Learning to Answer Questions by Understanding Using Entity-Based Memory Network
Computacion y Sistemas, ISSN: 2007-9737, Vol: 21, Issue: 4, Page: 799-808
2017
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
This paper introduces a novel neural network model for question answering, the entity-based memory network. It enhances neural networks’ ability of representing and calculating information over a long period by keeping records of entities contained in text. The core component is a memory pool which comprises entities’ states. These entities’ states are continuously updated according to the input text. Questions with regard to the input text are used to search the memory pool for related entities and answers are further predicted based on the states of retrieved entities. Entities in this model are regard as the basic units that carry information and construct text. Information carried by text are encoded in the states of entities. Hence text can be best understood by analysing its containing entities. Compared with previous memory network models, the proposed model is capable of handling fine-grained information and more sophisticated relations based on entities. We formulated several different tasks as question answering problems and tested the proposed model. Experiments reported satisfying results.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85040376648&origin=inward; http://dx.doi.org/10.13053/cys-21-4-2845; http://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2845; http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-55462017000400799&lng=en&tlng=en; http://www.scielo.org.mx/scielo.php?script=sci_abstract&pid=S1405-55462017000400799&lng=en&tlng=en; http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-55462017000400799; http://www.scielo.org.mx/scielo.php?script=sci_abstract&pid=S1405-55462017000400799; https://dx.doi.org/10.13053/cys-21-4-2845; https://www.cys.cic.ipn.mx/ojs/index.php/CyS/article/view/2845
Instituto Politecnico Nacional/Centro de Investigacion en Computacion
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