scholarly journals Automatic Key Term Extraction from Research Article using Hybrid Approach

2017 ◽  
Vol 166 (6) ◽  
pp. 17-21
Author(s):  
Selvani Deepthi ◽  
B. Rajesh ◽  
N. Vyshnavi ◽  
K. Moni
Keyword(s):  
Hybrid Approach ◽  
Term Extraction ◽  
Research Article

A New Semantic Relations-Based Hybrid Approach for Implicit Aspect Identification in Sentiment Analysis

2020 ◽  
Vol 19 (03) ◽  
pp. 2050019
Author(s):  
Hajar El Hannach ◽  
Mohammed Benkhalifa
Keyword(s):  
Sentiment Analysis ◽  
Hybrid Model ◽  
Opinion Mining ◽  
Short Term Memory ◽  
Hybrid Approach ◽  
Active Phase ◽  
Semantic Relations ◽  
Term Extraction ◽  
Supervised Classifiers ◽  
Restaurant Reviews

Within the next few years, sentiment analysis or opinion mining is set to become an important component of real-world applications for product manufacturers, e-commerce companies, and potential customers. Sentiment analysis deals with the computational assessment of people’s opinions apparent or hidden within the text according to three levels: document, sentence and aspect levels. The aspect-level is increasingly becoming an active phase of sentiment analysis. At this level, the aim is to determine the hidden target of opinion represented in datasets, known as aspect term identification. This paper proposes an original hybrid model combining semantic relations and frequency-based approach with supervised classifiers for implicit aspect identification (IAI). The proposed approach is directed towards improving the F1-performances for traditional supervised classifiers commonly used in this field based on eager and lazy learning, and deep learning technique using long short-term memory whit attention mechanism applied for IAI. Particularly, this work addresses aspect term extraction and aggregation, the two sub-tasks of IAI, involving adjectives and verbs. The effects of this approach are empirically examined on multiple datasets of electronic products and restaurant reviews with multiple aspect granularity levels. Comparing this method with similar approaches clearly shows the benefits of this method: (i) the use of an appropriately selected WordNet semantic relations of adjectives and verbs that significantly helps classifiers for IAI. (ii) Using the hybrid model helps classifiers better handle these selected WordNet semantic relations and therefore deal better with IAI.

scholarly journals HAMLET

Terminology ◽  
2021 ◽  
Author(s):  
Ayla Rigouts Terryn ◽  
Véronique Hoste ◽  
Els Lefever
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Hybrid Approach ◽  
Substantial Effect ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Term Extraction ◽  
Machine Learning Approach ◽  
Different Types

Abstract Automatic term extraction (ATE) is an important task within natural language processing, both separately, and as a preprocessing step for other tasks. In recent years, research has moved far beyond the traditional hybrid approach where candidate terms are extracted based on part-of-speech patterns and filtered and sorted with statistical termhood and unithood measures. While there has been an explosion of different types of features and algorithms, including machine learning methodologies, some of the fundamental problems remain unsolved, such as the ambiguous nature of the concept “term”. This has been a hurdle in the creation of data for ATE, meaning that datasets for both training and testing are scarce, and system evaluations are often limited and rarely cover multiple languages and domains. The ACTER Annotated Corpora for Term Extraction Research contain manual term annotations in four domains and three languages and have been used to investigate a supervised machine learning approach for ATE, using a binary random forest classifier with multiple types of features. The resulting system (HAMLET Hybrid Adaptable Machine Learning approach to Extract Terminology) provides detailed insights into its strengths and weaknesses. It highlights a certain unpredictability as an important drawback of machine learning methodologies, but also shows how the system appears to have learnt a robust definition of terms, producing results that are state-of-the-art, and contain few errors that are not (part of) terms in any way. Both the amount and the relevance of the training data have a substantial effect on results, and by varying the training data, it appears to be possible to adapt the system to various desired outputs, e.g., different types of terms. While certain issues remain difficult – such as the extraction of rare terms and multiword terms – this study shows how supervised machine learning is a promising methodology for ATE.

scholarly journals Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder

2021 ◽  
Vol 3 (3) ◽  
pp. 214-229
Author(s):  
B. Vivekanandam
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Performance Metrics ◽  
Research Output ◽  
Hybrid Approach ◽  
Disease Diagnosis ◽  
Great Accuracy ◽  
Multimodal Fusion ◽  
Research Article ◽  
Mri Characteristics

Alzheimer's Disorder (AD) may permanently impair memory cells, resulting in dementia. Researchers say that early Alzheimer's disease diagnosis is difficult. MRI is used to detect AD in clinical trials. It requires high discriminative MRI characteristics to accurately classify dementia stages. Due to the large extraction of features, improved deep CNN-based models have recently proven accurate. With fewer picture samples in the datasets, over-fitting issues arise, limiting the effectiveness of deep learning algorithms. This research article minimizes the overfitting error due to fusion techniques. This hybrid approach is used to classify Alzheimer's disease more accurately than other traditional approaches. Besides, the Convolutional Neural Network (CNN) provides more minute features of small changes in MRI scan images than any other algorithm. Therefore, the proposed algorithm provides great accuracy in the region of sagittal, coronal, and axial Mild Cognitive Impairments (MCI) in the brain segment classification. Moreover, this research article compares the proposed algorithm with previous research output that is used to help prove its superiority. The performance metrics uses Health Subject (HS), MCI, and Mini-Mental State Evaluation (MMSE) to evaluate the proposed research algorithm.

