{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T07:52:14Z","timestamp":1772265134888,"version":"3.50.1"},"posted":{"date-parts":[[2018,4,12]]},"group-title":"PeerJ Preprints","reference-count":0,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T00:00:00Z","timestamp":1523491200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>We propose a simple Neural Network model which can learn relation between sentences by modeling the task as Earth Mover's Distance(EMD) calculation. Underlying hypothesis is that a neural module can learn to approximate the flow optimization in EMD calculation for sentence comparison. Our model is simple to implement, light in terms of parameters and works across multiple supervised sentence comparison tasks. We show good results for the model on two datasets. Our model combines LSTM with a relational unit to model sentence comparison.<\/jats:p>","DOI":"10.7287\/peerj.preprints.26847v1","type":"posted-content","created":{"date-parts":[[2018,4,12]],"date-time":"2018-04-12T11:08:25Z","timestamp":1523531305000},"source":"Crossref","is-referenced-by-count":0,"title":["Supervised Mover's Distance: A simple model for sentence comparison"],"prefix":"10.7287","author":[{"given":"Muktabh Mayank","family":"Srivastava","sequence":"first","affiliation":[{"name":"ParallelDots, Inc., Gurgaon, India"}]}],"member":"4443","container-title":[],"original-title":[],"link":[{"URL":"https:\/\/peerj.com\/preprints\/26847v1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/preprints\/26847v1.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/preprints\/26847v1.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/peerj.com\/preprints\/26847v1.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T19:01:52Z","timestamp":1577127712000},"score":1,"resource":{"primary":{"URL":"https:\/\/peerj.com\/preprints\/26847v1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,4,12]]},"references-count":0,"URL":"https:\/\/doi.org\/10.7287\/peerj.preprints.26847v1","relation":{"is-replaced-by":[{"id-type":"doi","id":"10.7287\/peerj.preprints.26847v2","asserted-by":"subject"}],"is-original-form-of":[{"id-type":"doi","id":"10.7287\/peerj.preprints.26847v2","asserted-by":"object"}]},"subject":[],"published":{"date-parts":[[2018,4,12]]},"subtype":"preprint"}}