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Can We Survive without Labelled Data in NLP? Transfer Learning for Open Information Extraction

Autor: Sarhan, I., Spruit, M., Esposito, M., Massala, G., Minutolo, A., Pota, M., Sub Natural Language Processing, Natural Language Processing

Publikováno v: Applied Sciences, 10(17). MDPI
Applied Sciences
Volume 10
Issue 17
Applied Sciences, Vol 10, Iss 5758, p 5758 (2020)

Various tasks in natural language processing (NLP) suffer from lack of labelled training data, which deep neural networks are hungry for. In this paper, we relied upon features learned to generate relation triples from the open information extraction

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59f65ee956753b0dbf8bad965b538775
https://dspace.library.uu.nl/handle/1874/410075

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Convolutional Neural Networks for Question Classification in Italian language

Autor: Pota M., Esposito M., De Pietro G.

Publikováno v: The 16th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SOMET_17), pp. 604–615, 26-28 September, 2017
info:cnr-pdr/source/autori:Pota M.; Esposito M.; De Pietro G./congresso_nome:The 16th International Conference on Intelligent Software Methodologies, Tools, and Techniques (SOMET_17)/congresso_luogo:/congresso_data:26-28 September, 2017/anno:2017/pagina_da:604/pagina_a:615/intervallo_pagine:604–615

Question Classification (QC) is a very important module, to include into the pipeline usually employed to implement the Question Answering paradigm. Recently, good results have been achieved on the QC task by using Convolutional Neural Networks (CNNs

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=cnr_________::90b9b2b8a605e1a61ac392d479296c29
http://www.cnr.it/prodotto/i/377776

Network of three catalytic reactors with periodical feed switching for methanol synthesis: Bifurcation analysis

Autor: Pota M., Russo L., Mancusi E., CRESCITELLI, SILVESTRO

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______3730::54e16a5733b56e40e3f4f3589eb50752
http://hdl.handle.net/11588/313510