Sentence detection

dc.accession.numberT00891
dc.classification.ddc006.35 SHA
dc.contributor.advisorMitra, Suman K.
dc.contributor.authorShah, Pushya
dc.date.accessioned2020-09-22T14:15:14Z
dc.date.accessioned2025-06-28T10:28:56Z
dc.date.available2023-02-17T14:15:14Z
dc.date.issued2020
dc.degreeM. Tech
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.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.student.id201811066
dc.subjectNatural Language Processing
dc.subjectDeep Learning
dc.subjectBERT
dc.subjectBiLSTM
dc.subjectWord Embedding
dc.titleSentence detection
dc.typeDissertation

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