An Exploration of Discourse-Based Sentence Spaces for Compositional Distributional Semantics

By Tamara Polajnar, Laura Rimell, Stephen Clark. 2015

30 citations recorded by the source corpus.

This paper investigates whether the wider context in which a sentence is located can contribute to a distributional representation of sentence meaning. We compare a vector space for sentences in which the features are words occurring within the sentence, with two new vector spaces that only make use of surrounding context. Experiments on simple subject-verbobject similarity tasks show that all sentence spaces produce results that are comparable with previous work. However, qualitative analysis and user experiments indicate that extra-sentential contexts capture more diverse, yet topically coherent information.

Subfields: Artificial Intelligence

How researchers see this paper

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In Artificial Intelligence

1. Chen Fang
2. Virginia de Sa
3. Hailin Jin
4. Shuai Tang
5. Zhaowen Wang
6. Tao Mei
7. Gunhee Kim
8. Jianfei Cai

Citations in the corpus

• Trimming and Improving Skip-thought Vectors (2017, methodology): “[28] pointed out that the skip-thought model made use of the sentence-level distributional hypothesis [29, 30].”