Dynamic embedded topic models and change-point detection for exploring literary-historical hypotheses
We present a novel combination of dynamic embedded topic models and change-point detection to explore diachronic change of lexical semantic modality in classical and early Christian Latin. We demonstrate several methods for finding and characterizing patterns in the output, and relating them to traditional scholarship in Comparative Literature and Classics. This simple approach to unsupervised models of semantic change can be applied to any suitable corpus, and we conclude with future directions and refinements aiming to allow noisier, less-curated materials to meet that threshold.
Code (0)
등록된 구현이 없습니다.
Tasks
Change Point DetectionTopic ModelsSimilar Papers 제목 키워드 기반
Merging Embedded Topics with Optimal Transport for Online Topic Modeling on Data Streams
Topic modeling is a key component in unsupervised learning, employed to identify topics within a corpus of textual data. The rapid growth of social media generates an ever-growing volume of textual data daily, making onl…
Change Point DetectionReal-time Change Point Detection using On-line Topic Models
Detecting changes within an unfolding event in real time from news articles or social media enables to react promptly to serious issues in public safety, public health or natural disasters. In this study, we use on-line …
ArticlesChange Point DetectionTime Series AnalysisTopic ModelsChangepoint Analysis of Topic Proportions in Temporal Text Data
Changepoint analysis deals with unsupervised detection and/or estimation of time-points in time-series data, when the distribution generating the data changes. In this article, we consider \emph{offline} changepoint dete…
Time SeriesTime Series AnalysisNarrative Shift Detection: A Hybrid Approach of Dynamic Topic Models and Large Language Models
With rapidly evolving media narratives, it has become increasingly critical to not just extract narratives from a given corpus but rather investigate, how they develop over time. While popular narrative extraction method…
ArticlesChange Point DetectionLanguage ModelingLanguage Modelling+2PERCEPT: a new online change-point detection method using topological data analysis
Topological data analysis (TDA) provides a set of data analysis tools for extracting embedded topological structures from complex high-dimensional datasets. In recent years, TDA has been a rapidly growing field which has…
Change Point DetectionTopological Data Analysis