  As a fundamental task in natural language processing, word embedding converts
each word into a representation in a vector space. A challenge with word
embedding is that as the vocabulary grows, the vector space's dimension
increases and it can lead to a vast model size. Storing and processing word
vectors are resource-demanding, especially for mobile edge-devices
applications. This paper explores word embedding dimension reduction. To
balance computational costs and performance, we propose an efficient and
effective weakly-supervised feature selection method, named WordFS. It has two
variants, each utilizing novel criteria for feature selection. Experiments
conducted on various tasks (e.g., word and sentence similarity and binary and
multi-class classification) indicate that the proposed WordFS model outperforms
other dimension reduction methods at lower computational costs.
