  Two task-specific dependency-based word embedding methods are proposed for
text classification in this work. In contrast with universal word embedding
methods that work for generic tasks, we design task-specific word embedding
methods to offer better performance in a specific task. Our methods follow the
PPMI matrix factorization framework and derive word contexts from the
dependency parse tree. The first one, called the dependency-based word
embedding (DWE), chooses keywords and neighbor words of a target word in the
dependency parse tree as contexts to build the word-context matrix. The second
method, named class-enhanced dependency-based word embedding (CEDWE), learns
from word-context as well as word-class co-occurrence statistics. DWE and CEDWE
are evaluated on popular text classification datasets to demonstrate their
effectiveness. It is shown by experimental results they outperform several
state-of-the-art word embedding methods.
