An Introduction to Natural Language Processing NLP

According to the Zendesk benchmark, a tech company receives +2600 support inquiries per month. Receiving large amounts of support tickets from different channels (email, social media, live chat, etc), means companies need to have a strategy in place to categorize each incoming ticket. Predictive text, autocorrect, and autocomplete have become so accurate in word processing programs, like MS Word and Google Docs, that they can make us feel like we need to go back to grammar school.

Data generated from conversations, declarations or even tweets are examples of unstructured data. Unstructured data doesn’t fit neatly into the traditional row and column structure of relational databases, and represent the vast majority of data available in the actual world. Nevertheless, thanks to the advances in disciplines like machine learning a big revolution is going on regarding this topic.

Human Resources

This expertise is often limited and by leveraging your subject matter experts, you are taking them away from their day-to-day work. Words Cloud is a unique NLP algorithm that involves techniques for In this algorithm, the important words are highlighted, and then they are displayed in a table.

By enabling computers to understand human language, interacting with computers becomes much more intuitive for humans. Sentiment analysis is the process of identifying, extracting and categorizing opinions expressed in a piece of text. It can be used in media monitoring, customer service, and market research. The goal of sentiment analysis is to determine whether a given piece of text (e.g., an article or review) is positive, negative or neutral in tone. Today, we can see many examples of NLP algorithms in everyday life from machine translation to sentiment analysis. Lastly, symbolic and machine learning can work together to ensure proper understanding of a passage.

Lexical semantics (of individual words in context)

The main job of these algorithms is to utilize different techniques to efficiently transform confusing or unstructured input into knowledgeable information that the machine can learn from. Natural Language Processing (NLP) is a branch of Machine learning (ML) that is focused on making computers understand the human language. It is used to create language models, language translation apps like Google translate, and virtual assistants, among other things. Neural network algorithms are a type of machine learning algorithm that is designed to mimic the structure and function of the human brain.

As the name implies, NLP approaches can assist in the summarization of big volumes of text. Text summarization is commonly utilized in situations such as news headlines and research studies. A word cloud, sometimes known as a tag cloud, is a data visualization approach. Words from a text are displayed in a table, with the most significant terms printed in larger letters and less important words depicted in smaller sizes or not visible at all. You assign a text to a random subject in your dataset at first, then go over the sample several times, enhance the concept, and reassign documents to different themes.

Can Python be used for NLP?

In this article we have reviewed a number of different Natural Language Processing concepts that allow to analyze the text and to solve a number of practical tasks. We highlighted such concepts as simple similarity metrics, text normalization, vectorization, word embeddings, popular algorithms for NLP (naive bayes and LSTM). All these things are essential for NLP and you should be aware of them if you start to learn the field or need to have a general idea about the NLP. It is the branch of Artificial Intelligence that gives the ability to machine understand and process human languages. In this article, I will go through the 6 fundamental techniques of natural language processing that you should know if you are serious about getting into the field.

At the moment NLP is battling to detect nuances in language meaning, whether due to lack of context, spelling errors or dialectal differences. Lemmatization resolves words to their dictionary form (known as lemma) for which it requires detailed dictionaries in which the algorithm can look into and link words to their corresponding lemmas. The problem is that affixes can create or expand new forms of the same word (called inflectional affixes), or even create new words themselves (called derivational affixes). A potential approach is to begin by adopting pre-defined stop words and add words to the list later on.

common use cases for NLP algorithms

Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyze text and speech data efficiently. Natural language processing (NLP) is the ability of a computer program to understand human language as it is spoken and written — referred to as natural language. Statistical algorithms allow machines to read, understand, and derive meaning from human languages. Statistical NLP helps machines recognize patterns in large amounts of text. By finding these trends, a machine can develop its own understanding of human language. NLP algorithms can modify their shape according to the AI’s approach and also the training data they have been fed with.

Read more about NLP Importance and Common Types here.