TensorFlow Natural Language Processing

In this Python tutorial, we will focus on how to deploy a Natural Language Processing model with TensorFlow, and also we will look at some examples of how we can learn natural language processing in TensorFlow. And we will cover these topics.

• TensorFlow natural language Processing
• What are the benefits of natural language processing
• What are the advantages of natural language processing
• What are the disadvantages of natural language processing

TensorFlow natural language Processing

• Natural language Processing is a crucial tool for comprehending and interpreting the enormous amount of unstructured data that exists nowadays.
• Due to the impressive results that deep learning algorithms have achieved in a variety of difficult tasks, such as picture classification, speech recognition, and realistic text production, deep learning has recently been widely embraced for many NLP applications.
• The most user-friendly and effective deep learning framework currently in use is TensorFlow.
• Many practical applications exist for Natural Language Processing. A system with a high number of NLP tasks is considered to be good. You rely on a portion of these NLP activities when you use Google Translate to learn how to say “Where are you?” .
• Let’s have a look at some of the basics of NLP.
  • Tokenization: Tokenization is the initial stage of any NLP pipeline. It significantly affects the remainder of your pipeline. Tokenization is the process of dividing unstructured data and natural language text into units of data that can be regarded as discrete pieces. One can directly utilize a document’s token occurrences as a vector to represent the document.  This instantly converts a text document or unstructured string into a numerical data format appropriate for machine learning. They can also be directly employed by a computer to initiate helpful answers and actions. They could also be employed as features in a machine learning pipeline to initiate more complicated actions or judgments.
  • Word sense disambiguation: An essential NLP technique for determining a word’s meaning in the context in which it is used is called word sense disambiguation. It can be difficult for NLP systems to identify words correctly, therefore it might be useful to know how a word is used in a given sentence. The confusion that develops from determining the meaning of a word when it is employed in several contexts is essentially resolved by word sense disambiguation.
  • Sequencing: Now that our words are represented in this way, the next step is to represent our sentences using the right sequence of numbers. The data will then be prepared for a neural network to process in order to interpret or perhaps even generate new text.

Example:

Let’s take an example and use the tokenizer function

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer

new_words = ['The Clever fox',
             'Jumps over the lazy dog',
             ]

result = Tokenizer(num_words = 50)

result.fit_on_texts(new_words)

word_index = result.word_index

print(word_index)

In this example, we imported required libraries and then create a list of sample sentences that we want to tokenize. Next, we will declare a tokenizer that can have an index and the number of words is the maximum number of words that can be kept.

After that, we will fit the sentence for creating a tokenizer object. The Output will be a dictionary.

Here is the Screenshot of the following given code.

This is how we can use the tokenizer by using natural language Processing

Example 2:

Now we will take an example of natural language processing by using the Sequencing

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer

new_words = ['The Clever fox',
             'Jumps over the lazy dog',
             'you love my dog!',
            ]

new_result = Tokenizer(num_words = 50)
new_result.fit_on_texts(new_words)
output = new_result.word_index

new_sequences = new_result.texts_to_sequences(new_words)

print(output)
print(new_sequences)

You can refer to the below Screenshot

As you can see in the Screenshot the Output displays the index values with words by using the sequential method.

Read: TensorFlow next_batch + Examples

What are the benefits of natural language processing

• In this section, we will discuss the benefits of natural language processing.
  • Perform large-scale analysis: Scalable text analysis on a variety of documents, internal systems, emails, social media data, online reviews, and more is made possible by Natural Language Processing technology. Additionally, NLP tools may scale up or down instantly to accommodate your demands, giving you the amount of computational power you require.
  • Rank higher in search: In a company, search engine optimization is crucial. Every business aspires to have a high ranking. Naturally, Natural Language Processing can be useful. Solutions evaluate search queries, find and recommend similar keywords, and then optimize our content while saving time on research.
  • Manufacturing: Manufacturers can boost automation and streamline processes by using NLP to analyze data. They can make improvements to increase efficiency and immediately get the decision on the areas that require improvement.
  • Healthcare: In order to better diagnose and treat patients and provide better results, NLP can evaluate patient communication through emails, chat applications, and patient helplines.

Read: TensorFlow global average pooling

What are the advantages of natural language processing

• Here we will discuss some advantages of natural language processing
• Natural language processing helps the user to ask questions about any subject and get a result within a second.
• Natural language processing helps the computer to communicate with humans in any language.
• It is a faster customer service response time and also it is very time efficient.
• It will allow you to compare more language-based data to a human without getting tired and in a fair and consistent manner.
• Natural language processing is less costly as compared to hiring a human because a human can take three or four times longer than a machine to implement the task.

Read: Binary Cross Entropy TensorFlow

What are the disadvantages of natural language processing

• In this section, we will discuss the disadvantages of natural language processing.
• Natural language processing has an accuracy of documentation and it is unable to grab the new domain and it has a limited function that’s why Natural language Processing is built for a specific task.
• If the query is short-written, the algorithm might not be able to give the right response.

You may also like to read the following Python TensorFlow.

• Tensorflow embedding_lookup
• TensorFlow clip_by_value
• TensorFlow Multiplication
• TensorFlow Get Variable
• How to convert TensorFlow to one hot
• TensorFlow Fully Connected Layer
• Module ‘tensorflow’ has no attribute ‘truncated_normal’

In this tutorial, we have focused on how to deploy a TensorFlow Natural Language Processing model, and also we have looked at some examples of how we can learn natural language processing in TensorFlow. And we have covered these topics.

• TensorFlow natural language Processing
• What are the benefits of natural language processing
• What are the advantages of natural language processing
• What are the disadvantages of natural language processing