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Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping
Second, we use a reference set of entity names (e. g., proteins in UniProt) to identify entity mentions with high precision, but low recall, on an unlabeled corpus.
NSEEN: Neural Semantic Embedding for Entity Normalization
An important task in this process is entity normalization, which consists of mapping noisy entity mentions in text to canonical entities in well-known reference sets.
Adaptive Neighborhood Graph Construction for Inference in Multi-Relational Networks
A neighborhood graph, which represents the instances as vertices and their relations as weighted edges, is the basis of many semi-supervised and relational models for node labeling and link prediction.
Comparing apples to apples in the evaluation of binary coding methods
We discuss methodological issues related to the evaluation of unsupervised binary code construction methods for nearest neighbor search.
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