Improved Automatic Keyword Extraction Given More Semantic Knowledge

2016 ◽  
pp. 112-125 ◽  
Author(s):  
Kai Yang ◽  
Zhenhong Chen ◽  
Yi Cai ◽  
DongPing Huang ◽  
Ho-fung Leung
Keyword(s):  
Semantic Knowledge ◽  
Keyword Extraction

Phonomotor Versus Semantic Feature Analysis Treatment for Anomia in 58 Persons With Aphasia: A Randomized Controlled Trial

2019 ◽  
Vol 62 (12) ◽  
pp. 4464-4482 ◽  
Author(s):  
Diane L. Kendall ◽  
Megan Oelke Moldestad ◽  
Wesley Allen ◽  
Janaki Torrence ◽  
Stephen E. Nadeau
Keyword(s):  
Randomized Controlled Trial ◽  
Response Latency ◽  
Controlled Trial ◽  
Semantic Knowledge ◽  
Semantic Feature ◽  
Feature Analysis ◽  
Group Differences ◽  
Randomized Controlled ◽  
Confrontation Naming ◽  
Semantic Feature Analysis

Purpose The ultimate goal of anomia treatment should be to achieve gains in exemplars trained in the therapy session, as well as generalization to untrained exemplars and contexts. The purpose of this study was to test the efficacy of phonomotor treatment, a treatment focusing on enhancement of phonological sequence knowledge, against semantic feature analysis (SFA), a lexical-semantic therapy that focuses on enhancement of semantic knowledge and is well known and commonly used to treat anomia in aphasia. Method In a between-groups randomized controlled trial, 58 persons with aphasia characterized by anomia and phonological dysfunction were randomized to receive 56–60 hr of intensively delivered treatment over 6 weeks with testing pretreatment, posttreatment, and 3 months posttreatment termination. Results There was no significant between-groups difference on the primary outcome measure (untrained nouns phonologically and semantically unrelated to each treatment) at 3 months posttreatment. Significant within-group immediately posttreatment acquisition effects for confrontation naming and response latency were observed for both groups. Treatment-specific generalization effects for confrontation naming were observed for both groups immediately and 3 months posttreatment; a significant decrease in response latency was observed at both time points for the SFA group only. Finally, significant within-group differences on the Comprehensive Aphasia Test–Disability Questionnaire ( Swinburn, Porter, & Howard, 2004 ) were observed both immediately and 3 months posttreatment for the SFA group, and significant within-group differences on the Functional Outcome Questionnaire ( Glueckauf et al., 2003 ) were found for both treatment groups 3 months posttreatment. Discussion Our results are consistent with those of prior studies that have shown that SFA treatment and phonomotor treatment generalize to untrained words that share features (semantic or phonological sequence, respectively) with the training set. However, they show that there is no significant generalization to untrained words that do not share semantic features or phonological sequence features.

Sources of Semantic Knowledge

2008 ◽  
Author(s):  
Simon De Deyne ◽  
Gert Storms
Keyword(s):  
Semantic Knowledge

Automatic Keyword Extraction from Medical and Healthcare Curriculum

2016 ◽  
Author(s):  
Martin Komenda ◽  
Matěj Karolyi ◽  
Andrea Pokorná ◽  
Martin Víta ◽  
Vincent Kríž
Keyword(s):  
Keyword Extraction ◽  
Medical And Healthcare

How Well Do Distributional Models Capture Different Types of Semantic Knowledge?

2015 ◽  
Author(s):  
Dana Rubinstein ◽  
Effi Levi ◽  
Roy Schwartz ◽  
Ari Rappoport
Keyword(s):  
Semantic Knowledge ◽  
Different Types

The Integration of Arithmetic Knowledge and Semantic Knowledge in Addition Facts

2013 ◽  
Vol 44 (6) ◽  
pp. 720-734
Author(s):  
Xu-Qian CHEN ◽  
Ben-Xuan HE ◽  
Ji-Jia ZHANG
Keyword(s):  
Semantic Knowledge ◽  
Addition Facts

scholarly journals Semantic knowledge based reasoning framework for human robot collaboration

Procedia CIRP ◽  
2021 ◽  
Vol 97 ◽  
pp. 373-378
Author(s):  
Sharath Chandra Akkaladevi ◽  
Matthias Plasch ◽  
Michael Hofmann ◽  
Andreas Pichler
Keyword(s):  
Semantic Knowledge ◽  
Knowledge Based ◽  
Human Robot Collaboration

scholarly journals Context-Aware Holographic Communication Based on Semantic Knowledge Extraction

2021 ◽  
Author(s):  
Agata Manolova ◽  
Krasimir Tonchev ◽  
Vladimir Poulkov ◽  
Sudhir Dixir ◽  
Peter Lindgren
Keyword(s):  
Deep Neural Networks ◽  
Data Gathering ◽  
Communication Technologies ◽  
Knowledge Extraction ◽  
Semantic Knowledge ◽  
Business Model Innovation ◽  
Context Aware ◽  
Human Body Model ◽  
Network Resource ◽  
Body Model

AbstractAugmented, mixed and virtual reality are changing the way people interact and communicate. Five dimensional communications and services, integrating information from all human senses are expected to emerge, together with holographic communications (HC), providing a truly immersive experience. HC presents a lot of challenges in terms of data gathering and transmission, demanding Artificial Intelligence empowered communication technologies such as 5G. The goal of the paper is to present a model of a context-aware holographic architecture for real time communication based on semantic knowledge extraction. This architecture will require analyzing, combining and developing methods and algorithms for: 3D human body model acquisition; semantic knowledge extraction with deep neural networks to predict human behaviour; analysis of biometric modalities; context-aware optimization of network resource allocation for the purpose of creating a multi-party, from-capturing-to-rendering HC framework. We illustrate its practical deployment in a scenario that can open new opportunities in user experience and business model innovation.

scholarly journals Keyword extraction method for machine reading comprehension based on natural language processing

2021 ◽  
Vol 1955 (1) ◽  
pp. 012072
Author(s):  
Ruiheng Li ◽  
Xuan Zhang ◽  
Chengdong Li ◽  
Zhongju Zheng ◽  
Zihang Zhou ◽  
...  
Keyword(s):  
Reading Comprehension ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Extraction Method ◽  
Keyword Extraction ◽  
Machine Reading
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