366 citations · 866 across the 25 of their papers we have counts for
30 papers
Unsupervised Pre-training for Biomedical Question Answering
Vaishnavi Kommaraju, Karthick Gunasekaran, Kun Li +4
We explore the suitability of unsupervised representation learning methods on biomedical text -- BioBERT, SciBERT, and BioSentVec -- for biomedical question answering. To further i…
A Simple Approach to Case-Based Reasoning in Knowledge Bases
Rajarshi Das, Ameya Godbole, Shehzaad Dhuliawala +2
We present a surprisingly simple yet accurate approach to reasoning in knowledge graphs (KGs) that requires \emph{no training}, and is reminiscent of case-based reasoning in classi…
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types
Xin Luna Dong, Xiang He, Andrey Kan +19
Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question a…
Using BibTeX to Automatically Generate Labeled Data for Citation Field Extraction
Dung Thai, Zhiyang Xu, Nicholas Monath +2
Accurate parsing of citation reference strings is crucial to automatically construct scholarly databases such as Google Scholar or Semantic Scholar. Citation field extraction (CFE)…
Scalable Hierarchical Clustering with Tree Grafting
Nicholas Monath, Ari Kobren, Akshay Krishnamurthy +2
We introduce Grinch, a new algorithm for large-scale, non-greedy hierarchical clustering with general linkage functions that compute arbitrary similarity between two point sets. Th…
Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision
Trapit Bansal, Pat Verga, Neha Choudhary +1
Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonic…