Sentence_Restoration
Sentence Restoration from Automated Speech Recognition Transcripts. Unlike Sentence Boundary Disambiguation or Punctuation Restoration, this project has the limited but important (from an NLP perspective) task of taking automated speech transcripts which have zero punctuation and building sentences from them, necessary for all downstream NLP tasks (e.g. usage of BERT and similar large language models, POS taggers, NER, etc.)
Requirements: Tensorflow 2.10.1, and non-conflicting versions of nltk, pyodbc, numpy, sklearn, GloVe pretrained vectors (https://nlp.stanford.edu/projects/glove) - this code used the version from the Common Crawl (42B tokens, 1.9M vocab, uncased, 300d vectors) and Ted Talk transcripts (or your own collection of transcripts).
Code uses a 6-layer Recurrent Neural Network, converting the text into GloVe word vectors passed through three layers of a Bidirectional Long Short Term Memory (BiLSTM) before using a Dropout layer for overfitting and a final Dense layer to classify sequences of words into sequences of labels which describe which words in the sequence of words (if any) ends a sentence.
Next phase - feed the sentences into a BERT CNN supervised model to predict the relationship between transcripts and key business success metrics. Later future improvement - add audio input to better predict sentence endings using vocal inflections and pause times.