Analysis of 1.01 million unfiltered Bing queries identifies 18% as geospatial, dominated by transactional categories like costs (15.3%) that exceed traditional GIS scope.
3 David Arthur and Sergei Vassilvitskii
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
LLM reasoning refines unsupervised text clusters via coherence checks, redundancy removal, and label grounding, yielding better coherence and human-aligned labels on social media data.
PRISM distills sparse LLM labels into a fine-tuned embedding model for thresholded clustering that separates fine-grained topics better than prior local models or raw frontier embeddings.
Bibliometric methods rise from 19.61% to 31.81% usage as LIS scholars age, method diversity increases then declines, and scholars increasingly combine conventional and unconventional methods.
Granite Embedding Multilingual R2 releases 311M and 97M parameter bi-encoder models that achieve state-of-the-art retrieval performance on multilingual text, code, long-document, and reasoning datasets.
citing papers explorer
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Much of Geospatial Web Search Is Beyond Traditional GIS
Analysis of 1.01 million unfiltered Bing queries identifies 18% as geospatial, dominated by transactional categories like costs (15.3%) that exceed traditional GIS scope.
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Reasoning-Based Refinement of Unsupervised Text Clusters with LLMs
LLM reasoning refines unsupervised text clusters via coherence checks, redundancy removal, and label grounding, yielding better coherence and human-aligned labels on social media data.
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PRISM: LLM-Guided Semantic Clustering for High-Precision Topics
PRISM distills sparse LLM labels into a fine-tuned embedding model for thresholded clustering that separates fine-grained topics better than prior local models or raw frontier embeddings.
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Evolution of Research Method Usage Across the Academic Careers of Library and Information Science Scholars
Bibliometric methods rise from 19.61% to 31.81% usage as LIS scholars age, method diversity increases then declines, and scholars increasingly combine conventional and unconventional methods.
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Granite Embedding Multilingual R2 Models
Granite Embedding Multilingual R2 releases 311M and 97M parameter bi-encoder models that achieve state-of-the-art retrieval performance on multilingual text, code, long-document, and reasoning datasets.