| 361 |
Salience in the Generation of Multimodal Referring Acts
|
|
|
|
In: http://mcs.open.ac.uk/pp2464/inpress/Piwek-ICMI2009.pdf
|
|
BASE
|
|
Show details
|
|
|
| 362 |
Generating a Sentence from a Thought
|
|
|
|
In: http://world-comp.org/p2011/ICA3771.pdf
|
|
BASE
|
|
Show details
|
|
|
| 363 |
Fully Generated Scripted Dialogue for Embodied Agents
|
|
|
|
In: http://www.csd.abdn.ac.uk/~kvdeemte/NECA-final.pdf
|
|
BASE
|
|
Show details
|
|
|
| 364 |
Language Engineering System for Automatic Conversion of English Cyber Data into Urdu Websites
|
|
|
|
In: http://www.cs.bham.ac.uk/~isb855/papers/ICCA06_.pdf
|
|
BASE
|
|
Show details
|
|
|
| 365 |
doi: 0.5087/dad.2012.205 G-Asks: An Intelligent Automatic Question Generation System for Academic Writing Support
|
|
|
|
In: http://elanguage.net/journals/dad/article/viewFile/1463/2827/
|
|
BASE
|
|
Show details
|
|
|
| 367 |
Text-to-text Surface Realisation using Dependency-tree Replacement
|
|
|
|
In: http://each.uspnet.usp.br/ivandre/papers/text2TextSurfaceReal(10p).pdf
|
|
BASE
|
|
Show details
|
|
|
| 368 |
An Annotated Corpus of Film Dialogue for Learning and Characterizing Character Style
|
|
|
|
In: http://games.soe.ucsc.edu/sites/default/files/1114_Paper.pdf
|
|
BASE
|
|
Show details
|
|
|
| 369 |
Experience
|
|
|
|
In: http://www.desilinguist.org/pdf/madnani-cv.pdf
|
|
BASE
|
|
Show details
|
|
|
| 370 |
Perceived or Not Perceived: Film Character Models for Expressive NLG
|
|
|
|
In: http://users.soe.ucsc.edu/~maw/papers/icids-v12.pdf
|
|
BASE
|
|
Show details
|
|
|
| 371 |
Solicited review(s): Name Surname, University, Country Open review(s): Name Surname, University, Country
|
|
|
|
In: http://www.semantic-web-journal.net/system/files/swj450.pdf
|
|
BASE
|
|
Show details
|
|
|
| 372 |
IOS Press Natural Language Generation in the Context of the Semantic Web
|
|
|
|
In: http://www.semantic-web-journal.net/system/files/swj511_0.pdf
|
|
BASE
|
|
Show details
|
|
|
| 373 |
Feeding memes: a verbal communication challenge
|
|
|
|
In: http://mobileguide06.di.unito.it/pdf/Simi%26al.pdf
|
|
BASE
|
|
Show details
|
|
|
| 374 |
On Moving on on Ontologies: Mass, Count and Long Thin Things
|
|
|
|
In: http://acl.ldc.upenn.edu/W/W94/W94-0309.pdf
|
|
BASE
|
|
Show details
|
|
|
| 375 |
Exploring neural paraphrasing to improve fluency of rule-based generation
|
|
|
|
BASE
|
|
Show details
|
|
|
| 376 |
Spanish morphological generation with wide-coverage lexicons and decision trees
|
|
|
|
BASE
|
|
Show details
|
|
|
| 377 |
An adaptable lexical simplification architecture for major ibero-romance languages
|
|
|
|
BASE
|
|
Show details
|
|
|
| 379 |
Automatic generation of textual descriptions in data-to-text systems using a fuzzy temporal ontology: Application in air quality index data series
|
|
|
|
Abstract:
In this paper we present a model based on computational intelligence and natural language generation for the automatic generation of textual summaries from numerical data series, aiming to provide insights which help users to understand the relevant information hidden in the data. Our model includes a fuzzy temporal ontology with temporal references which addresses the problem of managing imprecise temporal knowledge, which is relevant in data series. We fully describe a real use case of application in the environmental information systems field, providing linguistic descriptions about the air quality index (AQI), which is a very well-known indicator provided by all meteorological agencies worldwide. We consider two different data sources of real AQI data provided by the official Galician (NW Spain) Meteorology Agency: (i) AQI distribution in the stations of the meteorological observation network and (ii) time series which describe the state and evolution of the AQI in each meteorological station. Both application models were evaluated following the current standards and good practices of manual human expert evaluation of the Natural Language Generation field. Assessment results by two experts meteorologists were very satisfactory, which empirically confirm that the proposed textual descriptions fit this type of data and service both in content and layout ; This research was funded by the Spanish Ministry for Science, Innovation and Universities (grants TIN2017-84796-C2-1-R, PID2020-112623GB-I00, and PDC2021-121072-C21) and the Galician Ministry of Education, University and Professional Training, Spain (grants ED431C2018/29 and ED431G2019/04). All grants were co-funded by the European Regional Development Fund (ERDF/FEDER program) ; SI
|
|
Keyword:
Data to text systems; Fuzzy linguistic terms; Linguistic descriptions of data; Natural language generation
|
|
URL: https://doi.org/10.1016/j.asoc.2022.108612
http://hdl.handle.net/10347/27633
|
|
BASE
|
|
Hide details
|
|
|
|
|
