss both the methodological implications of our results, as well as their implications for models of the mental lexicon.

scholarly journals Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3279
Author(s):  
Maria Habib ◽  
Mohammad Faris ◽  
Raneem Qaddoura ◽  
Manal Alomari ◽  
Alaa Alomari ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Quality Assessment ◽  
Transcript Level ◽  
Assessment Process ◽  
Classification Model ◽  
Systematic Evaluation ◽  
Transcript Levels ◽  
Perceptual Evaluation

Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided consultations in an automated manner. The proposed model is developed using three sets of features: features extracted from the signal level, the transcript level, and the signal and transcript levels. At the signal level, various statistical and spectral information is calculated to characterize the spectral envelope of the speech recordings. At the transcript level, a pre-trained embedding model is utilized to encompass the semantic and contextual features of the textual information. Additionally, the hybrid of the signal and transcript levels is explored and analyzed. The designed classification model relies on stacked layers of deep neural networks and convolutional neural networks. Evaluation results show that the model achieved a higher level of precision when compared with the manual evaluation approach followed by Altibbi’s operations team.

scholarly journals Paradigmatic enhancement of stem vowels in regular English inflected verb forms

Morphology ◽  
2021 ◽  
Vol 31 (2) ◽  
pp. 171-199
Author(s):  
Fabian Tomaschek ◽  
Benjamin V. Tucker ◽  
Michael Ramscar ◽  
R. Harald Baayen
Keyword(s):  
Cognitive Psychology ◽  
Speech Production ◽  
Lexical Processing ◽  
Mental Lexicon ◽  
Morphological Processing ◽  
Word Structure ◽  
Verb Forms ◽  
Growing Body ◽  
Complex Words ◽  
Phonetic Realization

AbstractMany theories of word structure in linguistics and morphological processing in cognitive psychology are grounded in a compositional perspective on the (mental) lexicon in which complex words are built up during speech production from sublexical elements such as morphemes, stems, and exponents. When combined with the hypothesis that storage in the lexicon is restricted to the irregular, the prediction follows that properties specific to regular inflected words cannot co-determine the phonetic realization of these inflected words. This study shows that the stem vowels of regular English inflected verb forms that are more frequent in their paradigm are produced with more enhanced articulatory gestures in the midsaggital plane, challenging compositional models of lexical processing. The effect of paradigmatic probability dovetails well with the Paradigmatic Enhancement Hypothesis and is consistent with a growing body of research indicating that the whole is more than its parts.

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

Tobacco Leaf Grading Based on Deep Convolutional Neural Networks and Machine Vision

2021 ◽  
Vol 65 (1) ◽  
pp. 11-22
Author(s):  
Mengyao Lu ◽  
Shuwen Jiang ◽  
Cong Wang ◽  
Dong Chen ◽  
Tian’en Chen
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Classification Accuracy ◽  
Classification Model ◽  
List Type ◽  
Tobacco Leaves ◽  
Tobacco Leaf ◽  
Grading Model

HighlightsA classification model for the front and back sides of tobacco leaves was developed for application in industry.A tobacco leaf grading method that combines a CNN with double-branch integration was proposed.The A-ResNet network was proposed and compared with other classic CNN networks.The grading accuracy of eight different grades was 91.30% and the testing time was 82.180 ms, showing a relatively high classification accuracy and efficiency.Abstract. Flue-cured tobacco leaf grading is a key step in the production and processing of Chinese-style cigarette raw materials, directly affecting cigarette blend and quality stability. At present, manual grading of tobacco leaves is dominant in China, resulting in unsatisfactory grading quality and consuming considerable material and financial resources. In this study, for fast, accurate, and non-destructive tobacco leaf grading, 2,791 flue-cured tobacco leaves of eight different grades in south Anhui Province, China, were chosen as the study sample, and a tobacco leaf grading method that combines convolutional neural networks and double-branch integration was proposed. First, a classification model for the front and back sides of tobacco leaves was trained by transfer learning. Second, two processing methods (equal-scaled resizing and cropping) were used to obtain global images and local patches from the front sides of tobacco leaves. A global image-based tobacco leaf grading model was then developed using the proposed A-ResNet-65 network, and a local patch-based tobacco leaf grading model was developed using the ResNet-34 network. These two networks were compared with classic deep learning networks, such as VGGNet, GoogLeNet-V3, and ResNet. Finally, the grading results of the two grading models were integrated to realize tobacco leaf grading. The tobacco leaf classification accuracy of the final model, for eight different grades, was 91.30%, and grading of a single tobacco leaf required 82.180 ms. The proposed method achieved a relatively high grading accuracy and efficiency. It provides a method for industrial implementation of the tobacco leaf grading and offers a new approach for the quality grading of other agricultural products. Keywords: Convolutional neural network, Deep learning, Image classification, Transfer learning, Tobacco leaf grading

