A corpus study of kasama ‘companion’ in Tagalog

2020 ◽  
Vol 46 (2) ◽  
pp. 240-298
Author(s):  
Sergei Klimenko
Keyword(s):  
Main Clause ◽  
Corpus Study ◽  
Case Marking ◽  
Different Types ◽  
Adverbial Clauses ◽  
Semantic Distinction

Abstract This paper presents a corpus-based study of a number of different types of previously undescribed constructions formed with the Tagalog noun kasama ‘companion’. Apart from independent and attributive uses, kasama frequently occurs as the predicate of an adjunct clause that can introduce a comitative participant, a semantically depictive secondary predicate, an event-oriented adjunct, or a predicative complement. The study analyses the frequency of kasama in all of these types of constructions and looks into their specific properties. This includes: the semantic distinction between additive and inclusory constructions with kasama; animacy agreement between arguments of kasama in additive constructions; variation in case marking of arguments of kasama; the preponderance of the absence of linkers – commonly known to introduce adverbial clauses in Tagalog – which are used to attach the kasama clause to the main clause; attested controllers of the kasama clause; positions available for the kasama clause in the sentence. Variation in case marking and compatibility with linkers suggests a classification of Tagalog adjunct clauses similar to that of Tagalog adverbials and prepositions. There is also some evidence to believe that kasama is being grammaticalized as a preposition. Comitative and semantically depictive constructions with kasama, which account for a quarter of the corpus sample, have never been studied before, despite the fact that Tagalog is included in several typological studies on comitative and depictive constructions.

scholarly journals A taxonomy of antonymy in Arabic: Egyptian and Saudi proverbs in comparison

2021 ◽  
Vol 7 (1) ◽  
pp. 200-222
Author(s):  
Hamada Hassanein ◽  
Mohammad Mahzari
Keyword(s):  
Main Clause ◽  
The Other ◽  
Subordinate Clause ◽  
Future Research ◽  
Case Marking ◽  
News Discourse ◽  
Discourse Functions ◽  
Syntactic Frames

Abstract This study has set out to identify, quantify, typify, and exemplify the discourse functions of canonical antonymy in Arabic paremiography by comparing two manually collected datasets from Egyptian and Saudi (Najdi) dialects. Building upon Jones’s (2002) most extensive and often-cited classification of the discourse functions of antonyms as they co-occur within syntactic frames in news discourse, the study has substantially revised this classification and developed a provisional and dynamic typology thereof. Two major textual functions are found to be quantitatively significant and qualitatively preponderant: ancillarity (wherein an A-pair of canonical antonyms project their antonymicity onto a more important B-pair) and coordination (wherein one antonym holds an inclusive or exhaustive relation to another antonym). Three new functions have been developed and added to the retrieved classification: subordination (wherein one antonym occurs in a subordinate clause while the other occurs in a main clause), case-marking (wherein two opposite cases are served by two antonyms), and replacement (wherein one antonym is substituted with another). Semicanonical and noncanonical guises of antonymy are left and recommended for future research.

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

Explanatory Power for Medical Expert Systems: Studies in the Representation of Causal Relationships for Clinical Consultations

1982 ◽  
Vol 21 (03) ◽  
pp. 127-136 ◽  
Author(s):  
J. W. Wallis ◽  
E. H. Shortliffe
Keyword(s):  
Explanatory Power ◽  
Prototype System ◽  
Inference Rules ◽  
Medical Expert ◽  
Comprehensive Knowledge ◽  
Different Types ◽  
Medical Expert Systems ◽  
Explanation System ◽  
Systems Studies

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Preventing corneal blindness caused by keratitis using artificial intelligence

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Jiewei Jiang ◽  
Kuan Chen ◽  
Qianqian Chen ◽  
Qinxiang Zheng ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Vision Loss ◽  
Learning System ◽  
Automated Classification ◽  
Slit Lamp ◽  
Resource Limited ◽  
The World ◽  
Different Types ◽  
Corneal Blindness

AbstractKeratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

NEURAL CLASSIFICATION OF MASS ABNORMALITIES WITH DIFFERENT TYPES OF FEATURES IN DIGITAL MAMMOGRAPHY

2006 ◽  
Vol 06 (01) ◽  
pp. 61-75 ◽  
Author(s):  
R. PANCHAL ◽  
B. VERMA
Keyword(s):  
Breast Cancer ◽  
Research Work ◽  
Breast Cancer Diagnosis ◽  
Classification Rate ◽  
Test Dataset ◽  
Benchmark Database ◽  
Different Types ◽  
Breast Tissues ◽  
Mass Type

Early detection of breast abnormalities remains the primary prevention against breast cancer despite the advances in breast cancer diagnosis and treatment. Presence of mass in breast tissues is highly indicative of breast cancer. The research work presented in this paper investigates the significance of different types of features using proposed neural network based classification technique to classify mass type of breast abnormalities in digital mammograms into malignant and benign. 14 gray level based features, four BI-RADS features, patient age feature and subtlety value feature have been explored using the proposed research methodology to attain maximum classification on test dataset. The proposed research technique attained a 91% testing classification rate with a 100% training classification rate on digital mammograms taken from the DDSM benchmark database.

