New types of computational perceptions: Linguistic descriptions in deforestation analysis

Conde-Clemente, Patricia; Alonso, José M.; Nunes, Éldman de Oliveira; Sánchez, Ángel Mediavilla; Triviño, Gracián

doi:10.1016/j.eswa.2017.05.031

Cited by 16 publications

(7 citation statements)

References 40 publications

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“…These conversational maxims were studied by Dale and Reiter in the context of NLG systems [7]. Moreover, they were also considered in previous publications (e.g., [6]) for evaluating the quality of NLG systems. It is worth noting that, in the context of NLG, human evaluation is considered as the most suitable evaluation method [45], since it is able to capture adequacy, correctness, and fluidity of the texts, among other relevant features.…”

Section: Automatic Linguistic Reporting Evaluation and Resultsmentioning

confidence: 99%

Automatic linguistic reporting of customer activity patterns in open malls

Ocaña

Chapela-Campa

Álvarez

et al. 2021

Multimed Tools Appl

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In this work, we present a complete system to produce an automatic linguistic reporting about the customer activity patterns inside open malls, a mixed distribution of classical malls joined with the shops on the street. These reports can assist to design marketing campaigns by means of identifying the best places to catch the attention of customers. Activity patterns are estimated with process mining techniques and the key information of localization. Localization is obtained with a parallelized solution based on WiFi fingerprint system to speed up the solution. In agreement with the best practices for human evaluation of natural language generation systems, the linguistic quality of the generated report was evaluated by 41 experts who filled in an online questionnaire. Results are encouraging, since the average global score of the linguistic quality dimension is 6.17 (0.76 of standard deviation) in a 7-point Likert scale. This expresses a high degree of satisfaction of the generated reports and validates the adequacy of automatic natural language textual reports as a complementary tool to process model visualization.

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Section: Automatic Linguistic Reporting Evaluation and Resultsmentioning

confidence: 99%

Automatic linguistic reporting of customer activity patterns in open malls

Ocaña

Chapela-Campa

Álvarez

et al. 2021

Multimed Tools Appl

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“…Linguistic database summarization methods rely on the concept of protoforms and fuzzy logic [6,44] to summarize data. Linguistic summarization of time-series data has been used for various applications, such as elderly care [40], physical activity tracking [33], driving simulation environments [13], deforestation analysis [11], human gait study [1], periodicity detection [28], time-series forecasting [22], and generation of longer temporal "narratives" from neonatal intensive care data via the use of a neonatal ontology [14]. Other work includes the use of genetic algorithms [7] to generate linguistic summaries from time-series, and those that place emphasis…”

Section: Related Workmentioning

confidence: 99%

A Framework for Generating Explanations from Temporal Personal Health Data

Harris,

Chen,

Zaki

2020

Preprint

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Whereas it has become easier for individuals to track their personal health data (e.g., heart rate, step count, food log), there is still a wide chasm between the collection of data and the generation of meaningful explanations to help users better understand what their data means to them. With an increased comprehension of their data, users will be able to act upon the newfound information and work towards striving closer to their health goals. We aim to bridge the gap between data collection and explanation generation by mining the data for interesting behavioral findings that may provide hints about a user's tendencies. Our focus is on improving the explainability of temporal personal health data via a set of informative summary templates, or "protoforms. " These protoforms span both evaluation-based summaries that help users evaluate their health goals and pattern-based summaries that explain their implicit behaviors. In addition to individual users, the protoforms we use are also designed for population-level summaries. We apply our approach to generate summaries (both univariate and multivariate) from real user data and show that our system can generate interesting and useful explanations.

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“…Existing types of computational perception have been proposed by Eciolaza, et al [5] and Conde-Clemente, et al [6]. They distinguished the following CP models:…”

Section: Existing and New Types Of Computational Perception (Cp)mentioning

confidence: 99%

“…Second, the first element in the list of elements is identified as the highest emotional experience level, followed by the second, the third, and so on. 4 6. T se is the linguistic template output: "The results show that the emotional experience level was" {the greatest Maximum} "in" {year}; {the second greatest was Maximum} "in" {year}; {the third greatest was Maximum} "in" {year}".…”

Section: Glmp For Analyzing Level Of Emotionmentioning

confidence: 99%

Using Customer Emotional Experience from E-Commerce for Generating Natural Language Evaluation and Advice Reports on Game Products

Gani

Tomimatsu

2019

J. ICT Res. Appl.

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Investigating customer emotional experience using natural language processing (NLP) is an example of a way to obtain product insight. However, it relies on interpreting and representing the results understandably. Currently, the results of NLP are presented in numerical or graphical form, and human experts still need to provide an explanation in natural language. It is desirable to develop a computational system that can automatically transform NLP results into a descriptive report in natural language. The goal of this study was to develop a computational linguistic description method to generate evaluation and advice reports on game products. This study used NLP to extract emotional experiences (emotions and sentiments) from e-commerce customer reviews in the form of numerical information. This paper also presents a linguistic description method to generate evaluation and advice reports, adopting the Granular Linguistic Model of a Phenomenon (GLMP) method for analyzing the results of the NLP method. The test result showed that the proposed method could successfully generate evaluation and advice reports assessing the quality of 5 game products based on the emotional experience of customers.

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New types of computational perceptions: Linguistic descriptions in deforestation analysis

Cited by 16 publications

References 40 publications

Automatic linguistic reporting of customer activity patterns in open malls

Automatic linguistic reporting of customer activity patterns in open malls

A Framework for Generating Explanations from Temporal Personal Health Data

Using Customer Emotional Experience from E-Commerce for Generating Natural Language Evaluation and Advice Reports on Game Products

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