Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Estevam R Hruschka"'
Publikováno v:
KDD
User-Generated text is a rich source of user insights and experiences that can be very helpful in many different daily life situations, such as when deciding what product to buy, what hotel to stay, what company to apply for a job, what region to buy
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5f3d377b6135c1877c251b3eb66f107
https://doi.org/10.1145/3447548.3469477
https://doi.org/10.1145/3447548.3469477
Autor:
Pedro Luiz Lima Bertarini, Laurence Rodrigues do Amaral, Raphael de Lima Mendes, Estevam R. Hruschka, Matheus de Souza Gomes, Alexandre Henrick da Silva Alves
Publikováno v:
CEC
The exponentially growing of generated data and the advanced computing techniques, catalyzed the necessity of innovative strategies to store, analyze and capture biological data. The biocuration was created due to the number and scope of scientific d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::82f3f716ef07c2e9c898ed2341ba106b
https://doi.org/10.1109/cec45853.2021.9504981
https://doi.org/10.1109/cec45853.2021.9504981
Publikováno v:
WWW (Companion Volume)
Despite rapid developments in the field of machine learning research, collecting high-quality labels for supervised learning remains a bottleneck for many applications. This difficulty is exacerbated by the fact that state-of-the-art models for NLP t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df67a492b7d09f4f3506d0d89b4428e1
http://arxiv.org/abs/2106.12767
http://arxiv.org/abs/2106.12767
Autor:
Estevam R. Hruschka
Publikováno v:
KDD
This tutorial focuses on how to build Question Answering (QA) syetems based on the Never-Ending Learning (NEL) approach. NEL systems can be roughly described as computer systems that learn over time to become better in solving a specific task. Differ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cb5b90b12aaee6b13b1c721ea0752ec9
https://doi.org/10.1145/3394486.3406486
https://doi.org/10.1145/3394486.3406486
Autor:
Lucas Rezende Bruno, Igor Henrique P. Rodrigues, Edimilson Batista dos Santos, Estevam R. Hruschka
Publikováno v:
BRACIS
The first never-ending learning system reported in literature is named NELL (Never-Ending Language Learning). The main goal of the NELL system is to learn to read the web better each day, and to store the gathered knowledge in a never-ending growing
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dc9b95dd9183367192e755479b3816cb
https://doi.org/10.1109/bracis.2019.00125
https://doi.org/10.1109/bracis.2019.00125
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030298586
HAIS
HAIS
Internet and social Web made possible the acquisition of information to feed a growing number of Machine Learning (ML) applications and, in addition, brought light to the use of crowdsourcing approaches, commonly applied to problems that are easy for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6af789c49857ff00d6c09aee893bedeb
https://doi.org/10.1007/978-3-030-29859-3_59
https://doi.org/10.1007/978-3-030-29859-3_59
Publikováno v:
Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018).
With advances in machine learning, natural language processing, processing speed, and amount of data storage, conversational agents are being used in applications that were not possible to perform within a few years. NELL, a machine learning agent wh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::31da3944185c713a78368bfa96d89c18
https://doi.org/10.5753/eniac.2018.4475
https://doi.org/10.5753/eniac.2018.4475
Automatic Identification of Equivalence of Concepts in Different Languages for Never-Ending Learning
Publikováno v:
Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018).
This paper describes the process of automatic identification of concepts in different languages using a base that relies on simple semantic and morphosyntactic characteristics like string similarity, difference in words amount and translation positio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f628e986a0e965f154081e3161d47a40
https://doi.org/10.5753/eniac.2018.4412
https://doi.org/10.5753/eniac.2018.4412
Autor:
William W. Cohen, J. Welling, Kathryn Mazaitis, Ndapandula Nakashole, Bryan Kisiel, Andrew Carlson, T. Mohamed, M. Greaves, Bhavana Dalvi, Tom M. Mitchell, Mehdi Samadi, Abulhair Saparov, Justin Betteridge, Jayant Krishnamurthy, Estevam R. Hruschka, Partha Pratim Talukdar, Ni Lao, Matt Gardner, Abhinav Gupta, Xinlei Chen, Bishan Yang, Emmanouil Antonios Platanios, Alan Ritter, Richard Wang, Derry Tanti Wijaya, Burr Settles
Publikováno v:
IndraStra Global.
Whereas people learn many different types of knowledge from diverse experiences over many years, and become better learners over time, most current machine learning systems are much more narrow, learning just a single function or data model based on
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5ab7fb1f0cf56fcd9e096c98ddb1b755
https://igi.indrastra.com/items/show/188726
https://igi.indrastra.com/items/show/188726
Publikováno v:
BRACIS
Large Knowledge Bases construction has been a relevant topic in the last years. Most techniques focus on discovering facts or relations like presidentOf(Bush, United_States). Many of them are associated with a specific temporal scope, thus, they are
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e7a9a96fde322e5f6d49cc571490e0e5
https://doi.org/10.1109/bracis.2017.56
https://doi.org/10.1109/bracis.2017.56