scholarly journals Space Precession Target Classification Based on Radar High-Resolution Range Profiles

2019 ◽  
Vol 2019 ◽  
pp. 1-9
Author(s):  
Yizhe Wang ◽  
Cunqian Feng ◽  
Yongshun Zhang ◽  
Sisan He
Keyword(s):  
Parameter Extraction ◽  
Classification Performance ◽  
Support Vector ◽  
Electromagnetic Data ◽  
Feature Extractor ◽  
Different Types ◽  
Radar Echo ◽  
High Level ◽  
Cone Target

Precession is a common micromotion form of space targets, introducing additional micro-Doppler (m-D) modulation into the radar echo. Effective classification of space targets is of great significance for further micromotion parameter extraction and identification. Feature extraction is a key step during the classification process, largely influencing the final classification performance. This paper presents two methods for classifying different types of space precession targets from the HRRPs. We first establish the precession model of space targets and analyze the scattering characteristics and then compute electromagnetic data of the cone target, cone-cylinder target, and cone-cylinder-flare target. Experimental results demonstrate that the support vector machine (SVM) using histograms of oriented gradient (HOG) features achieves a good result, whereas the deep convolutional neural network (DCNN) obtains a higher classification accuracy. DCNN combines the feature extractor and the classifier itself to automatically mine the high-level signatures of HRRPs through a training process. Besides, the efficiency of the two classification processes are compared using the same dataset.

Lexicographer’s Lacunas or How to Deal with Missing Representative Dictionary Forms on the Example of Czech

2019 ◽  
Author(s):  
Dominika Kováříková ◽  
Michal Škrabal ◽  
Václav Cvrček ◽  
Lucie Lukešová ◽  
Jiří Milička
Keyword(s):  
Grammatical Category ◽  
Manual Analysis ◽  
Different Types ◽  
Word Forms ◽  
Frequency Threshold

Abstract When compiling a list of headwords, every lexicographer comes across words with an unattested representative dictionary form in the data. This study focuses on how to distinguish between the cases when this form is missing due to a lack of data and when there are some systemic or linguistic reasons. We have formulated lexicographic recommendations for different types of such ‘lacunas’ based on our research carried out on Czech written corpora. As a prerequisite, we calculated a frequency threshold to find words that should have the representative form attested in the data. Based on a manual analysis of 2,700 nouns, adjectives and verbs that do not, we drew up a classification of lacunas. The reasons for a missing dictionary form are often associated with limited collocability and non-preference for the representative grammatical category. Findings on unattested word forms also have significant implications for language potentiality.

scholarly journals ECG Prediction Based on Classification via Neural Networks and Linguistic Fuzzy Logic Forecaster

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Eva Volna ◽  
Martin Kotyrba ◽  
Hashim Habiballa
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Ecg Signals ◽  
Detection Systems ◽  
Language Interpretation ◽  
Different Types ◽  
Time Courses ◽  
Natural Language Interpretation ◽  
Unique Capability

The paper deals with ECG prediction based on neural networks classification of different types of time courses of ECG signals. The main objective is to recognise normal cycles and arrhythmias and perform further diagnosis. We proposed two detection systems that have been created with usage of neural networks. The experimental part makes it possible to load ECG signals, preprocess them, and classify them into given classes. Outputs from the classifiers carry a predictive character. All experimental results from both of the proposed classifiers are mutually compared in the conclusion. We also experimented with the new method of time series transparent prediction based on fuzzy transform with linguistic IF-THEN rules. Preliminary results show interesting results based on the unique capability of this approach bringing natural language interpretation of particular prediction, that is, the properties of time series.

scholarly journals French adverbial clauses, rescue by ellipsis and the truncation versus intervention debate

Probus ◽  
2015 ◽  
Vol 27 (1) ◽  
Author(s):  
J.-Marc Authier ◽  
Liliane Haegeman
Keyword(s):  
Main Clause ◽  
Left Periphery ◽  
Empirical Basis ◽  
Relativized Minimality ◽  
Adverbial Clauses

AbstractThis paper investigates the restrictions on movement to the left periphery found in non-root environments such as French central adverbial clauses and argues that an analysis of main clause phenomena based on intervention/Relativized Minimality is to be preferred to one based on structural truncation. The empirical basis for this claim consists of an examination of some asymmetries between French infinitival TP ellipsis and infinitival TP Topicalization. Adopting Authie's (2011) approach to TP ellipsis whereby the to-be-elided TP undergoes fronting in the computational component but fails to be spelled out at PF, we argue that these asymmetries follow from the fact that in French, while a spelled out fronted TP is an intervener for

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