A common semantic space for monolingual and cross-lingual meta-embeddings
Fecha
2019-10-29Autor
García Ferrero, Iker
Metadatos
Mostrar el registro completo del ítemResumen
This master’s thesis presents a new technique for creating monolingual and cross-lingual meta-embeddings. Our method integrates multiple word embeddings created from complementary techniques, textual sources, knowledge bases and languages. Existing word vectors are projected to a common semantic space using linear transformations and averaging. With our method the resulting meta-embeddings maintain the dimensionality of the original embeddings without losing information while dealing with the out-of-vocabulary (OOV) problem. Furthermore, empirical evaluation demonstrates the effectiveness of our technique with respect to previous work on various intrinsic and extrinsic multilingual evaluations.