A Complete Guide to Natural Language Processing (NLP)

Data is power. The more information you possess, the higher the chance you’ll be able to gain competitive advantage and find your place on the market. Knowing your customers better and finding new ways to meet your target audience’s needs and wants is the key to becoming successful in your niche and outpacing your rivals.

Today we are all aware of the ability of machines and computers to work with big data sets in the format of spreadsheets or database tables. Such information is easier to categorize and analyze as it comes in a structured format.

However, in the modern fast-paced environment, businesses are often swamped with an unprecedented amount of unstructured data that comes in the form of words, language flows, recordings, or raw texts. In such an informational overload, it is challenging for companies to cut through this noise and keep up with the leading trends on the market. Since such data is hard to analyze, many choose to refrain from this information, which is a big mistake as it can actually become a gold mine for your business!

So, instead of just ignoring this informational flow, why don’t you use natural language processing to extract meaningful insights from it? Read on to discover how to apply NLP software to benefit your business!

What is Natural Language Processing (NLP)?

The definition of NLP is simple and complex at the same time. Natural language processing is the discipline that exists at the intersection of linguistics and data science, which also correlates with a number of other fields.

Natural Language Processing can be defined as a subfield of artificial intelligence which leverages AI & ML tools, techniques, and algorithms to understand unstructured natural language data and derive meaning from it.

With significant advancements in information technologies and increases in computational power, NLP has seen its revival. The accessibility of data has provided practitioners with more opportunities to apply natural language processing technologies and extract insights for such industries as healthcare, fintech, banking, marketing, media, and others.

Steps in Natural Language Processing

Whether you’re a tech enthusiast trying to interpret the meaning of NLP or an entrepreneur searching for ways to boost your business, this guide is certainly for you. We will guide you through the process of natural language processing, outline its main steps, and expand on where to start if you want to get the best out of data science solutions.

If we run through the NLP basics, there are 7 basic steps you need to undertake to help your computer understand natural language:

• Sentence Segmentation
• Word Tokenization
• Text Lemmatization
• Stop Words
• Dependency Parsing in NLP
• Named Entity Recognition (NER)
• Coreference Resolution

Sentence Segmentation

The first step in natural language processing is to split sentences into separate objects. This stage is pretty easy. A smart AI algorithm screens the data sets and defines punctuation marks. Each time it notices a period, it considers the sentence finished and separates it from the whole text. This stage is important as it allows the NLP model to derive the meaning of the sentence and then get down to the analysis of the whole paragraph.

Breaking the text into sentences can be a piece of cake when data comes in a more or less structured format. However, the information might be presented without punctuation marks or lack other elements of the text. In such cases, data scientists apply complex techniques to identify meaningful parts.

Word Tokenization

Once you have your text broken into sentences, then it’s time to separate words and determine their parts of speech. In English, this is easy to do by identifying spaces between the words or tokens. Interestingly, punctuation marks are also considered separate tokens as they carry certain meanings and can change the whole idea of the text.

The next step in NLP is to look at each token separately and define its part of speech. AI algorithms analyze each word and apply a certain set of criteria to categorize it into adjectives, nouns, verbs, etc. This will help a machine understand the role of each token in the sentence or text.

For this purpose, a pretrained parts-of-speech classification model is used. This model has been trained by processing millions of English texts previously tagged and marked to provide the algorithms with essential data. It analyzes large data sets, which helps it to develop statistics that are further used to define which part of speech a word belongs to.

Text Lemmatization

Most texts and sentences contain root words as well as words with different grammatical forms. Natural language processing is used here to help the machine identify meaning and categorize these words. For instance, you might see the words “population” and “populated” in the same text. Although they belong to different parts of speech, the meaning of these words is quite similar.

NLP models are applied here to figure out the “lemma” of each token, which is the basic form of each word. This step helps an AI system understand the central concept of the text.

Stop Words

The next essential step in natural language processing is to identify stop words and filter them out before decoding the central meaning of the text. Each language has a number of linkers and “filler” words that do not add any extra meaning to the text, but they appear frequently in speech or in a casually written text.

Such objects might produce a kind of noise which will hinder an NLP system from deriving insights from the data. Thus, NLP pipelines usually mark these tokens as “stop words” and skip them when analyzing your text or any other piece of data.

Dependency Parsing in NLP

Dependency parsing is what data scientists do next in NLP. Their primary task at this stage is to discover the relations between all the words in a text. For this reason, NLP algorithms build a parse tree which defines the root word in the sentence and bridges the gap between other tokens. They may also define a parent word for each token to gain more insight and subsequently to understand the core concept.

Named Entity Recognition (NER)

When you have a ready-made parse tree, it is time to move on to Named Entity Recognition (NER). At this stage of natural language processing, data scientists start extracting ideas from the text by relating tokens to real-life objects.

If we look at our text examples, we can see that the words “Milan” and “Italy” refer to geographical places, while “Western Roman Empire” is the historical name. By extracting this information, an NLP model derives extra meaning from the text which can further be used to conduct thorough analysis.

Coreference Resolution

After we’ve finished Named Entity Recognition, we have plenty of information at our fingertips: we’ve split the text into sentences and words, derived their meaning, and even built relations between the main objects in the text.

However, we still have one obstacle that prevents our NLP model from a complete understanding of the natural language. Each language has many entities, such as pronouns and other parts of speech, that may relate to another word in the sentence and expand its meaning. Coreference resolution is performed to cluster all mentions in text which refer to a real-life concept or entity. Thus, an NLP model will understand what words like “he,” “its,” or “thus” refer to.

To better understand how coreference resolution functions, you can go to this Hugging Face resource and play with the texts a bit.

This was a complete overview of the natural language processing and the basic steps data scientists undertake to derive meaning from the text or any other piece of unstructured data. Once you feel you understand the process, let’s look into how you can apply NLP technologies in your niche!

Where is Natural Language Processing Applied?

Chatbots

Without NLP, chatbots won’t deliver any value to its users. It is a smart NLP model that allows the chatbot to understand your greeting and reply to you when you send a message. Businesses across all domains utilize chatbots to improve customer experience and analyze clients’ feedback.

Sentiment Analysis Software

Have you ever wondered how your customers feel when they use your service? That is exactly where sentiment analysis systems come in handy. This software is applied to interpret and classify emotions based on available text abstracts, comments, etc.

Marketing

Natural language processing has the potential to strengthen your marketing efforts and boost its efficiency. Starting with simple chatbots and moving to smart AI copywriters generating slogans, NLP models make the lives of marketers easier.

Banking

If you conduct quick research, you’ll find out that there are plenty of vendors selling NLP solutions to banks. AI software can help banking institutions mitigate risks, automate business processes, or check the quality of customer services.

Fake News Detection

The rapid rise in the popularity of social media platforms has not only fostered communication between various social groups but has also triggered the spread of fake news. NLP systems are frequently applied to detect fake information and provide statistics on its exposure.

Healthcare

Natural language processing has opened up a bunch of opportunities for healthcare providers. NLP models currently help medical workers process patient data, improve the quality of medical care, identify patients who need special care, and provide sufficient support to people with disabilities.

Launch Your Next NLP Project with LITSLINK!

Natural language processing has proven itself to be that breakthrough which many businesses have desired for years. With smart NLP models, you can get rid of tedious work, improve your customer service, and boost performance. Reach out to LITSLINK, and our team of experienced data scientists will analyze your request and come up with the best solution to empower your business!

Originally published 10 July, 2020 here.