A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System
Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes, comparing different models and the lack of evaluation based on real-world examples of use. We developed a novel, interactive human-in-the-loop topic modeling system with a user-friendly interface that enables users compare and record every step they take, and a novel topic words suggestion feature to help users provide feedback that is faithful to the ground truth. Our system also supports not only what traditional topic models can do, i.e., learning the topics from the whole corpus, but also targeted topic modelling, i.e., learning topics for specific aspects of the corpus. In this article, we provide an overview of the system and present the results of a series of user studies designed to assess the value of the system in progressively more realistic applications of topic modelling.
Code (0)
등록된 구현이 없습니다.
Tasks
Topic ModelsSimilar Papers 제목 키워드 기반
DeepSI: Interactive Deep Learning for Semantic Interaction
In this paper, we design novel interactive deep learning methods to improve semantic interactions in visual analytics applications. The ability of semantic interaction to infer analysts' precise intents during sensemakin…
Deep LearningUser Ex Machina : Simulation as a Design Probe in Human-in-the-Loop Text Analytics
Topic models are widely used analysis techniques for clustering documents and surfacing thematic elements of text corpora. These models remain challenging to optimize and often require a "human-in-the-loop" approach wher…
ClusteringSensitivityTopic ModelsScholastic: Graphical Human-Al Collaboration for Inductive and Interpretive Text Analysis
Interpretive scholars generate knowledge from text corpora by manually sampling documents, applying codes, and refining and collating codes into categories until meaningful themes emerge. Given a large corpus, machine le…
ClusteringI-MPN: Inductive Message Passing Network for Efficient Human-in-the-Loop Annotation of Mobile Eye Tracking Data
Comprehending how humans process visual information in dynamic settings is crucial for psychology and designing user-centered interactions. While mobile eye-tracking systems combining egocentric video and gaze signals ca…
NavigateObjectObject RecognitionVideo Segmentation+1iCap: Interactive Image Captioning with Predictive Text
In this paper we study a brand new topic of interactive image captioning with human in the loop. Different from automated image captioning where a given test image is the sole input in the inference stage, we have access…
Image CaptioningSentenceSentence Completion