MONITORING AND CLASSIFYING THE STATE OF HARD DISKS USING RECURRENT NEURAL NETWORKS

2021 ◽  
pp. 36-43
Author(s):  
L. A. Demidova ◽  
A. V. Filatov
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Experimental Studies ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Dense Layer ◽  
Hard Disks ◽  
Destructive Testing ◽  
Data Set ◽  
State Classification

The article considers an approach to solving the problem of monitoring and classifying the states of hard disks, which is solved on a regular basis, within the framework of the concept of non-destructive testing. It is proposed to solve this problem by developing a classification model using machine learning algorithms, in particular, using recurrent neural networks with Simple RNN, LSTM and GRU architectures. To develop a classification model, a data set based on the values of SMART sensors installed on hard disks it used. It represents a group of multidimensional time series. At the same time, the structure of the classification model contains two layers of a neural network with one of the recurrent architectures, as well as a Dropout layer and a Dense layer. The results of experimental studies confirming the advantages of LSTM and GRU architectures as part of hard disk state classification models are presented.

Inflectional and Derivational Morphology in the Mental Lexicon: Symmetries and Asymmetries in Repetition Priming

1992 ◽  
Vol 44 (2) ◽  
pp. 373-390 ◽  
Author(s):  
H. Schriefers ◽  
A. Friederici ◽  
P. Graetz
Keyword(s):  
Repetition Priming ◽  
Mental Lexicon ◽  
Derivational Morphology ◽  
Lexical Organization ◽  
Priming Effects ◽  
Priming Paradigm ◽  
Complex Forms ◽  
Complex Words ◽  
Prime Target

Using a repetition priming paradigm, the interrelations between morphologically related words in the mental lexicon were examined in two experiments. In contrast to most previous studies, in which morphologically complex words occur as primes and stems as targets, derivationally and inflectionally complex forms were fully crossed in prime–target pairs. Experiment 1 showed asymmetries in the pattern of priming effects between different inflectional forms of German adjectives. Such asymmetries are problematic for any theory that assumes that all members of an inflectional paradigm share one entry in the mental lexicon. Experiment 2 contrasted derivational and inflectional variants of the same stems used in Experiment 1. Once again, there were same clear asymmetries in the pattern of priming effects. The implications of these results for models of lexical organization of inflectional and derivational morphology are discussed.

scholarly journals CROSS-LANGUAGE TEXT CLASSIFICATION WITH CONVOLUTIONAL NEURAL NETWORKS FROM SCRATCH

2017 ◽  
Vol 2 ◽  
pp. 24-33 ◽  
Author(s):  
Musbah Zaid Enweiji ◽  
Taras Lehinevych ◽  
Аndrey Glybovets
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Current Method ◽  
Classification Model ◽  
The Novel ◽  
Novel Approach ◽  
Multilingual Learning ◽  
Cross Language ◽  
Language Text ◽  
Language Classification

Cross language classification is an important task in multilingual learning, where documents in different languages often share the same set of categories. The main goal is to reduce the labeling cost of training classification model for each individual language. The novel approach by using Convolutional Neural Networks for multilingual language classification is proposed in this article. It learns representation of knowledge gained from languages. Moreover, current method works for new individual language, which was not used in training. The results of empirical study on large dataset of 21 languages demonstrate robustness and competitiveness of the presented approach.

How word frequency affects morphological processing in monolinguals and bilinguals

2003 ◽  
Vol 6 (3) ◽  
pp. 213-225 ◽  
Author(s):  
MINNA LEHTONEN ◽  
MATTI LAINE
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Morphological Processing ◽  
Decision Task ◽  
Frequency Range ◽  
Medium Frequency ◽  
Word Forms ◽  
Full Form ◽  
Differential Pattern ◽  
Complex Words

The present study investigated processing of morphologically complex words in three different frequency ranges in monolingual Finnish speakers and Finnish-Swedish bilinguals. By employing a visual lexical decision task, we found a differential pattern of results in monolinguals vs. bilinguals. Monolingual Finns seemed to process low frequency and medium frequency inflected Finnish nouns mostly by morpheme-based recognition but high frequency inflected nouns through full-form representations. In contrast, bilinguals demonstrated a processing delay for all inflections throughout the whole frequency range, suggesting decomposition for all inflected targets. This may reflect different amounts of exposure to the word forms in the two groups. Inflected word forms that are encountered very frequently will acquire full-form representations, which saves processing time. However, with the lower rates of exposure, which characterize bilingual individuals, full-form representations do not start to develop.

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