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

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.

Visualizing Article Similarities via Sparsified Article Network and Map Projection for Systematic Reviews.

Systematic Reviews (SRs) of biomedical literature summarize evidence from high-quality studies to inform clinical decisions, but are time and labor intensive due to the large number of article collections. Article similarities established from textual features have been shown to assist in the identification of relevant articles, thus facilitating the article screening process efficiently. In this study, we visualized article similarities to extend its utilization in practical settings for SR researchers, aiming to promote human comprehension of article distributions and hidden patterns.

Using ontology-based semantic similarity to facilitate the article screening process for systematic reviews.

Objective: Systematic Reviews (SRs) are utilized to summarize evidence from high quality studies and are considered the preferred source of evidence-based practice (EBP). However, conducting SRs can be time and labor intensive due to the high cost of article screening. In previous studies, we demonstrated utilizing established (lexical) article relationships to facilitate the identification of relevant articles in an efficient and effective manner.

Examining the Distribution, Modularity, and Community Structure in Article Networks for Systematic Reviews.

Systematic reviews (SRs) provide high quality evidence for clinical practice, but the article screening process is time and labor intensive. As SRs aim to identify relevant articles with a specific scope, we propose that a pre-defined article relationship, using similarity metrics, could accelerate this process. In this study, we established the article relationship using MEDLINE element similarities and visualized the article network with the Force Atlas layout.