Learning word embeddings from dependency relations

Continuous-space word representation has demonstrated its effectiveness in many natural language pro-cessing(NLP) tasks. The basic idea for embedding training is to update embedding matrix based on its context. However, such context has been constrained on fixed surrounding words, which we believe are not sufficient to represent the actual relations for given center word. In this work we extend previous approach by learning distributed representations from dependency structure of a sentence which can capture long distance relations. Such context can learn better semantics for words, which is proved on Semantic-Syntactic Word Relationship task. Besides, competitive result is also achieved for dependency embeddings on WordSim-353 task.