Our objective is to accurately identify the author of the sentences in the test set.īefore getting started with our code, let’s import all the required libraries. The data was prepared by chunking larger texts into sentences using CoreNLP’s Ma圎nt sentence tokenizer, so we may notice the odd non-sentence here and there. This is a Kaggle competition dataset contains text from works of fiction written by spooky authors of the public domain: Edgar Allan Poe, HP Lovecraft, and Mary Shelley. Now we will solve an author classification problem based on text documents. One LSTM layer on the input sequence and second LSTM layer on the reversed copy of the input sequence provides more context for learning sequences: Let’s Start Coding: Each LSTM cells have four neural network layers interacting within.Įach LSTM cell receives an input from an Input sequence, previous cell state and output from previous LSTM cell.īidirectional LSTM trains two layers on the input sequence. ![]() LSTM has chains of repeating the LSTM block. LSTM is explicitly designed to avoid the long-term dependency problem. It is introduced by Hochreiter & Schmidhuber (1997). It is capable of learning long-term dependencies. Long Short Term Memory network usually just called “LSTM” - is a special kind of RNN. ![]() Ii) This works well for short sentences, when we deal with a long article, there will be a long term dependency problem I) RNN suffers from exploding and vanishing gradient, which makes the RNN model learn slower by propagating a lesser amount of error backward. Ii) RNNs are ideal for text and speech data analysis. I) RNN has a memory that captures what has been calculated so far.
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