What is Natural Language Processing (NLP)?

If we break down the term ‘ Natural Language Processing ’, what do we get? ‘Natural language’ and ‘processing’.

‘Natural language’ refers to the language that you and I use naturally. Not the one with perfect grammar that we use in academic essays, but the one we use in day-to-day life. It often includes sarcasm, slang and short forms. ‘Processing ‘means transforming or utilizing the input to produce an output.

When put together, Natural Language Processing (NLP)refers to the comprehension of natural human language with high regard to the intended meaning instead of the literal meaning.

You and I and every other human being on this planet are constantly using NLP to understand each other accurately. It’s the reason we are able to read between the lines and catch the undertone, although high-level sarcasm can be difficult to decode sometimes. Anyways, this was in terms of us, humans. But NLP can also be integrated into machines and software’s. Only because of this, AI chatbots like ChatGPT are able to comprehend your questions even with horribly wrong grammar.

That said, in this article, we’ll be discussing what NLP is in terms of machine learning, its working and examples, plus more…

What is Natural Language Processing (NLP)?

 

 

Image Credit: https://medium.com/@Coursesteach/natural-language-processing-part-1-5727b4efc8b4

Oracle describes NLP as “…a branch of artificial intelligence (AI) that enables computers to comprehend, generate, and manipulate human language.” It is the point where computer science, artificial intelligence and linguistics interact or overlap. And it’s not just limited to text (as in ChatGPT) but also includes speech (for example, Siri).

NLP further has two broad subsets – Natural Language Understanding (NLU) and Natural Language Generation (NLG). Natural language understanding is the comprehension aspect of NLP. It tries to figure out the intended meaning of each word and the sentences as a whole. It involves the conversion of unstructured data ( your input) into structured data which the machine can interpret easily.

Once this is done, the next step is to respond. This is done by natural language generation. NLG uses the structured data to produce a response (unstructured data) in natural human language.

If we see in terms of ChatGPT, NLU helps the software to comprehend your prompt and understand what is that you want it to do. NLG then puts together the required data in a way that seems human-written.

Benefits of Natural Language Processing (NLP)

NLP has opened a whole new possibility for machine learning and AI technology. The following are some advantages of NLP:

• Faster and large-scale analysis: NLP enables machines to analyze large amounts of data faster and more efficiently than conventional technologies. Its linguistic and AI capabilities allow it to comprehend complex data accurately, thus reducing errors and bias.
• Cost-effectiveness: Many tasks which were earlier performed manually can be automated using NLP software’s. It’s like an all-in-one technology that can perform tasks like data analysis, summarization, spam detection, translation, and much more without extra costs. Thus, it helps to save the cost of hiring various individuals or software’s for specific tasks.
• Faster and accurate data extraction: Because NLP can analyze huge amounts of data in less time, it can also navigate through all this data to extract a particular piece of data whenever needed.
• Improved customer experience: NLP when used in the customer care sector helps to resolve basic customer queries faster. Moreover, it provides 24/7 customer support and can even automatically transfer the query to a human agent if the query is too complex or specific.

Real-life examples of Natural Language Processing (NLP)

• Gmail: Gmail started with using NLP for spam filtration. NLP used a criteria that involved repetitive words, overly incorrect grammar, suspiciously urgent tone, explicit content, etc. to detect potentially spam emails. Now, it is additionally used for categorizing mails into 3 more labels: primary, social, and promotions. Predictive text when writing emails is also an NLP-based feature.
• Search engines: Search engines use NLP primarily for understanding search queries better and providing the most appropriate search results. This involves correcting typos, removing filler words, ranking search results, etc.
• Grammarly: Grammarly, the most famous writing tool, also uses NLP along with AI for correcting grammar and spelling, detecting tone and style, and offering alternative versions of the text.
• Language translation software’s: NLP allows software’s like Google Translate to comprehend the intended meaning instead of the literal meaning of the text. It also helps with producing grammatically correct output that conveys the message accurately.

