Top 5 Natural Language Process (NLP) Techniques

Top 5 Natural Language Process (NLP) Techniques

What is Natural Language Processing (NLP)?

To communicate naturally with a machine.

The AI-powered method of adapting human language understandable to software is called Natural Language Processing. In case your business software only grasps a foreign language, you get NLP as your translator. To make that possible, there are various NLP techniques and we will be discussing the top five of them here.

1. Sentiment Analysis

Sentiment Analysis is an NLP specialty. This mechanism dissects data (be it text or voice) to figure out if the sentiment conveyed is negative, neutral, or positive. Basically, takes the input and deciphers it into the invested emotion. Your text predictor on keypads suggests emojis to use according to the type of text you type. NLP Techniques Python is impossible to imagine without the sentiment analysis process in the equation.

How it is helpful is it helps identifies your user’s emotions and aids you to learn what they prefer and not in your development. Sentiment Analysis has various benefits; aside from allowing you to learn about your user’s feedback, it allows you more room to improve and enhance your product. Isn’t sentiment analysis simply amazing? You bet it is.

2. Text Summarization

Text Summarization refers to the task of dissecting jargon and identifying if it is technical, medical, scientific, or anything else. Having done that, this feature conveys that jargon is the most used and understandable term. Isn’t this too tiring since our languages vastly differ? Implementing some of the primary verb-noun algorithms, a text summary can be immediately generated into concise jargon. Australia NLP is incomplete without the process of text summarization in the whole NLP methodology and this all can be properly executed via a custom software system.

We often have complex jargon beyond the comprehension of a certain group of audience among the general public as we are at best bilinguals. Since not everyone knows foreign terms, this is exactly why text summarization is needed to make things easier for such a lot. Thank the NLP for the gifts it provides.

3. Named Entity Recognition

What Named Entity Recognition (NER) does is it tags named identities and extracts them from within text for more analysis. This NLP technique is similar to sentiment analysis with the only difference being that NER merely tags identities whether they are locations, proper nouns, people, or organizations. What limits NER’s process is either the content team’s imagination or your feedback as NLP Australia often explains the limitations of NER.

If the named identities are not tagged, it does not get clearer which task is assigned to the development team, resulting in more confusion and unwanted chaos across development. NER is nonetheless a blissful process that helps things get easier. So without NER, the completion of NLP techniques is not possible and it will come off as hollow even if it the other tasks are done according to their own requirements.

4. Keyword Extraction

Keyword extraction is the automated technique of filtering the most important information from within text with the help of machine learning and AI algorithms. Having done the process, what we get is the most relevant and on-point piece of information to work with. Australia NLP considers keyword extraction an integral part of the entire natural language processing system. This entire process of keyword extraction can be conducted smoothly through a custom software system instead of relying on off-the-shelf solutions.

Furthermore, SEO is powered with the help of keyword extraction as well. If you want your website to come on top, be it via a direct solution or via a custom software system, you will need some sort of keyword extraction to proceed with. That’s where this NLP technique will be made practical for you.

5. Stemming and Lemmatization

Stemming and Lemmatization is the process of restructuring, tagging, and breaking down text data based on either definition or root stem. Different valuable data can be achieved by sorting each of these processes. Combing through each process and understanding the relevant guide, this process can be executed smoothly. NLP Techniques Python has to rely on this particular phase of stemming and lemmatization to sort out text-based data.

Just imagine a process where you do not get to break down, tag, or restructure your text-based data. It would cost double the time and double the energy as well as the cost to filter out valuable data and put it to some pragmatic use helpful both for the developers and for the clients. Getting nowhere without lemmatization and stemming, are we?

These are the top 5 NLP techniques in use and application most commonly nowadays. If you want to perform something that involves NLP, you would need to have some knowledge of these techniques.


Natural Language Processing is a reliable subfield of Artificial Intelligence. As it is growing more in the tech industry, many developers and businesses are adopting the NLP techniques. It is important to not miss out on them if you want to further your career in NLP. This is why to learn NLP in Australia properly, you must be aware of the techniques described above. There are other NLP techniques as well that we could not possibly have described since we are bound to only explain five here, you may check on them as well. Hopefully, this read has helped you gain valuable knowledge on the top five NLP techniques.

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