A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?
- Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.
- Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
- Reduce the learning rate and run the training process until the training loss stops decreasing.
- Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
Answer(s): D
Explanation:
Word2vec is a technique that can learn distributed representations of words, also known as word embeddings, from large amounts of text data. Word embeddings can capture the semantic and syntactic similarities and relationships between words, and can be used as input features for neural network models. Word2vec can be trained on domain-specific corpora to obtain more relevant and accurate word embeddings for a particular task.