Correcting the Common Discourse Bias in Linear Representation of Sentences using Conceptors
This work addresses the challenge of generating sentence embeddings for natural language processing, specifically in the clinical domain, but appears incremental as it builds on existing word embedding techniques.
The paper tackled the problem of creating effective sentence embeddings by proposing a method that uses a weighted average of word vectors followed by a soft projection, and demonstrated its effectiveness on the clinical semantic textual similarity task of the BioCreative/OHNLP Challenge 2018, though no concrete numbers were provided.
Distributed representations of words, better known as word embeddings, have become important building blocks for natural language processing tasks. Numerous studies are devoted to transferring the success of unsupervised word embeddings to sentence embeddings. In this paper, we introduce a simple representation of sentences in which a sentence embedding is represented as a weighted average of word vectors followed by a soft projection. We demonstrate the effectiveness of this proposed method on the clinical semantic textual similarity task of the BioCreative/OHNLP Challenge 2018.