Natural Language Processing NLP: 7 Key Techniques

In addition, NLP’s data analysis capabilities are ideal for reviewing employee surveys and quickly determining how employees feel about the workplace. Let’s look at some of the most popular techniques used in natural language processing. Note how some of them are closely intertwined and only serve as subtasks for solving larger problems.

Symbolic AI uses symbols to represent knowledge and relationships between concepts. It produces more accurate results by assigning meanings to words based on context and embedded knowledge to disambiguate language. It is a highly demanding NLP technique where the algorithm summarizes a text briefly and that too in a fluent manner. It is a quick process as summarization helps in extracting all the valuable information without going through each word. This type of NLP algorithm combines the power of both symbolic and statistical algorithms to produce an effective result. By focusing on the main benefits and features, it can easily negate the maximum weakness of either approach, which is essential for high accuracy.

Relational semantics (semantics of individual sentences)

You often only have to type a few letters of a word, and the texting app will suggest the correct one for you. And the more you text, the more accurate it becomes, often recognizing commonly used words and names faster than you can type them. This example is useful to see how the lemmatization changes the sentence using its base form (e.g., the word “feet”" was changed to “foot”). That’s a lot to tackle at once, but by understanding each process and combing through the linked tutorials, you should be well on your way to a smooth and successful NLP application.

Through TFIDF frequent terms in the text are “rewarded” (like the word “they” in our example), but they also get “punished” if those terms are frequent in other texts we include in the algorithm too. On the contrary, this method highlights and “rewards” unique or rare terms considering all texts. It is a discipline that focuses on the interaction between data science and human language, and is scaling to lots of industries. Google Translate, Microsoft Translator, and Facebook Translation App are a few of the leading platforms for generic machine translation.

Evolution of natural language processing

The use of voice assistants is expected to continue to grow exponentially as they are used to control home security systems, thermostats, lights, and cars – even let what you’re running low on in the refrigerator. You can even customize lists of stopwords to include words that you want to ignore. You can mold your software to search for the keywords relevant to your needs – try it out with our sample keyword extractor. Named Entity Recognition, or NER (because we in the tech world are huge fans of our acronyms) is a Natural Language Processing technique that tags ‘named identities’ within text and extracts them for further analysis.

These assistants are a form of conversational AI that can carry on more sophisticated discussions. And if NLP is unable to resolve an issue, it can connect a customer with the appropriate personnel. Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way.

Must-know NLP algorithms for natural language processing

Read more about NLP Importance and Common Types here.