Invitation to take our preview release for a spin

Dear friends, many thanks for your continued support and patience while we spent past few months completely overhauling our technology from the ground up. We invite you to try our latest preview release at http://beta.nlpcore.com with following features in place.

Search Engine Improvements – Read at https://www.nlpcore.io/search-engine-improvements-in-preview-release

User Experience – Read at https://www.nlpcore.io/user-documentation

It is still a work in progress but we believe that features incorporated thus far will give you an opportunity to search for your specific terms, candidate Bioentities and their interactions as well as provide us your feedback upon quality of results provided.

With this release, we have major infrastructure changes both in our engine and in our user experience behind us and will be adding new features in coming weeks. In particular:

We will make Document View fully functional at par with Graph View with double-click / right-click functionality on color-coded entities or plain text to be promoted as entities.

We will incorporate a sign-in option that allows us to track your feedback for quality of results with-in your context (instead of global context in this release)

Beyond this preview release, we have a few more exploration, collaboration and transaction features planned but we hope to receive and prioritize your valuable and critical feedback from this preview release higher than our planned items.

We hope you will find time to try our latest iteration for a few hours, a couple of days as your schedule permits and send us your feedback at feedback@nlpcore.com. Thank you once again for your encouragement and support in making our search platform relevant and useful for biologists and beyond.

Search Engine Improvements in Preview Release

After months of experiments, we are finally getting ready to unravel our new search engine along with a preview release of our life sciences research portal. Here is a brief summary of improvements that we made in our core platform.

  • The new algorithm looks for search terms exhaustively across the entire corpus (currently NIH open access articles and abstracts totaling over 10 Million) as the initial search space
  • It quickly narrows down the search space based upon strength of word occurrences, their part of speech, their significance (as a dictionary term) and their relationship to other search terms
  • We have incorporated dictionaries to improve search relevance and make the interface generic such that we can continually add new dictionaries and results will get better automatically. Dictionaries can be applied freely – be it a list of know proteins, reagents or information categories. The search engine will identify matching words and improves their proximity search to discover more meaningful and contextually relevant items.
  • The new algorithm discovers new concepts above and beyond Bioentities or reagents, based upon their relevance to your search terms and their frequent occurrences in the search space. These concepts allow you to further filter search results in conjunction with Bioentities and reagents – thereby letting you precisely identify your candidate results.
  • We have made the search execution platform robust and incorporated search progress monitoring interface as part of all requests. User is given progress feedback across various stages of query execution.