Neural Network Revamps Search for Research
December 7, 2017
Research is a pain, especially when you have to slog through millions of results to find specific and accurate results. It takes time and lot of reading, but neural networks could cut down on the investigation phase. The Economist wrote a new article about how AI will benefit research: “A Better Way To Search Through Scientific Papers.”
The Allen Institute for Artificial Intelligence developed Semantic Search to aid scientific research. Semantic Search’s purpose is to discover scientific papers most relevant to a particular problem. How does Semantic Scholar work?
Instead of relying on citations in other papers, or the frequency of recurring phrases to rank the relevance of papers, as it once did and rivals such as Google Scholar still do, the new version of Semantic Scholar applies AI to try to understand the context of those phrases, and thus achieve better results.
Semantic Scholar relies on a neural network, a system that mirrors real neural networks and learns by trial and error tests. To make Semantic Search work, the Allen Institute team annotated ten and sixty-seven abstracts. From this test sample, they found 7,000 medical terms with which 2,000 could be paired. The information was fed into the Semantic Search neural network, then it found more relationships based on the data. Through trial and error, the neural network learns more patterns.
The Allen Institute added 26 million biomedical research papers to the already 12 million in the database. The plan is to make scientific and medical research more readily available to professionals, but also to regular people.
Whitney Grace, December 7, 2017
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