Safety and Domain Independence

Whereas individual differences in the degree of time discounting have been found to be meaningfully related to important outcome variables, some researchers have reported evidence that individual differences in time discounting cannot be generalized among domains - a phenomenon called domain independence. However, the Participant × Domain interaction and its importance in relation to the main effect of domain have never been studied. In the present paper, generalizability analysis is used for the first time to separate the sources of variance in time discounting choices (into differences between participants, domains, magnitudes, delays, and their interactions). Results show that the most important source of variance is the Participant × Domain interaction. Differences between participants and between magnitudes were also important. Thus, several sources of variance in time discounting choices should be acknowledged. Most importantly, people seem to differ in their reaction to domains.

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Towards enhancement of a lexicon-based approach for Saudi dialect sentiment analysis

Journal of Information Science ◽

10.1177/0165551516688143 ◽

2017 ◽

Vol 44 (2) ◽

pp. 184-202 ◽

Cited By ~ 12

Author(s):

Adel Assiri ◽

Ahmed Emam ◽

Hmood Al-Dossari

Keyword(s):

Sentiment Analysis ◽

Large Scale ◽

Training Data ◽

Linguistic Features ◽

Algorithm Development ◽

Domain Independence ◽

Low Performance

Sentiment analysis (SA) techniques are applied to assess aspects of language that are used to express feelings, evaluations and opinions in areas such as customer sentiment extraction. Most studies have focused on SA techniques for widely used languages such as English, but less attention has been paid to Arabic, particularly the Saudi dialect. Most Arabic SA studies have built systems using supervised approaches that are domain dependent; hence, they achieve low performance when applied to a new domain different from the learning domain, and they require manually labelled training data, which are usually difficult to obtain. In this article, we propose a novel lexicon-based algorithm for Saudi dialect SA that features domain independence. We created an annotated Saudi dialect dataset and built a large-scale lexicon for the Saudi dialect. Then, we developed our weighted lexicon-based algorithm. The proposed algorithm mines the associations between polarity and non-polarity words for the dataset and then weights these words based on their associations. During algorithm development, we also proposed novel rules for handling some linguistic features such as negation and supplication. Several experiments were performed to evaluate the performance of the proposed algorithm.

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Improving the Domain Independence of Data Provenance Ontologies

Journal of Database Management ◽

10.4018/jdm.2017010104 ◽

2017 ◽

Vol 28 (1) ◽

pp. 43-62

Author(s):

Jun Liu ◽

Sudha Ram

Keyword(s):

Data Provenance ◽

Shared Data ◽

Novel Approach ◽

Conceptual Graph ◽

Domain Independence ◽

Domain Ontologies ◽

Using Data

Provenance is becoming increasingly important as more and more people are using data that they themselves did not generate. In the last decade, significant efforts have been directed toward developing generic, shared data provenance ontologies that support the interoperability of provenance across systems. An issue that is impeding the use of such provenance ontologies is that a generic provenance ontology, no matter how complete it is, is insufficient for capturing the diverse, complex provenance requirements in different domains. In this paper, the authors propose a novel approach to adapting and extending the W7 model, a well-known generic ontology of data provenance. Relying on various knowledge expansion mechanisms provided by the Conceptual Graph formalism, the authors' approach enables us to develop domain ontologies of provenance in a disciplined yet flexible way.

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