Bibliographic Classification using the ADS Databases
Alberto Accomazzi, Michael J. Kurtz, Guenther Eichhorn, Edwin Henneken, Carolyn S. Grant, Markus Demleitner, Stephen S. Murray
Abstract
We discuss two techniques used to characterize bibliographic records based on their similarity to and relationship with the contents of the NASA Astrophysics Data System (ADS) databases. The first method has been used to classify input text as being relevant to one or more subject areas based on an analysis of the frequency distribution of its individual words. The second method has been used to classify existing records as being relevant to one or more databases based on the distribution of the papers citing them. Both techniques have proven to be valuable tools in assigning new and existing bibliographic records to different disciplines within the ADS databases.
Create a lesson
Related papers
Reasoning Quality Matters: Combating Reasoning Collapse in LLM-based Embedding Learning
Zihan Gong, Xiaohan Ye, Jiangchao Yao et al.
Think Thrice Before Reranking: Multi-perspective Evidence and Reasoning Integration for Text Reranking
Lijun Liu, Zhengzong Chen, Wenyan Li et al.
The Missing Complement: State-Conditioned Minimal Sufficient Evidence for Coding Agents
Zhexi Feng, Ruiyi Zhang, Yongbo Yang et al.
Reproducing Transparent and Scrutable Recommendations: Exploring Open-Weight Models via Natural-Language User Profiles
Noah Mamié, Laurin van den Bergh
Dense Feature Representation over Sequence Modeling: A Solution to the KDD Cup 2026 UniRec Challenge
Yi Zhang, Weiliang Ji
Self-Evolving Search Index
Sangam Lee, Wonjae Lee, Sunghwan Kim et al.