Attributes of words and relations between two words are central to numerous
tasks in Artificial Intelligence such as knowledge representation, similarity
measurement, and analogy detection. Often when two words share one or more
attributes in common, they are connected by some semantic relations. On the
other hand, if there are numerous semantic relations between two words, we can
expect some of the attributes of one of the words to be inherited by the other.
Motivated by this close connection between attributes and relations, given a
relational graph in which words are inter- connected via numerous semantic
relations, we propose a method to learn a latent representation for the
individual words. The proposed method considers not only the co-occurrences of
words as done by existing approaches for word representation learning, but also
the semantic relations in which two words co-occur. To evaluate the accuracy of
the word representations learnt using the proposed method, we use the learnt
word representations to solve semantic word analogy problems. Our experimental
results show that it is possible to learn better word representations by using
semantic semantics between words.

通过语义关系学习单词的潜在表示，结合单词共现和语义关系，解决语义单词类比问题。