Despite a multitude of empirical studies, little consensus exists on whether neural networks
are able to generalise compositionally, a controversy that, in part, stems from a lack of …

Social networks like Twitter are good means for people to express themselves and ask for
help in times of crisis. However, to provide help, authorities need to identify informative posts …

This thesis contributes to ongoing research related to the categorical compositional model
for natural language of Coecke, Sadrzadeh and Clark in three ways: Firstly, I propose a …

Purpose The paper aims to provide a semiotic interpretation of the role played by
entanglement in quantum-based models aimed to information retrieval and suggests …

In recent years word embedding/distributional semantic models evolved to become a
fundamental component in many natural language processing (NLP) architectures due to …

摘要词汇语义表示是自然语言理解的基础. 传统的基于语义词典的编码表示构建成本高昂,
而独热表示又存在高维稀疏等缺点. 词汇的分布式表示将词汇映射为低维稠密的实值向量 …

This paper provides a method for improving tensor-based compositional distributional
models of meaning by the addition of an explicit disambiguation step prior to composition. In …

We propose a strategy to build the distributional meaning of sentences, which is mainly
based on two types of semantic objects: context vectors associated with content words and …

In recent years, distributional language representation models have demonstrated great
practical success. At the same time, the need for interpretability has elicited questions on …

Distributional semantic models represent the meaning of words as vectors. We introduce a
selection method to learn a vector space that each of its dimensions is a natural word. The …