TextEssence: A Tool for Interactive Analysis of Semantic Shifts Between Corpora
Not on the map. Why?
Abstract (arXiv)
Embeddings of words and concepts capture syntactic and semantic regularities of language; however, they have seen limited use as tools to study characteristics of different corpora and how they relate to one another. We introduce TextEssence, an interactive system designed to enable comparative analysis of corpora using embeddings. TextEssence includes visual, neighbor-based, and similarity-based modes of embedding analysis in a lightweight, web-based interface. We further propose a new measure of embedding confidence based on nearest neighborhood overlap, to assist in identifying high-quality embeddings for corpus analysis. A case study on COVID-19 scientific literature illustrates the utility of the system. TextEssence is available from https://github.com/drgriffis/text-essence.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 52 references that are in the map. First posted on arXiv 19 Mar 2021.
Builds on
18 of 52 references.
References · 18
- 2016node2vec: Scalable Feature Learning for Networkscited by 1,913
- 2017Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypescited by 251
- 2019ScispaCy: Fast and Robust Models for Biomedical Natural Language Processingcited by 244
- 2016Diachronic Word Embeddings Reveal Statistical Laws of Semantic Changecited by 219
- 2020SemEval-2020 Task 1: Unsupervised Lexical Semantic Change Detectioncited by 105
- 2018Diachronic word embeddings and semantic shifts: a surveycited by 90
- 2014Statistically Significant Detection of Linguistic Changecited by 82
- 2019Distributional Semantics and Linguistic Theorycited by 53
- 2016Embedding Projector: Interactive Visualization and Interpretation of Embeddingscited by 43
- 2018Clinical Concept Embeddings Learned from Massive Sources of Multimodal Medical Datacited by 33
- 2020COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generationcited by 32
- 2018Factors Influencing the Surprising Instability of Word Embeddingscited by 31
6 more
- 2019How we do things with words: Analyzing text as social and cultural datacited by 11
- 2019BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scalecited by 8
- 2018Jointly Embedding Entities and Text with Distant Supervisioncited by 7
- 2020Going Beyond T-SNE: Exposing textttwhatlies in Text Embeddingscited by 3
- 2020Exploring Neural Entity Representations for Semantic Informationcited by 2
- 2019Writing habits and telltale neighbors: analyzing clinical concept usage patterns with sublanguage embeddingscited by 1
Built on by
Citing papers per year: 2022: 1.
1 paper.