Evaluate the model using test set with metrics precision

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Reference no: EM133703617

Neural Network Approach for WSD:

In this question, you will build WSD using a Bi-directional LSTM. You may use PyTorch, Keras or Tensorflow for this task.

1. Split the data such that the 50 sentences used in question 1, will be the test set for this question and remaining sentences will be the train set. Build a vocabulary of all the words in the SemCor dataset and assign unique index to each word.

2. Extract WordNet tags as the labels for evaluation. Note that for stop words, the data does not have the tags.

3. Build a neural network model with an Embedding layer of dimension 100, Bi-directional LSTM layers, a Dense layer, and an output layer with the SoftMax activation function.

4. Train the model assuming your own hyper-parameters such as epochs, optimizer, learn ing rate, etc.

5. Evaluate the model using test set with metrics precision, recall, and F-score. Report the hyper-parameters used for model training.

6. With examples from test set, compare and analyse the results of the 3 modelsl. Your analysis will include the performance of each model, if the model is able to disambiguate the senses, if it fails then why, etc.

Reference no: EM133703617

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