What Is Natural Language Understanding

Throughout the book, we have covered various examples of using BERT for various tasks. Figure 1-17 illustrates the workings of a self-attention mechanism, which is a key component of a transformer. Interested readers can look at [30] for more details on self-attention mechanisms and transformer architecture.

T values per confidence level and degrees of freedom are available in t distribution tables. The t-test looks at the difference between observed and https://www.metadialog.com/ expected means, scaled by the variance of the data. It tells us how likely we are to get a sample (the real sample) of that mean and variance.

Search and content analytics

More advanced systems can summarize news articles and recognize complex language structures. Such systems must have a coarse understanding to compress the articles without losing the key meaning. AI can answer questions about things like flight times, give directions, tell you where restaurants are, and perform simple financial transactions. A sense signature is a vector/set of words which are related to a particular sense. TF-IDF can also be used, or we can take the number of all target word senses divided by the number of all the senses that appear with a feature F, and taking the logarithm.

Before the magic begins, at SPG, we believe it is crucial to spend some time pre-processing our data. This refers to a number of small, yet hugely important tasks that will essentially convert our sentences into a format machines can grasp. Ensuring regulatory compliance is a critical aspect of the maritime industry.

Natural Language Processing

Natural Language Processing is considered more challenging than other data science domains. Going by all the recent achievements of DL models, one might think that DL should be the go-to way to build NLP systems. In this scheme, the hidden layer gives a compressed representation of input data, capturing the essence, and the output layer (decoder) reconstructs the input examples of natural language processing representation from the compressed representation. While the architecture of the autoencoder shown in Figure 1-18 cannot handle specific properties of sequential data like text, variations of autoencoders, such as LSTM autoencoders, address these well. Throughout this book, we’ll discuss how all these approaches are used for developing various NLP applications.

Convolutional neural networks (CNNs) are very popular and used heavily in computer vision tasks like image classification, video recognition, etc. CNNs have also seen success in NLP, especially examples of natural language processing in text-classification tasks. One can replace each word in a sentence with its corresponding word vector, and all vectors are of the same size (d) (refer to “Word Embeddings” in Chapter 3).