TY - GEN
T1 - Analogies Between Short Sentences
T2 - 9th Language and Technology Conference, LTC 2019
AU - Lepage, Yves
N1 - Funding Information:
Work supported by JSPS Grant 18K11447 (Kakenhi C) “Self-explainable and fast-to-train example-based machine translation using neural networks”.
Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - The present article proposes a method to solve analogies between sentences by combining existing techniques to solve formal analogies between strings and semantic analogies between words. The method is applied on sentences from the Tatoeba corpus. Two datasets of more than five thousand semantico-formal analogies, in English and French, are released.
AB - The present article proposes a method to solve analogies between sentences by combining existing techniques to solve formal analogies between strings and semantic analogies between words. The method is applied on sentences from the Tatoeba corpus. Two datasets of more than five thousand semantico-formal analogies, in English and French, are released.
UR - http://www.scopus.com/inward/record.url?scp=85132910202&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85132910202&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-05328-3_11
DO - 10.1007/978-3-031-05328-3_11
M3 - Conference contribution
AN - SCOPUS:85132910202
SN - 9783031053276
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 163
EP - 179
BT - Human Language Technology. Challenges for Computer Science and Linguistics - 9th Language and Technology Conference, LTC 2019, Revised Selected Papers
A2 - Vetulani, Zygmunt
A2 - Paroubek, Patrick
A2 - Kubis, Marek
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 17 May 2019 through 19 May 2019
ER -