Abstract
Learning vector representation for words is an important research field which
may benefit many natural language processing tasks. Two limitations exist in
nearly all available models, which are the bias caused by the context
definition and the lack of knowledge utilization. They are difficult to tackle
because these algorithms are essentially unsupervised learning approaches.
Inspired by deep learning, the authors propose a supervised framework for
learning vector representation of words to provide additional supervised fine
tuning after unsupervised learning. The framework is knowledge rich approacher
and compatible with any numerical vectors word representation. The authors
perform both intrinsic evaluation like attributional and relational similarity
prediction and extrinsic evaluations like the sentence completion and sentiment
analysis. Experiments results on 6 embeddings and 4 tasks with 10 datasets show
that the proposed fine tuning framework may significantly improve the quality
of the vector representation of words.
Users
Please
log in to take part in the discussion (add own reviews or comments).