Adult semantic memory has been traditionally conceptualized as a relatively static memory
system that consists of knowledge about the world, concepts, and symbols. Considerable …

Recently, the popularity of using the expressive power of quantum computing to solve
known, challenging problems has increased remarkably. This study aims to develop a clear …

Answer sentence selection is the task of identifying sentences that contain the answer to a
given question. This is an important problem in its own right as well as in the larger context …

Neural network methods have achieved promising results for sentiment classification of text.
However, these models only use semantics of texts, while ignoring users who express the …

Vector‐based models of word meaning have become increasingly popular in cognitive
science. The appeal of these models lies in their ability to represent meaning simply by …

The lexicon of any natural language encodes a huge number of distinct word meanings. Just
to understand this article, you will need to know what thousands of words mean. The space …

We provide conceptual and mathematical foundations for near-term quantum natural
language processing (QNLP), and do so in quantum computer scientist friendly terms. We …

This paper proposes a framework for representing the meaning of phrases and sentences in
vector space. Central to our approach is vector composition which we operationalize in …

We use reinforcement learning to learn tree-structured neural networks for computing
representations of natural language sentences. In contrast with prior work on tree-structured …

In this paper we address the problem of modeling compositional meaning for phrases and
sentences using distributional methods. We experiment with several possible combinations …