Visualization of Clandestine Labs from Seizure Reports: Thematic Mapping and Data Mining Research Directions
원문 초록
The problem of spatiotemporal event visualization based on reports entails subtasks ranging from named entity recognition to relationship extraction and mapping of events. We present an approach to event extraction that is driven by data mining and visualization goals, particularly thematic mapping and trend analysis. This paper focuses on bridging the information extraction and visualization tasks and investigates topic modeling approaches. We develop a static, finite topic model and examine the potential benefits and feasibility of extending this to dynamic topic modeling with a large number of topics and continuous time. We describe an experimental test bed for event mapping that uses this end-to-end information retrieval system, and report preliminary results on a geoinformatics problem: tracking of methamphetamine lab seizure events across time and space.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Neural Named Entity Recognition and Multi-Type Normalization Tool for Biomedical Text Mining
Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models
Label-Guided In-Context Learning for Named Entity Recognition
Named Entity Recognition in Historical Italian: The Case of Giacomo Leopardi's Zibaldone
A Comparative Analysis of Static Word Embeddings for Hungarian
Methods for Recognizing Nested Terms
토론
이 논문과 연결된 포럼 글이 아직 없습니다.