Please use this identifier to cite or link to this item: http://drsr.daiict.ac.in//handle/123456789/973
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dc.contributor.advisorMitra, Suman K.
dc.contributor.authorShah, Pushya
dc.date.accessioned2020-09-22T14:15:14Z
dc.date.available2023-02-17T14:15:14Z
dc.date.issued2020
dc.identifier.citationShah, Pushya (2020). Sentence detection. Dhirubhai Ambani Institute of Information and Communication Technology. vi, 13 p. (Acc.No: T00891)
dc.identifier.urihttp://drsr.daiict.ac.in//handle/123456789/973
dc.description.abstractSentence detection is a very important task for any natural language processing (NLP) application. Accuracy and performance of all other downstream natural language processing (NLP) task like Sentiment, Text Classification, named entity recognition (NER), Relation, etc depends on the accuracy of correctly detected sentence boundary. Clinical domain is very different compare to general domain of languages. Clinical sentence structure and vocabulary are different from general English. That’s why available sentence boundary detector tools are not performing well on clinical domain and we required a specific sentence detection model for clinical documents. ezDI Solutions (India) LLP have developed such system that can detect the sentence boundary. We examined Bidirectional Encoder Representations from Transformers (BERT) and Bidirectional Long Short-Term Memory (BiLSTM) algorithm and used BiLSTM-BERT hybrid model for sentence boundary detection on medical corpora.
dc.subjectNatural Language Processing
dc.subjectDeep Learning
dc.subjectBERT
dc.subjectBiLSTM
dc.subjectWord Embedding
dc.classification.ddc006.35 SHA
dc.titleSentence detection
dc.typeDissertation
dc.degreeM. Tech
dc.student.id201811066
dc.accession.numberT00891
Appears in Collections:M Tech Dissertations

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