Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Alfio Gliozzo"'
Publikováno v:
Information, Vol 12, Iss 8, p 316 (2021)
In this paper, we propose a fully automated system to extend knowledge graphs using external information from web-scale corpora. The designed system leverages a deep-learning-based technology for relation extraction that can be trained by a distantly
Externí odkaz:
https://doaj.org/article/7bdd82f8c8b746c1a3965e3af319567e
Autor:
Shirish Shevade, Saswati Dana, Dinesh Garg, Michael R. Glass, Dinesh Khandelwal, G. P. Shrivatsa Bhargav, L. Venkata Subramaniam, Alfio Gliozzo
Publikováno v:
AAAI
Research on the task of Reading Comprehension style Question Answering (RCQA) has gained momentum in recent years due to the emergence of human annotated datasets and associated leaderboards, for example CoQA, HotpotQA, SQuAD, TriviaQA, etc. While st
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f6e817ac585cc7d175605ce83ad5468
https://doi.org/10.1609/aaai.v34i05.6272
https://doi.org/10.1609/aaai.v34i05.6272
Publikováno v:
Findings of the Association for Computational Linguistics: NAACL 2022.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c25eba07b5dd506d5b805e82d070f0b1
https://doi.org/10.18653/v1/2022.findings-naacl.59
https://doi.org/10.18653/v1/2022.findings-naacl.59
Autor:
Nandana Mihindukulasooriya, Mike Sava, Gaetano Rossiello, Md. Faisal Mahbub Chowdhury, Irene Yachbes, Aditya Gidh, Jillian Duckwitz, Kovit Nisar, Michael Santos, Alfio Gliozzo
Publikováno v:
The Semantic Web – ISWC 2022 ISBN: 9783031194320
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21eec7f003edb138501b794417330ab5
https://doi.org/10.1007/978-3-031-19433-7_47
https://doi.org/10.1007/978-3-031-19433-7_47
Autor:
Michael R. Glass, Vishwajeet Kumar, Feifei Pan, Alfio Gliozzo, Saneem A. Chemmengath, Rishav Chakravarti, Nicolas Rodolfo Fauceglia, Samarth Bharadwaj, Avi Sil, Mustafa Canim
Publikováno v:
NAACL-HLT
Transformer based architectures are recently used for the task of answering questions over tables. In order to improve the accuracy on this task, specialized pre-training techniques have been developed and applied on millions of open-domain web table
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c8348b4fc76b281009d9d6af851f9fc
http://arxiv.org/abs/2104.08303
http://arxiv.org/abs/2104.08303
Autor:
Nandana Mihindukulasooriya, Michael Glass, Yu Deng, Faisal Mahbub Chowdhury, Alfio Gliozzo, Nicolas Rodolfo Fauceglia, Ruchi Mahindru
Publikováno v:
Proceedings of the First Workshop on Interactive Learning for Natural Language Processing.
Dynamic faceted search (DFS), an interactive query refinement technique, is a form of Human–computer information retrieval (HCIR) approach. It allows users to narrow down search results through facets, where the facets-documents mapping is determin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f42b19f02d94daa01daa9fed7eb61737
https://doi.org/10.18653/v1/2021.internlp-1.5
https://doi.org/10.18653/v1/2021.internlp-1.5
Autor:
Nandana Mihindukulasooriya, Gaetano Rossiello, Tahira Naseem, Pavan Kapanipathi, Ibrahim Abdelaziz, Mihaela A. Bornea, Alfio Gliozzo
Publikováno v:
The Semantic Web – ISWC 2021 ISBN: 9783030883607
ISWC
ISWC
Relation linking is essential to enable question answering over knowledge bases. Although there are various efforts to improve relation linking performance, the current state-of-the-art methods do not achieve optimal results, therefore, negatively im
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ecb842d462c6c74d36ecd09e0029fea6
https://doi.org/10.1007/978-3-030-88361-4_19
https://doi.org/10.1007/978-3-030-88361-4_19
Autor:
Ibrahim Abdelaziz, Nandana Mihindukulasooriya, Tahira Naseem, Alfio Gliozzo, Pavan Kapanipathi, Young-Suk Lee, Alexander G. Gray, Salim Roukos, Srinivas Ravishankar
Publikováno v:
ACL/IJCNLP (2)
Relation linking is a crucial component of Knowledge Base Question Answering systems. Existing systems use a wide variety of heuristics, or ensembles of multiple systems, heavily relying on the surface question text. However, the explicit semantic pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f7f7b6f70304c8ee67a9d43bc4201961
https://doi.org/10.18653/v1/2021.acl-short.34
https://doi.org/10.18653/v1/2021.acl-short.34
Autor:
Sumit Neelam, Srinivas Ravishankar, Young-Suk Lee, Revanth Gangi Reddy, Salim Roukos, Mo Yu, Francois P. S. Luus, G. P. Shrivatsa Bhargav, Achille Fokoue, Dinesh Garg, Udit Sharma, Lucian Popa, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Alfio Gliozzo, Hima P. Karanam, Alexander G. Gray, Maria Chang, Cristina Cornelio, Dinesh Khandelwal, Tahira Naseem, Naweed Khan, Sairam Gurajada, Pavan Kapanipathi, Yunyao Li, Saswati Dana, Ramón Fernandez Astudillo, Ryan Riegel, Ndivhuwo Makondo, Gaetano Rossiello
Publikováno v:
ACL/IJCNLP (Findings)
Knowledge base question answering (KBQA)is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understanding, necessity for reasoning, and lack of large end-to-end training data
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f6b6776e2a881011df04cf026394b876
https://doi.org/10.18653/v1/2021.findings-acl.339
https://doi.org/10.18653/v1/2021.findings-acl.339
Publikováno v:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations.
We present the first end-to-end, transformer-based table question answering (QA) system that takes natural language questions and massive table corpora as inputs to retrieve the most relevant tables and locate the correct table cells to answer the qu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::baa60ea3306b92763bf8c29376136a2d
https://doi.org/10.18653/v1/2021.acl-demo.24
https://doi.org/10.18653/v1/2021.acl-demo.24