Google NLP

Introduction

Before learning about Google NLP, let us first learn some basics about NLP itself.

• NLP stands for Natural Language Processing. In NLP, we analyze and synthesize the input and the trained NLP model then predicts the necessary output.
• NLP is the backbone of technologies like Artificial Intelligence and Deep Learning.
• In basic terms, we can say that NLP is nothing but the computer program's ability to process and understand the provided human language.
• The NLP process starts by first converting our input text into a series of tokens (called the Doc object) and then performing several operations on the Doc object.
• A typical NLP process consists of various stages like tokenizer, tagger, lemmatizer, parser, and entity recognizer. In every stage, the input is the Doc object and the output is the processed Doc.
• Every stage makes some kind of respective change to the Doc object and feeds it to the subsequent stage of the process.

Cloud computing is nowadays quite affordable and hence cloud service providers are also making the NLP service affordable for public users so that it can be widely used. The cloud service provides us with various APIs and other facilities that let us extract the information from large data sets and use them for our applications such as feature detection, voice assistance, social media recommendation, etc.

What is Google NLP?

With the popularity of the cloud and its associated technologies, giant cloud service providers are also providing a lot of facilities to use Natural Language Processing on the cloud. There are also a wide variety of already existing data sets that can also help us if we want to start our specific business. For example, there are numerous chatbot data available on the Google cloud that we can use to process our customer's needs and demands.

Google Cloud Natural Language (or Google NLP) is a combination of machine learning models and linguistics that provides us with features like sentiment analysis, categorization, syntax analysis, entity recognition, etc. Google's very own deep learning modules back the Google Cloud Natural Language services and that is why it is so accurate and popular. The Google deep learning modules are the backbone of the system that runs the famous Google services like language understanding, Google Search engine, Google Assistant, etc.

We can use the pre-trained models of the Google Natural Language APIs to apply the NLU (Natural Language Understanding) to our application and use the features like content classification, entity sentiment analysis, entity analysis, syntax analysis, semantic analysis, etc.

To use the Google NLP, please follow the steps shown below:

• Step 1. Login to the GCP platform.
• Step 2. Create an API key by setting the environment variable with our PROJECT_ID.
• Step 3. Create a service account that helps us to access the various features of the Google Natural Language API and other such APIs.
• Step 4. Create the credential which is used to login into the newly created service account.
• Step 5. We can now connect our instance using SSH and then open the Compute Engine tab to use the various API service.

To learn more about it, and for the practical demonstration, and lab, you can refer to the official page here.

Google also provides us with services like Google Healthcare Natural Language AI, Google AutoML, etc.

The Need for Google NLP

So far we have discussed a bit about Google NLP, let us now see the need of using Google NLP.

Some of the prime reasons for using Google NLP are:

1. We can apply the NLU or Natural Language Understanding to the Natural Language Processing applications with the help of Google NLP.
2. We can use Google NLP to train high-level and high-quality models (Machine Learning Models), we can even customize our model's learning with the help of easy code.
3. It can be integrated with the other Google popular APIs like Speech-to-Text API, Vision API, and translation API.
4. We can use its entity analysis feature to label the various fields of the input document and then use it for insights. Similarly, the sentiment analysis feature can be used to extract the human-level accurate meaning out of the input text.
5. The models are pre-trained with powerful data sets so it saves our money and time in training the model. Also, the training process takes a lot of computational power so we do not have to worry about such stuff.

Some of the use cases of using an NLP API like Google NLP are:

• identifying the fake reviews of a particular website.
• filtering of harmful and un-useful content out of a particular social media post.
• to write articles using the pre-trained NLP models on a scheduled basis.
• to create chatbots.
• to understand the user sentiment and needs, and thus show them desired products.
• to filter out the CVs or resumes by a recruiter. This helps in getting the accurate candidate for the respective position.
• to analyze the contracts and agreements with the help of pre-trained NLP models.

Google NLP Architecture

Google provides us with various data models and APIs to use the NLP services on the cloud. Google's GCP or Google Cloud Platform lets us use the NLP services. The GCP provides us with numerous services like Google Natural Language API, Google AutoML, Google Healthcare Natural Language AI, etc.

Let us see the overall architecture of the Google NLP with the help of the diagram shown below.

Features of Google NLP

The Google NLP is an interface that contains a lot of pre-trained NLP models that can process numerous tasks. Since these models are already heavily trained with large corpora or documents, we can easily rely on their performance. With Google NLP, we need not use our data sets for training the models we can directly use them. It is one of the biggest advantages of Google NLP.

The Google NLP is also quite useful in the field of marketing. The B2B brands can use its features to target the right audience and thus achieve greater profits. Some of its important features in the field of marketing, and other fields are as follows:

• With the help of Google NLP, we can easily identify the trends of an industry with the help of social media.
• With the help of sentiment analysis, we can send messages to the users in their favorable tone and language.
• We can also extract the features from the text for any research and other such tasks.
• We can also track client satisfaction with the help of the sentiment analysis feature.
• We can also integrate these features on a scheduled basis with the help of APIs.
• It can be used to identify a misspelled word by cross-matching it to a set of relevant words in the language dictionary used as a training set. The misspelled word is then fed to a machine-learning algorithm that calculates the word’s deviation from the correct one in the training set.
• It can also be used to understand and produce an accurate translation of global languages in both text and voice formats.

Building an Entity Extractor with Google NLP

Let us now learn how we can create an entity extractor or how we can analyze an entity. Let us see the step-wise approach.

1. Log in to the GCP platform using your email id and password.

2. Start the Cloud Shell that is used to fire commands.

3. Enable the API using the command (before using the Google NLP API, we must enable the Google APIs):

4. After enabling the API, we must authenticate the API requests with our service account. So, to create a service account, we first need to set up our project ID and then create the service account. The commands are:

5. Now, you need to create the credentials that will be used to log in to the service account. You can create and save the credentials in a JSON file (for example ~/key.json). The command for the same is:

6. Finally, we need to set the environment variables to use Python. So, to set the environment variable, we need to pass the credential that is stored in the ~/key.json file. The command for the same is:

7. Now the setup process is complete, we can install the client library (the Google Cloud Python client library) so that we can issue commands and use the Google NLP. The command for the same is:

8. We can perform the Entity Extraction and Analysis using the below code.

In a very similar manner, we can perform other operations like sentiment analysis, syntax analysis, etc.