An HMM-Based Multi-view Co-training Framework for Single-View Text Corpora

2016 ◽  
pp. 66-78
Author(s):  
Eva Lorenzo Iglesias ◽  
Adrían Seara Vieira ◽  
Lourdes Borrajo Diz
Keyword(s):  
Single View ◽  
Text Corpora

An HMM-based synthetic view generator to improve the efficiency of ensemble systems

2019 ◽  
Vol 28 (1) ◽  
pp. 4-18
Author(s):  
L Borrajo ◽  
A Seara Vieira ◽  
E L Iglesias
Keyword(s):  
Input Data ◽  
Markov Models ◽  
Multiple Views ◽  
Single Model ◽  
Single View ◽  
Ensembles Of Classifiers ◽  
Text Corpora ◽  
Standard Text ◽  
Synthetic View ◽  
Ensemble Systems

Abstract One of the most active areas of research in semi-supervised learning has been to study methods for constructing good ensembles of classifiers. Ensemble systems are techniques that create multiple models and then combine them to produce improved results. These systems usually produce more accurate solutions than a single model would. Specially, multi-view ensemble systems improve the accuracy of text classification because they optimize the functions to exploit different views of the same input data. However, despite being more promising than the single-view approaches, document datasets often have no natural multiple views available. This study proposes an algorithm to generate a synthetic view from a standard text dataset. The model generates a new view from the standard bag-of-words approach using an algorithm based on hidden Markov models (HMMs). To show the effectiveness of the proposed HMM-based synthetic view generation method, it has been integrated in a co-training ensemble system and tested with four text corpora: Reuters, 20 Newsgroup, TREC Genomics and OHSUMED. The results obtained are promising, showing a significant increase in the efficiency of the ensemble system compared to a single-view approach.

THE CONCEPTUALIZATION OF EMOTIONS AND AXIOLOGICAL CONCEPTS IN SLAVONIC PROVERBS AND IDIOMS: FROM CONSCIENCE TO ENVY

2019 ◽  
Vol 2019 (29) ◽  
pp. 248-271
Author(s):  
Oleh Tyshchenko
Keyword(s):  
Cultural Anthropology ◽  
Legal Aspect ◽  
English Text ◽  
Folk Culture ◽  
Cultural Concepts ◽  
Text Corpora ◽  
Psychic Disorders ◽  
Body And Soul ◽  
Germanic Languages ◽  
Better Than

The presented research reveals imagery-metaphoric and phraseological objectivities of the conceptual spheres Soul, Consciousness, Envy, Jealousy and Greed in Polish, Russian, Ukrainian, Czech and Slovak languages and conceptual picture of the world (first of all in proverbs and sayings, idioms, imagery means of secondary nomination both in standard language and its regional or dialectal variants) according to the indication of holistic characteristic and semantic intersection of these concepts. It describes the spheres of their typological coincidence and differences from the point of imagery motivation. It defines the symbolic functions of these ethno cultural concepts (object sphere) with respect to the specificity of manifestation of Envy in archaic texts, believes, in the language of traditional folk culture and archaic expressions with religious sense that reach Christian ideology, ideas of moral purity and dirt, Body and Soul. It has been defined the collocations with the components envy and jealousy in some thesauri and dictionaries in terms of the specificity of interlingual equivalence and expressions of envy and similar negative emotions and their functioning in the Ukrainian and English text corpora. The analysis demonstrated that practically in all compared languages and linguistic cultures Envy is associated with greed and jealousy, psychic disorders with a corresponding complex of feelings, expressed by metaphoric predicates of destruction and remorse that encode the moral and legal aspect of conscience (conscience is a judge, witness and executioner). Metaphor of Envy containing nominations of colours differ in the Slavonic and Germanic languages whereas those denoting spatial, gustatory, odour, acoustic and parametrical meaning are similar. Many imagery contexts of Envy correlate with such conceptual oppositions as richness and poverty, light and darkness; success is associated with the frames “foreign is better than domestic” where Envy encodes the meaning of encroachment upon another's property, “envy is better than sympathy”, “envy dominates where there are richness, success, welfare, happiness” which confirms the ideas of representatives in the field of psychoanalysis, cultural anthropology and sociology. In some languages the motives of black magic, evil eye (in Polish, Ukrainian and Russian) are rooted in the sphere of folk believes and invocations, as well as cultural anthroponyms.

Visual Analysis of Topic Transition among Different Sources of Text Corpora

2017 ◽  
Vol 29 (12) ◽  
pp. 2265
Author(s):  
Yi Zhang ◽  
Yudong Shao ◽  
Jiawan Zhang
Keyword(s):  
Visual Analysis ◽  
Text Corpora ◽  
Different Sources

Abstraction and Understanding of Indoor Scenes from Single-View RGB-D Scanning Data

2018 ◽  
Vol 30 (6) ◽  
pp. 1046
Author(s):  
Yuliang Sun ◽  
Yongwei Miao ◽  
Lijie Yu ◽  
Pajarola Renato
Keyword(s):  
Single View ◽  
Indoor Scenes

scholarly journals Minimal Solvers for Single-View Lens-Distorted Camera Auto-Calibration

2021 ◽  
Author(s):  
Yaroslava Lochman ◽  
Oles Dobosevych ◽  
Rostyslav Hryniv ◽  
James Pritts
Keyword(s):  
Single View

scholarly journals Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1556
Author(s):  
Zhengeng Yang ◽  
Hongshan Yu ◽  
Shunxin Cao ◽  
Qi Xu ◽  
Ding Yuan ◽  
...  
Keyword(s):  
Chronic Diseases ◽  
Volume Estimation ◽  
Portion Size ◽  
Inner Product ◽  
Probability Vector ◽  
Internal Reference ◽  
Single View ◽  
Unhealthy Diet ◽  
Simplified Procedure ◽  
Traditional Image

It is well known that many chronic diseases are associated with unhealthy diet. Although improving diet is critical, adopting a healthy diet is difficult despite its benefits being well understood. Technology is needed to allow an assessment of dietary intake accurately and easily in real-world settings so that effective intervention to manage being overweight, obesity, and related chronic diseases can be developed. In recent years, new wearable imaging and computational technologies have emerged. These technologies are capable of performing objective and passive dietary assessments with a much simplified procedure than traditional questionnaires. However, a critical task is required to estimate the portion size (in this case, the food volume) from a digital image. Currently, this task is very challenging because the volumetric information in the two-dimensional images is incomplete, and the estimation involves a great deal of imagination, beyond the capacity of the traditional image processing algorithms. In this work, we present a novel Artificial Intelligent (AI) system to mimic the thinking of dietitians who use a set of common objects as gauges (e.g., a teaspoon, a golf ball, a cup, and so on) to estimate the portion size. Specifically, our human-mimetic system “mentally” gauges the volume of food using a set of internal reference volumes that have been learned previously. At the output, our system produces a vector of probabilities of the food with respect to the internal reference volumes. The estimation is then completed by an “intelligent guess”, implemented by an inner product between the probability vector and the reference volume vector. Our experiments using both virtual and real food datasets have shown accurate volume estimation results.

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