Search Results for author:
Found 4 papers, 0 papers with code
Tagging Funding Agencies and Grants in Scientific Articles using Sequential Learning Models
In this paper we present a solution for tagging funding bodies and grants in scientific articles using a combination of trained sequential learning models, namely conditional random fields (CRF), hidden markov models (HMM) and maximum entropy models (MaxEnt), on a benchmark set created in-house.
Using Local Alignments for Relation Recognition
We give a definition of a local alignment (LA) kernel based on the Smith-Waterman score as a sequence similarity measure and proceed with a range of possibilities for computing similarity between elements of sequences.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
