TY - GEN
T1 - Right-truncatable neural word embeddings
AU - Suzuki, Jun
AU - Nagata, Masaaki
PY - 2016/1/1
Y1 - 2016/1/1
N2 - This paper proposes an incremental learning strategy for neural word embedding methods, such as SkipGrams and Global Vectors. Since our method iteratively generates embedding vectors one dimension at a time, obtained vectors equip a unique property. Namely, any right-truncated vector matches the solution of the corresponding lower-dimensional embedding. Therefore, a single embedding vector can manage a wide range of dimensional requirements imposed by many different uses and applications.
AB - This paper proposes an incremental learning strategy for neural word embedding methods, such as SkipGrams and Global Vectors. Since our method iteratively generates embedding vectors one dimension at a time, obtained vectors equip a unique property. Namely, any right-truncated vector matches the solution of the corresponding lower-dimensional embedding. Therefore, a single embedding vector can manage a wide range of dimensional requirements imposed by many different uses and applications.
UR - http://www.scopus.com/inward/record.url?scp=84994193602&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84994193602&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84994193602
T3 - 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016 - Proceedings of the Conference
SP - 1145
EP - 1151
BT - 2016 Conference of the North American Chapter of the Association for Computational Linguistics
PB - Association for Computational Linguistics (ACL)
T2 - 15th Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2016
Y2 - 12 June 2016 through 17 June 2016
ER -