Abductive Reasoning for Keyword Recovering in Semantic-Based Keyword Extraction

2008 ◽  
Author(s):  
Rachada Kongkachandra ◽  
Kosin Chamnongthai
Keyword(s):  
Abductive Reasoning ◽  
Keyword Extraction

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

Online Third Places: Supporting Well-Being Through Identifying and Managing Unintended Consequences

2021 ◽  
pp. 109467052110188
Author(s):  
Joy Parkinson ◽  
Lisa Schuster ◽  
Rory Mulcahy
Keyword(s):  
Well Being ◽  
Unintended Consequences ◽  
Abductive Reasoning ◽  
Service Research ◽  
Data Sets ◽  
Management Service ◽  
Consumer Empowerment ◽  
Online Service ◽  
Third Places ◽  
Third Place

Unintended consequences of service are important yet infrequently examined in transformative service research. This research examines an online service community that transformed into an online third place, with consumers socializing and forming lasting relationships. Using practice-informed theory-building and an abductive reasoning approach, findings are presented from both manual and automated coding of three qualitative data sets that form the basis of a case study examining an online weight management service forum. Extending beyond current conceptualizations of the third place, this study is the first to propose a framework delineating online third place characteristics and their impact on consumers’ eudaimonic (the capacity for self-realization) and hedonic (attainment of pleasure and avoidance of pain) well-being. Findings show that in the absence of a physical or virtual servicescape, social factors including social density, equity, and personalization are key to constructing an online third place that supports well-being through building social connections and enjoyment. The new framework provides guidance for service managers to transform their online service communities into online third places to support consumer well-being and to identify and manage potential unintended consequences, for example, by ensuring segmentation of the community based on consumer groups’ shared interests and consumer empowerment through participation.

scholarly journals Creative leaps in theory: the might of abduction

2021 ◽  
Author(s):  
Mario Veen
Keyword(s):  
Health Professions ◽  
Central Place ◽  
Health Professions Education ◽  
Abductive Reasoning ◽  
The Poor ◽  
The World

AbstractThis paper argues that abductive reasoning has a central place in theorizing Health Professions Education. At the root of abduction lies a fundamental debate: How do we connect practice, which is always singular and unique, with theory, which describes the world in terms of rules, generalizations, and universals? While abduction was initially seen as the ‘poor cousin’ of deduction and induction, ultimately it has something important to tell us about the role of imagination and humility in theorizing Health Professions Education. It is that which makes theory possible, because it allows us to ask what might be the case and calls attention to the role of creative leaps in theory. Becoming aware of the abductive reasoning we already perform in our research allows us to take the role of imagination—something rarely associated with theory—seriously.

scholarly journals Offensive keyword extraction based on the attention mechanism of BERT and the eigenvector centrality using a graph representation

2021 ◽  
Author(s):  
Gretel Liz De la Peña Sarracén ◽  
Paolo Rosso
Keyword(s):  
Language Model ◽  
Hybrid Approach ◽  
Attention Mechanism ◽  
Graph Representation ◽  
Keyword Extraction ◽  
Eigenvector Centrality ◽  
Model Learning ◽  
Harmful Content ◽  
The Impact ◽  
Offensive Language

AbstractThe proliferation of harmful content on social media affects a large part of the user community. Therefore, several approaches have emerged to control this phenomenon automatically. However, this is still a quite challenging task. In this paper, we explore the offensive language as a particular case of harmful content and focus our study in the analysis of keywords in available datasets composed of offensive tweets. Thus, we aim to identify relevant words in those datasets and analyze how they can affect model learning. For keyword extraction, we propose an unsupervised hybrid approach which combines the multi-head self-attention of BERT and a reasoning on a word graph. The attention mechanism allows to capture relationships among words in a context, while a language model is learned. Then, the relationships are used to generate a graph from what we identify the most relevant words by using the eigenvector centrality. Experiments were performed by means of two mechanisms. On the one hand, we used an information retrieval system to evaluate the impact of the keywords in recovering offensive tweets from a dataset. On the other hand, we evaluated a keyword-based model for offensive language detection. Results highlight some points to consider when training models with available datasets.

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