{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,20]],"date-time":"2025-06-20T16:27:52Z","timestamp":1750436872619,"version":"3.28.0"},"reference-count":8,"publisher":"IEEE","license":[{"start":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T00:00:00Z","timestamp":1601942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T00:00:00Z","timestamp":1601942400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2020,10,6]],"date-time":"2020-10-06T00:00:00Z","timestamp":1601942400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,10,6]]},"DOI":"10.1109\/picst51311.2020.9468084","type":"proceedings-article","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T20:28:45Z","timestamp":1625257725000},"page":"335-337","source":"Crossref","is-referenced-by-count":9,"title":["Sentence Segmentation from Unformatted Text using Language Modeling and Sequence Labeling Approaches"],"prefix":"10.1109","author":[{"given":"Ievgen","family":"Iosifov","sequence":"first","affiliation":[]},{"given":"Olena","family":"Iosifova","sequence":"additional","affiliation":[]},{"given":"Volodymyr","family":"Sokolov","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref4","first-page":"9","article-title":"Language models are unsupervised multitask learners","volume":"1","author":"radford","year":"2019","journal-title":"OpenAIRE blog"},{"key":"ref3","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"vaswani","year":"2017","journal-title":"Advances in neural information processing systems"},{"journal-title":"RoBERTa A Robustly optimized BERT Pretraining Approach","year":"2019","author":"liu","key":"ref6"},{"journal-title":"Bert Pretraining of deep bidirectional transformers for language understanding","year":"2019","author":"devlin","key":"ref5"},{"key":"ref8","article-title":"Techniques comparison for natural language processing","author":"iosifova","year":"2020","journal-title":"8th International Conference on &#x201C;Mathematics Information Technologies Education"},{"key":"ref7","article-title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","author":"sanh","year":"2019","journal-title":"NeurIPS"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1002\/aic.690391009"},{"key":"ref1","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"rumelhart","year":"1986","journal-title":"Nature"}],"event":{"name":"2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&T)","start":{"date-parts":[[2020,10,6]]},"location":"Kharkiv, Ukraine","end":{"date-parts":[[2020,10,9]]}},"container-title":["2020 IEEE International Conference on Problems of Infocommunications. Science and Technology (PIC S&amp;T)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9467832\/9467885\/09468084.pdf?arnumber=9468084","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T21:55:51Z","timestamp":1656453351000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9468084\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,6]]},"references-count":8,"URL":"https:\/\/doi.org\/10.1109\/picst51311.2020.9468084","relation":{},"subject":[],"published":{"date-parts":[[2020,10,6]]}}}