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首页> 外文会议>3rd workshop on representation learning for NLP 2018 >Quantum-inspired Complex Word Embedding
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Quantum-inspired Complex Word Embedding

机译：量子启发式复杂词嵌入

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摘要

A challenging task for word embeddings is to capture the emergent meaning or polarity of a combination of individual words. For example, existing approaches in word embeddings will assign high probabilities to the words "Penguin" and "Fly" if they frequently co-occur, but it fails to capture the fact that they occur in an opposite sense - Penguins do not fly. We hypothesize that humans do not associate a single polarity or sentiment to each word. The word contributes to the overall polarity of a combination of words depending upon which other words it is combined with. This is analogous to the behavior of microscopic particles which exist in all possible states at the same time and interfere with each other to give rise to new states depending upon their relative phases. We make use of the Hilbert Space representation of such particles in Quantum Mechanics where we subscribe a relative phase to each word, which is a complex number, and investigate two such quantum inspired models to derive the meaning of a combination of words. The proposed models achieve better performances than state-of-the-art non-quantum models on the binary sentence classification task.
机译：单词嵌入的一项艰巨任务是捕获单个单词组合的新出现的含义或极性。例如，单词嵌入中的现有方法会经常向单词“ Penguin”和“ Fly \”分配高概率，但无法捕捉到它们以相反的意义出现的事实-企鹅会这样做不飞。我们假设人类不会将单个极性或情感与每个单词相关联。取决于单词与哪个单词组合在一起，单词对单词组合的整体极性有贡献。这类似于微观粒子的行为，它们同时以所有可能的状态存在，并根据它们的相对相位而相互干扰以产生新的状态。我们利用量子力学中此类粒子的希尔伯特空间表示法，在其中我们为每个单词赋予一个相对相位（即复数），并研究了两个这样的量子启发模型来得出单词组合的含义。在二进制句子分类任务上，提出的模型比最新的非量子模型具有更好的性能。

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