17,687 research outputs found
Induced hesitant 2-tuple linguistic aggregation operators with application in group decision making
In this article, hesitant 2-tuple linguistic arguments are used to evaluate the group decision making problems which have inter dependent or inter active attributes. Operational laws are developed for hesitant 2-tuple linguistic elements and based on these operational laws hesitant 2- tuple weighted averaging operator and generalized hesitant 2- tuple averaging operator are proposed. Combining Choquet integral with hesitant 2-tuple linguistic information, some new aggregation operators are defined, including the hesitant 2-tuple correlated averaging operator, the hesitant 2-tuple correlated geometric operator and the generalized hesitant 2-tuple correlated averaging operator. These proposed operators successfully manage the correlations among the elements. After investigating the properties of these operators, a multiple attribute decision making method based on these operators, is suggested. Finally, an example is given to illustrate the practicality and feasibility of proposed method
Method for aggregating correlated interval grey linguistic variables and its application to decision making
With respect to multiple attribute decision making (MADM) problems in which attribute values take the form of interval grey linguistic variables, a new decision making analysis method is developed. In this paper, we propose the interval grey linguistic variables ordered weighted aggregation (IGLOWA) operator, and then use the Choquet integral to develop the interval grey linguistic correlated ordered arithmetic aggregation (IGLCOA) operator and the interval grey linguistic correlated ordered geometric aggregation (IGLCOGA) operator. Those operators not only consider the importance of the elements, but also can reflect the correlations among the elements. Then, we develop an approach to multiple attribute decision making problems with correlative weights which attribute values are given in terms of interval grey linguistic variables information based on those operators. Finally an illustrative example is given to use the method in the range of uncertain multiple attribute decision making. The results show that the method proposed in this paper is feasible.
First published online: 15 Mar 201
Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package
This paper describes the R package crqa to perform cross-recurrence
quantification analysis of two time series of either a categorical or
continuous nature. Streams of behavioral information, from eye movements to
linguistic elements, unfold over time. When two people interact, such as in
conversation, they often adapt to each other, leading these behavioral levels
to exhibit recurrent states. In dialogue, for example, interlocutors adapt to
each other by exchanging interactive cues: smiles, nods, gestures, choice of
words, and so on. In order for us to capture closely the goings-on of dynamic
interaction, and uncover the extent of coupling between two individuals, we
need to quantify how much recurrence is taking place at these levels. Methods
available in crqa would allow researchers in cognitive science to pose such
questions as how much are two people recurrent at some level of analysis, what
is the characteristic lag time for one person to maximally match another, or
whether one person is leading another. First, we set the theoretical ground to
understand the difference between 'correlation' and 'co-visitation' when
comparing two time series, using an aggregative or cross-recurrence approach.
Then, we describe more formally the principles of cross-recurrence, and show
with the current package how to carry out analyses applying them. We end the
paper by comparing computational efficiency, and results' consistency, of crqa
R package, with the benchmark MATLAB toolbox crptoolbox. We show perfect
comparability between the two libraries on both levels
Joint perceptual decision-making: a case study in explanatory pluralism.
Traditionally different approaches to the study of cognition have been viewed as competing explanatory frameworks. An alternative view, explanatory pluralism, regards different approaches to the study of cognition as complementary ways of studying the same phenomenon, at specific temporal and spatial scales, using appropriate methodological tools. Explanatory pluralism has been often described abstractly, but has rarely been applied to concrete cases. We present a case study of explanatory pluralism. We discuss three separate ways of studying the same phenomenon: a perceptual decision-making task (Bahrami et al., 2010), where pairs of subjects share information to jointly individuate an oddball stimulus among a set of distractors. Each approach analyzed the same corpus but targeted different units of analysis at different levels of description: decision-making at the behavioral level, confidence sharing at the linguistic level, and acoustic energy at the physical level. We discuss the utility of explanatory pluralism for describing this complex, multiscale phenomenon, show ways in which this case study sheds new light on the concept of pluralism, and highlight good practices to critically assess and complement approaches
Population size predicts lexical diversity, but so does the mean sea level - why it is important to correctly account for the structure of temporal data
In order to demonstrate why it is important to correctly account for the
(serial dependent) structure of temporal data, we document an apparently
spectacular relationship between population size and lexical diversity: for
five out of seven investigated languages, there is a strong relationship
between population size and lexical diversity of the primary language in this
country. We show that this relationship is the result of a misspecified model
that does not consider the temporal aspect of the data by presenting a similar
but nonsensical relationship between the global annual mean sea level and
lexical diversity. Given the fact that in the recent past, several studies were
published that present surprising links between different economic, cultural,
political and (socio-)demographical variables on the one hand and cultural or
linguistic characteristics on the other hand, but seem to suffer from exactly
this problem, we explain the cause of the misspecification and show that it has
profound consequences. We demonstrate how simple transformation of the time
series can often solve problems of this type and argue that the evaluation of
the plausibility of a relationship is important in this context. We hope that
our paper will help both researchers and reviewers to understand why it is
important to use special models for the analysis of data with a natural
temporal ordering
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
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