Adequate representation of natural language semantics requires access to vast
amounts of common sense and domain-specific world knowledge. Prior work in the
field was based on purely statistical techniques that did not make use of
background knowledge, on limited lexicographic knowledge bases such as WordNet,
or on huge manual efforts such as the CYC project. Here we propose a novel
method, called Explicit Semantic Analysis (ESA), for fine-grained semantic
interpretation of unrestricted natural language texts. Our method represents
meaning in a high-dimensional space of concepts derived from Wikipedia, the
largest encyclopedia in existence. We explicitly represent the meaning of any
text in terms of Wikipedia-based concepts. We evaluate the effectiveness of our
method on text categorization and on computing the degree of semantic
relatedness between fragments of natural language text. Using ESA results in
significant improvements over the previous state of the art in both tasks.
Importantly, due to the use of natural concepts, the ESA model is easy to
explain to human users.

本文提出一种名为 Explicit Semantic Analysis（ESA）的新方法，基于 Wikipedia 的概念表示未受限制的自然语言文本的含义，用于文本分类和计算自然语言文本片段之间的语义相关性，此方法利用自然概念使 ESA 模型易于向人类用户解释，并在这两个任务中取得了显著的改进。