Visual Exploration of Neural Document Embedding in Information Retrieval: Semantics and Feature Selection

Xiaonan Ji, Han Wei Shen, Alan Ritter, Raghu MacHiraju, Po Yin Yen

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

Neural embeddings are widely used in language modeling and feature generation with superior computational power. Particularly, neural document embedding-converting texts of variable-length to semantic vector representations-has shown to benefit widespread downstream applications, e.g., information retrieval (IR). However, the black-box nature makes it difficult to understand how the semantics are encoded and employed. We propose visual exploration of neural document embedding to gain insights into the underlying embedding space, and promote the utilization in prevalent IR applications. In this study, we take an IR application-driven view, which is further motivated by biomedical IR in healthcare decision-making, and collaborate with domain experts to design and develop a visual analytics system. This system visualizes neural document embeddings as a configurable document map and enables guidance and reasoning; facilitates to explore the neural embedding space and identify salient neural dimensions (semantic features) per task and domain interest; and supports advisable feature selection (semantic analysis) along with instant visual feedback to promote IR performance. We demonstrate the usefulness and effectiveness of this system and present inspiring findings in use cases. This work will help designers/developers of downstream applications gain insights and confidence in neural document embedding, and exploit that to achieve more favorable performance in application domains.

Original languageEnglish
Article number8667702
Pages (from-to)2181-2192
Number of pages12
JournalIEEE Transactions on Visualization and Computer Graphics
Volume25
Issue number6
DOIs
StatePublished - Jun 1 2019

Keywords

  • Neural document embedding
  • feature selection
  • information retrieval
  • semantic analysis

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