Double-target self-supervised clustering with multi-feature fusion for medical question texts.

ABSTRACT:

Background

To make the question text represent more information and construct an end-to-end text clustering model, we propose a double-target self-supervised clustering with multi-feature fusion (MF-DSC) for texts which describe questions related to the medical field. Since medical question-and-answer data are unstructured texts and characterized by short characters and irregular language use, the features extracted by a single model cannot fully characterize the text content.

Methods

Firstly, word weights were obtained based on term frequency, and word vectors were generated according to lexical semantic information. Then we fused term frequency and lexical semantics to obtain weighted word vectors, which were used as input to the model for deep learning. Meanwhile, a self-

SUBMITTER: Shen X 

PROVIDER: S-EPMC11637006 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature