What is Natural Language Processing (NLP) and How Does It Work?
Natural language processing (NLP) is a field of computer science and artificial intelligence that focuses on enabling computers to understand and interpret human language. It is a type of artificial intelligence that deals with understanding the meaning behind words and sentences, as opposed to simply recognizing them. NLP is used in a variety of applications such as machine translation, dialogue systems, automated customer service, and more.
In NLP, the main goal is to teach computers to understand natural language by analyzing its syntax, semantics, and structure. By doing so, computers can be trained to interpret and respond to human language in a natural way. The most common approach to NLP is a supervised learning algorithm, in which the computer is trained on a set of data that contains examples of natural language. The algorithm then uses this data to make predictions about how to interpret similar sentences or words.
The most well-known natural language processing classifier consists of prediction and training. First, in the training process, the text input is processed, and all text features are created. Then, once the deep learning model acquires the text features, it uses them to make predictions when compared to new text that gets inputted.
You can build a natural language processing classifier using a naïve Bayes classifier from the textblob library which is built on top of NLTK. TextBlob is a very easy-to-use natural language processing tool API commonly used in many NLP problems.
Python’s scikit-learn library also provides a pipeline natural language processing framework you can use for text classification as follows.
Using Naïve Bayes Classifier
The most common approach to building a natural language processing classifier is by using a naïve Bayes classifier. This is a supervised learning algorithm that uses the Bayes theorem to make predictions.
Naïve Bayes classifiers work by analyzing the text features that have been extracted from the text input. It then uses these features to make predictions about the text. The text features can include words, phrases, and other elements of the text.
To build a naïve Bayes classifier using TextBlob, you need to first create a corpus of text samples, each labeled with its class. This can be done with the following code:
from textblob . classifiers import NaiveBayesClassifier as NBC
from textblob import TextBlob
training_corpus = [
( ‘Sample Text 1’, ‘Class_B’ ),
( “Sample Text 2”, ‘Class_B’ ),
( ‘Sample Text 3’, ‘Class_B’ ),
( ‘Sample Text 4’, ‘Class_B’ ),
( ‘Sample Text 5’, ‘Class_A’ ),
( ‘Sample Text 6’, ‘Class_A’ ),
( ‘Sample Text 7’, ‘Class_A’ ),
( ‘Sample Text 8’, ‘Class_A’ ),
( “Sample Text 9”, ‘Class_A’ ),
( ‘Sample Text 9, ‘Class_B’ ) ]
Test_corpus = [
( “Sample Text”, ‘Class_B’ ),
( “Sample Text”, ‘Class_A’ ),
( ‘Sample Text’, ‘Class_A’ ),
( “Sample Text”, ‘Class_B’ ),
( ‘Sample Text’, ‘Class_A’ ),
( ‘Sample Text’, ‘Class_B’ ) ]
model = NBC( training_corpus )
Once you have created the corpus, the next step is to create the model. The model is then trained on the training corpus and tested on the test corpus. The model can then be used to make predictions on new text. To print the results, you can use the following code:
print ( model . classify ( “Sample Text” ) )
“Class_A”
Print ( model . classify ( “Sample Text” ) )
“Class_B”
print( model . accuracy ( test_corpus ) )
0.83
Using Scikit-Learn
Python’s scikit-learn library also provides a pipeline natural language processing framework you can use for text classification. To use it, you need to first prepare your data for SVM model using the same text corpus and training corpus from the naïve Bayes example from above.
from sklearn . feature_extraction . text
import TfidfVectorizer from sklearn . metrics
import classification_report
from sklearn import svm
train_data = []
train_labels = []
for row in training_corpus:
train_data.append( row[0] )
train_labels.append( row[1] )
test_data = []
test_labels = []
for row in test_corpus:
test_data.append( row[0] )
test_labels.append( row[1] )
After that is complete, you must create and train your feature vectors.
vectorizer = TfidfVectorizer( min_df=4, max_df=0.9 )
train_vectors = vectorizer.fit_transform( train_data )
The next step is to apply a model on your test data and perform classification with SVM. Then you can print your results.
Test_vectors = vectorizer.transform( test_data )
model = svm.SVC( kernel=’linear’ )
model.fit( train_vectors, train_labels )
prediction = model.predict( test_vectors )
[‘Class_A’ ‘Class_A’ ‘Class_B’ ‘Class_B’ ‘Class_A’ ‘Class_A’ ]
print( classification_report( test_labels, prediction ) )
Improving Natural Language Processing Classifiers
The text classification models like these are dependent upon the quantity and quality of your model features. While applying deep learning models, it is always a good idea to include more training data.
You can always improve your natural language processing classifiers using several different techniques like text similarity, matching or coherence resolution. Text similarity looks to identify if two sentences or phrases have the same meaning. Matching looks to identify if two sentences or phrases are the same. Coherence resolution looks to identify if two sentences or phrases are related.
All of these techniques can help improve the accuracy of your natural language processing classifiers by providing more context and data to your model. This will help the model make more accurate predictions about the text that it is analyzing.
In conclusion, natural language processing is a powerful tool for understanding and interpreting human language. It can be used in a variety of applications such as machine translation, dialogue systems, automated customer service, and more. The most well-known natural language processing classifier consists of prediction and training, and it can be built using a naïve Bayes classifier from the textblob library or using a pipeline natural language processing framework from the scikit-learn library. Additionally, you can improve your natural language processing classifiers using several different techniques like text similarity, matching, or coherence resolution.