How does NLP work

See NLP as a combination of several techniques and tools called NLP ‘tasks’, each giving NLP its various capabilities. But before these tools are utilized, there’s a preprocessing that NLP follows:

 Natural Language Processing(NLP) Preprocessing

♦ Tokenization

It is the process of breaking down any text into a number of smaller units called tokens. For example, if the sentence is ‘Emma is wearing a blue dress’, then during tokenization, it would be split into tokens – ‘Emma’, ‘is’, ‘wearing’, ‘a’, ‘blue’, and ‘dress’.

♦ Stemming & Lemmatization

These two processes occur together and have the same purpose, but differ in their procedure. Stemming removes common affixes (both prefixes and suffixes) from words to derive their base form. However, it may not always produce meaningful, or in technical terms, semantically correct base words. For example, it may consider ‘happi’ as the base word for ‘happier’.

On the other hand, lemmatization reduces the words to their correct base form that can be found in the dictionary.  For example, unlike stemming, it reduces ‘happier’ to ‘happy’.

♦ Stop word removal

Stop word removal removes all filler and unimportant words from the text like ‘the’, ‘is’, ‘of’, etc. This is done to help focus on more important and meaningful words from the text.

As a note – stemming, lemmatization and stop word removal can be combined into a single category called text normalization. The purpose of normalizing text is to make the input text consistent and uniform so it can be easily utilized by the NLP software.

 Natural Language Processing (NLP) tasks

♦ Part-of-speech tagging

Nouns, pronouns, verbs, adverbs, adjectives, etc. are what we call as parts of speech. They tell us about how a word functions within a sentence. Part-of-speech tagging is a technique used by NLP for tagging each word in the input text with a part of speech to better understand their meaning.

♦ Word sense disambiguation

NLP uses this technique to identify the correct meaning of a word with multiple meanings. For example, consider two sentences:

1. He sat on the bank of the river.
2. She deposited some money in the bank.

Both use the word ‘bank’ but in different contexts. Word-sense disambiguation identifies the first ‘bank’ as ‘riverside’ and the second one as a ‘financial institution’.

♦ Sentiment analysis

As the name suggests, sentiment analysis is about interpreting the sentiment or emotion behind a text. It can classify the text into positive, negative or neutral and even detect emotions. It’s mainly used in analyzing customer reviews and feedback.

♦ Machine translation

Machine translation involves translating text-based or speech-based data from one language to another while maintaining their original meaning. It requires the use of suitable words and correct grammar from the output language.

♦ Text generation

One of the most popular features of NLP is text generation. It’s used in generative AIs like ChatGPT and Google’s Gemini for generating a wide range of texts from poetry to blog articles and computer codes.

Named-entity recognition

This process works to classify names or nouns in a text into categories like people, location, dates, organizations, etc. For example, let’s take a sentence…

‘Michael gave his book to James’

Here, named-entity recognition classifies ‘Michael’ and ‘James’ as a person. Moreover, it also correctly links ‘his’ to ‘Michael’.

Challenges and limitations of Natural Language Processing(NLP)

• NLP relies heavily on the data it is trained on. If it was fed biased or incorrect data during training, it may produce such outputs later as well.
• Semantic analysis or the understanding of meanings is the strength as well as the limitation of NLP. Although it has high accuracy, it is still limited to the use of words whether text or audio. It cannot grasp alternative forms of communication like body language or voice modulation.
• NLP may misinterpret or fail to process highly complex inputs that are full of slang, sarcasm or ambiguity.

Conclusion

Natural language processing is no doubt, a game-changer in the field of AI and machine learning. From simple data analysis and automation, NLP has led machines into the complex arena of understanding human language along with its subtleties. By enabling computers to process and interpret text and speech as humans do, NLP has opened up a new possibility for communication between humans and machines. At this pace, it is possible that one day NLP-powered AI software will be able to breach its limitations and be able to empathize with humans on a deeper level.