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Found 8 papers, 3 papers with code
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles
Ever since the COVID-19 pandemic broke out, the academic and scientific research community, as well as industry and governments around the world have joined forces in an unprecedented manner to fight the threat.
Overview of the DAGPap22 Shared Task on Detecting Automatically Generated Scientific Papers
As a test set, the participants are provided with a 5x larger corpus of openly accessible human-written as well as generated papers from the same scientific domains of documents.
Generating Topic Pages for Scientific Concepts Using Scientific Publications
In this paper, we describe Topic Pages, an inventory of scientific concepts and information around them extracted from a large collection of scientific books and journals.
EigenSent: Spectral sentence embeddings using higher-order Dynamic Mode Decomposition
Distributed representation of words, or word embeddings, have motivated methods for calculating semantic representations of word sequences such as phrases, sentences and paragraphs.
Semi-supervised Ensemble Learning with Weak Supervision for Biomedical Relationship Extraction
To tackle this problem, we propose a methodology for extending training datasets to arbitrarily big sizes and training complex, data-hungry models using weak supervision.
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection
The proposed method outperforms the existing state-of-the-art on a document-level novelty detection dataset by a margin of ∼5{\%} in terms of accuracy.
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.
Text Relatedness Based on a Word Thesaurus
Without doubt, a measure of relatedness between text segments must take into account both the lexical and the semantic relatedness between words.
