Word Sense Disambiguation (WSD) is the task to determine the sense of an
ambiguous word in a given context. Previous approaches for WSD have focused on
supervised and knowledge-based methods, but inter-sense interactions patterns
or regularities for disambiguation remain to be found. We argue the following
cause as one of the major difficulties behind finding the right patterns: for a
particular context, the intended senses of a sequence of ambiguous words are
dependent on each other, i.e. the choice of one word's sense is associated with
the choice of another word's sense, making WSD a combinatorial optimization
this http URL this work, we approach the interactions between senses of different
target words by a Quadratic 0-1 Integer Programming model (QIP) that maximizes
the objective function consisting of (1) the similarity between candidate
senses of a target word and the word in a context (the sense-word similarity),
and (2) the semantic interactions (relatedness) between senses of all words in
the context (the sense-sense relatedness).

本文介绍了一种基于 QIP 模型的词义消歧方法，通过最大化目标函数中词义 - 单词相似性和词义 - 词义关联性来捕获目标词不同词义间的互动，以解决词义相互依赖导致的组合优化问题。