Introduction

摘要

The idea of statistical analysis of language is an old idea, but modern NLP started with a focus on methods based on pure symbolic analysis of language. Statistical methods were introduced to NLP in its current form in the 1980s/1990s, allowing "soft" reasoning about language, and made NLP more data-driven. Over the last decade another step has been taken in this direction - it was proposed to represent and analyze language in vector spaces. Now-a-days, context, symbolic and high-dimensional representations are often augmented with relatively low-dimensional vector-space representations. Vector space representations have been successfully used in different areas of NLP such as syntax and semantics.