Surgical and endovascular hybrid approach in peripheral arterial disease of the lower limbs

VASA ◽  
2016 ◽  
Vol 45 (5) ◽  
pp. 417-422 ◽  
Author(s):  
Anouk Grandjean ◽  
Katia Iglesias ◽  
Céline Dubuis ◽  
Sébastien Déglise ◽  
Jean-Marc Corpataux ◽  
...  
Keyword(s):  
Mortality Rate ◽  
Peripheral Arterial Disease ◽  
Limb Salvage ◽  
Hybrid Approach ◽  
Arterial Disease ◽  
Early Mortality ◽  
Secondary Patency ◽  
Limb Salvage Rate ◽  
Peripheral Arterial ◽  
Salvage Rate

Abstract. Background: Multilevel peripheral arterial disease is frequently observed in patients with intermittent claudication or critical limb ischemia. This report evaluates the efficacy of one-stage hybrid revascularization in patients with multilevel arterial peripheral disease. Patients and methods: A retrospective analysis of a prospective database included all consecutive patients treated by a hybrid approach for a multilevel arterial peripheral disease. The primary outcome was the patency rate at 6 months and 1 year. Secondary outcomes were early and midterm complication rate, limb salvage and mortality rate. Statistical analysis, including a Kaplan-Meier estimate and univariate and multivariate Cox regression analyses were carried out with the primary, primary assisted and secondary patency, comparing the impact of various risk factors in pre- and post-operative treatments. Results: 64 patients were included in the study, with a mean follow-up time of 428 days (range: 4 − 1140). The technical success rate was 100 %. The primary, primary assisted and secondary patency rates at 1 year were 39 %, 66 % and 81 %, respectively. The limb-salvage rate was 94 %. The early mortality rate was 3.1 %. Early and midterm complication rates were 15.4 % and 6.4 %, respectively. The early mortality rate was 3.1 %. Conclusions: The hybrid approach is a major alternative in the treatment of peripheral arterial disease in multilevel disease and comorbid patients, with low complication and mortality rates and a high limb-salvage rate.

Analytical Study of Violence Theme in Victoria Schwab’s This Savage Song

2019 ◽  
Keyword(s):  
Analytical Study ◽  
Research Paper ◽  
The Novel ◽  
Vicious Cycle ◽  
Aggressive Approach ◽  
Research Article ◽  
The People ◽  
The Social ◽  
Different Dimensions ◽  
The Right

This research article focuses on the theme of violence and its representation by the characters of the novel “This Savage Song” by Victoria Schwab. How violence is transmitted through genes to next generations and to what extent socio- psycho factors are involved in it, has also been discussed. Similarly, in what manner violent events and deeds by the parents affect the psychology of children and how it inculcates aggressive behaviour in their minds has been studied. What role is played by the parents in grooming the personality of children and ultimately their decisions to choose the right or wrong way has been argued. In the light of the theory of Judith Harris, this research paper highlights all the phenomena involved: How the social hierarchy controls the behaviour. In addition, the aggressive approach of the people in their lives has been analyzed in the light of the study of second theorist Thomas W Blume. As the novel is a unique representation of supernatural characters, the monsters, which are the products of some cruel deeds, this research paper brings out different dimensions of human sufferings with respect to these supernatural beings. Moreover, the researcher also discusses that, in what manner the curse of violence creates an inevitable vicious cycle of cruel monsters that makes the life of the characters turbulent and miserable.

Delayed Closure of Multiple Muscular Ventricular Septal Defects in an Infant after Coarctation Repair and a Hybrid Procedure�A Case Report

2011 ◽  
Vol 14 (1) ◽  
pp. 67 ◽  
Author(s):  
Ireneusz Haponiuk ◽  
Maciej Chojnicki ◽  
Radosaw Jaworski ◽  
Jacek Juciski ◽  
Mariusz Steffek ◽  
...  
Keyword(s):  
Septal Defect ◽  
Color Doppler ◽  
Hybrid Approach ◽  
Ventricular Septal Defects ◽  
Color Doppler Echocardiography ◽  
Septal Defects ◽  
Coarctation Repair ◽  
Step Procedure ◽  
The Right ◽  
Delayed Closure

There are several strategies of surgical approach for the repair of multiple muscular ventricular septal defects (mVSDs), but none leads to a fully predictable, satisfactory therapeutic outcome in infants. We followed a concept of treating multiple mVSDs consisting of a hybrid approach based on intraoperative perventricular implantation of occluding devices. In this report, we describe a 2-step procedure consisting of a final hybrid approach for multiple mVSDs in the infant following initial coarctation repair with pulmonary artery banding in the newborn. At 7 months, sternotomy and debanding were performed, the right ventricle was punctured under transesophageal echocardiographic guidance, and the 8-mm device was implanted into the septal defect. Color Doppler echocardiography results showed complete closure of all VSDs by 11 months after surgery, probably via a mechanism of a localized inflammatory response reaction, ventricular septum growth, and implant endothelization.

Sign in / Sign up

Export Citation Format

Share Document