What is NLP in Python?
Artificial Intelligence has evolved tremendously in the last decade, and so is one of its sub-fields – Natural Language Processing. The advancement in AI is a result of the massive computational capacity of the modern systems, and the large volumes of unstructured data that’s getting generated from a plethora of sources. Natural Language Processing or NLP is the study of AI which enables computers to process raw unstructured textual data and extract hidden insights from it.
Unlike humans, computers are not smart enough to process unstructured data. Human Beings could derive meanings from such data while computers could only do so with the structured data stored in the databases. To find patterns, and derive meaning from natural data, computers use the tools and techniques involved in NLP to process such data.
How NLP Works in Python?
It is very complex to read and understand English. The below sentence is one such example where it is really difficult for the computer to comprehend the actual thought behind the sentence.
In Machine Learning, a pipeline is built for every problem where each piece of a problem is solved separately using ML. The final result would be the combination of several machine learning models chained together. Natural Language Processing works similar to this where the English sentence is divided into chunks.
There are several facts present in this paragraph. Things would have been easy if computers themselves could understand what London is, but for doing so, the computers need to be trained with written language basic concepts.
1. Sentence Segmentation – The corpus is broken into several sentences like below.
This would make our life easier as it is better to process a single sentence than a paragraph as a whole. The splitting could be done based on punctuations, or several other complicated techniques which works on uncleaned data as well.
2. Word Tokenization – A sentence could further be split into the token of words as shown below.
After tokenization, the above sentence is split into –
3. Parts of Speech Prediction – This process is about generating the parts of speech for each token. This would enable us to understand the meaning of the sentence and the topic that is talked about in the sentence.
4. Lemmatization – A word in a sentence might appear in different forms. Lemmatization tracks a word back to its root i.e., the lemma of each word.
5. Stop words identification – There are a lot of filler words like ‘the’, ‘a’, in a sentence. These words act like noise in a text whose meaning we are trying to extract. Thus it is necessary to filter out those stop words to build a better model.
Based on the application, the stop words could vary. However, there is a pre-defined list of stop works one could refer to.
6. Named Entity Recognition – NER is the process of finding entities like name, place, person, organization, etc., from a sentence.
The context of the appearance of a word in a sentence is used here. To grab structured data out of a text, NER systems have a lot of uses.
Example of NLP in Python
Most companies are now willing to process unstructured data for the growth of their business. NLP has a wide range of uses, and of the most common use cases is Text Classification.
The classification of text into different categories automatically is known as text classification. The detection of spam or ham in an email, the categorization of news articles, are some of the common examples of text classification. The data used for this purpose need to be labeled.
The few steps in a text-classification pipeline which needs to be followed are –
- The loading and the pre-processing of the data is the first step, and then it would be split into train, and validation set.
- The Feature Engineering step involves extracting the useful features or creating additional meaningful features which would help in developing a better predictive model.
- To build the model, the labeled dataset is used to train the model.
Pandas, Scikit-learn, XGBoost, TextBlog, Keras are few of the necessary libraries we need to install. Then we would import the libraries for dataset preparation, feature engineering, etc.
The data is huge with almost 3.6 million reviews could be downloaded from here. A fraction of the data is used. It is download and read into a Pandas data frame.
The target variable is encoded and the data is split into train, and test sets.
Feature engineering is performed using the below different methods.
1. Count Vectors – The representation of a document, a term, and its frequency from a corpus is achieved by the count vectors.
2. TF-IDF Vectors – In a document, the relative importance of a term is represented by the Term Frequency (TF), and the Inverse Document Frequency (IDF) score. The TF-IDF could be calculated by –
The TF-IDF vectors could be generated by Word-level which presents the score of every term, and the N-gram level which is the combination of n-terms.
3. Word Embedding – The representation of documents and words in the form of a dense vector are known as word embedding. There are pre-trained embedding such as Glove, Word2Vec which could be used or it could be trained as well.
4. Topic models– It is the group of words from a document which carries the most information. The Latent Dirichlet Allocation is used here for topic modeling.
The mode is built after the feature engineering is done, and the relevant features have been extracted.
5. Naïve Bayes – It is based on Bayes Theorem, and the algorithm believes that there is no relationship among the features in a dataset.
6. Logistic Regression – It measures the linear relationship between the features, and the target variable is measured based on a sigmoid function which estimates the probabilities.
7. Support Vector Machine – A hyperplane separates two classes in an SVM.
8. Random Forest model – An ensemble model where reduces variance, and bags multiple decision trees together.
9. X G Boost – Bias is reduced, and weak learners converted to strong ones.
How NLP would help you in your career?
Natural Language Processing is a booming field in the market and almost every organization needs an NLP Engineer to help them process the raw data. Thus it’s imperative to master the skills required as there would be no shortage of jobs in the market.
Conclusion: NLP in Python
In this article, we started off with an introduction to NLP in Python and then implemented one use case in Python to show how to work with NLP in Python.
This has been a guide to the NLP in Python. Here we discussed the Example, Use cases, and how to work with NLP in Python. You can also go through our other suggested articles to learn more –