TY - GEN
T1 - Semantic search in offshore engineering with linguistics and neural processing Pipelines
AU - Gonçalves, Flavio Jaime Pol
AU - de Oliveira Carmo, Vinicius Cleves
AU - de Melo, Vinicius Toquetti
AU - da Silva Cunha, Rodrigo
AU - Santos, Ismael H.F.
AU - Barreira, Rodrigo Augusto
AU - Cugnasca, Carlos Eduardo
AU - Cozman, Fabio Gagliardi
AU - Gomi, Edson Satoshi
N1 - Publisher Copyright:
© 2021 by ASME
PY - 2021
Y1 - 2021
N2 - This paper presents a computing pipeline architecture for semantic search in the domain of Offshore Engineering. The proposed system combines modules such as document retriever, passage retriever, and answer extractor to produce textual responses to queries in natural language such as: “What FPSO motion is mostly affected by viscous damping?” Such responses are often needed in Offshore Engineering activities, and linguistic techniques such as those based on inverted indexes with a syntactic focus tend to perform poorly. Instead, this research explores semantic techniques that take into account the meaning of words in the domain of Offshore Engineering. This paper describes a Linguistic QA pipeline architecture built that provides a way to retrieve answers instantly from a collection of 13,000 unstructured technical documents about Offshore Engineering, reports the achieved results and future work. This paper also presents additional modules under construction that exploit Neural Networks and ontologies approaches for semantic search in the domain of Offshore Engineering.
AB - This paper presents a computing pipeline architecture for semantic search in the domain of Offshore Engineering. The proposed system combines modules such as document retriever, passage retriever, and answer extractor to produce textual responses to queries in natural language such as: “What FPSO motion is mostly affected by viscous damping?” Such responses are often needed in Offshore Engineering activities, and linguistic techniques such as those based on inverted indexes with a syntactic focus tend to perform poorly. Instead, this research explores semantic techniques that take into account the meaning of words in the domain of Offshore Engineering. This paper describes a Linguistic QA pipeline architecture built that provides a way to retrieve answers instantly from a collection of 13,000 unstructured technical documents about Offshore Engineering, reports the achieved results and future work. This paper also presents additional modules under construction that exploit Neural Networks and ontologies approaches for semantic search in the domain of Offshore Engineering.
UR - http://www.scopus.com/inward/record.url?scp=85117111638&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/5197e2ee-2992-311b-8af7-b3579f8cef44/
U2 - 10.1115/OMAE2021-62979
DO - 10.1115/OMAE2021-62979
M3 - Contribución a la conferencia
AN - SCOPUS:85117111638
SN - 9780791885116
T3 - Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
BT - Proceedings of the International Conference on Offshore Mechanics and Arctic Engineering - OMAE
PB - American Society of Mechanical Engineers (ASME)
Y2 - 21 June 2021 through 30 June 2021
ER -