  Capturing the semantic relations of words in a vector space contributes to
many natural language processing tasks. One promising approach exploits
lexico-syntactic patterns as features of word pairs. In this paper, we propose
a novel model of this pattern-based approach, neural latent relational analysis
(NLRA). NLRA can generalize co-occurrences of word pairs and lexico-syntactic
patterns, and obtain embeddings of the word pairs that do not co-occur. This
overcomes the critical data sparseness problem encountered in previous
pattern-based models. Our experimental results on measuring relational
similarity demonstrate that NLRA outperforms the previous pattern-based models.
In addition, when combined with a vector offset model, NLRA achieves a
performance comparable to that of the state-of-the-art model that exploits
additional semantic relational data.
