Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages
Multilingual topic models can reveal patterns in cross-lingual document collections. However, existing models lack speed and interactivity, which prevents adoption in everyday corpora exploration or quick moving situations (e.g., natural disasters, political instability). First, we propose a multilingual anchoring algorithm that builds an anchor-based topic model for documents in different languages. Then, we incorporate interactivity to develop MTAnchor (Multilingual Topic Anchors), a system that allows users to refine the topic model. We test our algorithms on labeled English, Chinese, and Sinhalese documents. Within minutes, our methods can produce interpretable topics that are useful for specific classification tasks.
Code (1)
Tasks
General ClassificationTopic ModelsMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Tandem Anchoring: a Multiword Anchor Approach for Interactive Topic Modeling
Interactive topic models are powerful tools for those seeking to understand large collections of text. However, existing sampling-based interactive topic modeling approaches scale poorly to large data sets. Anchor method…
Document ClassificationInformation RetrievalSentiment AnalysisTopic ModelsXTRA: Cross-Lingual Topic Modeling with Topic and Representation Alignments
Cross-lingual topic modeling aims to uncover shared semantic themes across languages. Several methods have been proposed to address this problem, leveraging both traditional and neural approaches. While previous methods …
Contrastive LearningGloCTM: Cross-Lingual Topic Modeling via a Global Context Space
Cross-lingual topic modeling seeks to uncover coherent and semantically aligned topics across languages - a task central to multilingual understanding. Yet most existing models learn topics in disjoint, language-specific…
Mapping how LLMs debate societal issues when shadowing human personality traits, sociodemographics and social media behavior
Large Language Models (LLMs) can strongly shape social discourse, yet datasets investigating how LLM outputs vary across controlled social and contextual prompting remain sparse. Cognitive Digital Shadows (CDS) is a 190,…
Code-Switching Reveals Language Anchoring in Multilingual LLMs
Multilingual Large Language Models (MLLMs) are increasingly expected to handle Code-Switched (CS) inputs, yet mixing languages frequently degrades performance relative to source- or target-language monolingual counterpar…
Question Answering