Over the past years, distributed representations have proven effective and flexible keepers of prior knowledge to be integrated into downstream applications. This survey is focused on semantic representation of meaning. We start from the theoretical background behind word vector space models and highlight one of its main limitations: the meaning conflation deficiency. Then, we explain how this deficiency can be addressed through a transition from word level to the more fine-grained level of word senses (in its broader acceptation) as a method for modelling unambiguous lexical meaning. We present a comprehensive overview of the wide range of techniques in the two main branches of sense representation, i.e., unsupervised and knowledge-based. Finally, this survey covers the main evaluation procedures and an analysis of five important aspects: interpretability, sense granularity, adaptability to different domains, compositionality and integration into downstream applications.

此研究综述了分布式语义表示的理论背景，介绍了从单词级别向更精细的词义级别转化的方法，并提供了对两种主要分支进行的广泛技术概述；最终，对其重要方面进行了分析与评估。

从词到义项嵌入：对意义向量表示的调查