Natural Language Processing | What It Is & Why It Matters

Natural Language Processing (NLP) is a branch of artificial intelligence. Especially that helps computers understand, interpret and manipulate human language.

In addition, Natural Language Processing draws from many disciplines. Including computer science and computational linguistics, in its pursuit to fill the gap between human communication and computer understanding.

While natural language processing isn’t a new science, technology is rapidly advancing. Thanks to an increased interest in human-to-machine communications. Plus the availability of big data, powerful computing, and enhanced algorithms.

Natural Language Processing

As a human, you may speak and write in English, Spanish or Chinese. But a computer’s native language – known as machine code or machine language – is largely incomprehensible to most people.

At your device’s lowest levels, communication occurs not with words but through millions of zeros and ones that produce logical actions.

How did Natural Language Processing evolve?

Indeed, programmers used punch cards to communicate with the first computers 70 years ago. This manual and arduous process were understood by a relatively small number of people.

Now you can say, “Alexa, I like this song,” and a device playing music in your home will lower the volume and reply, “OK. Rating saved,” in a human-like voice. Then it adapts its algorithm to play that song – and others like it – the next time you listen to that music station.

Natural Language Processing

Let’s take a closer look at that interaction. Your device activated when it heard you speak, understood the unspoken intent in the comment, executed an action and provided feedback in a well-formed English sentence, all in the space of about five seconds.

The complete interaction was made possible by NLP, along with other AI elements such as machine learning and deep learning. I have built many applications that solve practical NLP problems through the use of machine learning.

In this blog, I talk about a few of them:

Text Classification

Text Classification is the task of assigning a label to a piece of text which would be a single sentence or a document.

Some common examples include sentiment classification, predicting user rating, detecting if an email is spam or not. Deep Learning has made it easy to use RNNs and CNNs for Text Classification.

I did a mini-project using IMDB reviews to classify a review into either a positive or negative review. I used a Multi-Layered RNN to classify reviews with an accuracy of 83%. For more detail, please see my affiliate Github link here.

Besides RNN, CNNs can also be used for text classification. This blog explains it beautifully. I built a CNN for classifying financial services customer complaints into different departments and presented this at Google NEXT Conference.

Text Summarization

Deep Learning has allowed us to make progress on Abstractive Text Summarization. This is a sequence to sequence tasks where input and output sequences can be of different lengths.

I have built a Pointer Generator Model to do summarizations. The model gets a Rouge Score of 0.39 which is state of the art for Abstractive Summarization. For more information, please check out our affiliate Blog here. 

Question Answer Model

NLP can also help find answers to question in a paragraph! SQUAD data set has made this possible. In fact, some of the latest models are able to get to human-level accuracy in answering questions from a context.

How does NLP work?

Natural language processing includes many different techniques for interpreting human language, ranging from statistical and machine learning methods to rules-based and algorithmic approaches.

We need a broad array of approaches because the text- and voice-based data varies widely, as do the practical applications.

Basic NLP tasks include tokenization and parsing, lemmatization/stemming, part-of-speech tagging, language detection and identification of semantic relationships. If you ever diagrammed sentences in grade school, you’ve done these tasks manually before.

In general terms, NLP tasks break down language into shorter, elemental pieces, try to understand relationships between the pieces and explore how the pieces work together to create meaning.

These underlying tasks are often used in higher-level NLP capabilities, such as:
  • Content categorization. A linguistic-based document summary, including search and indexing, content alerts and duplication detection.
  • Topic discovery and modeling. Accurately capture the meaning and themes in text collections, and apply advanced analytics to text, like optimization and forecasting.
  • Contextual extraction. Automatically pull structured information from text-based sources.
  • Sentiment analysis. Identifying the mood or subjective opinions within large amounts of text, including average sentiment and opinion mining.
  • Speech-to-text and text-to-speech conversion. Transforming voice commands into written text, and vice versa.
  • Document summarization. Automatically generating synopses of large bodies of text.
  • Machine translation. Automatic translation of text or speech from one language to another.

NLP is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.

NLP and text analytics

Natural language processing goes hand in hand with text analytics, which counts, groups and categorizes words to extract structure and meaning from large volumes of content.

Text analytics is used to explore textual content and derive new variables from raw text that may be visualized, filtered, or used as inputs to predictive models or other statistical methods.

NLP and text analytics are used together for many applications, including:

  • Investigative discovery. Identify patterns and clues in emails or written reports to help detect and solve crimes.
  • Subject-matter expertise. Classify content into meaningful topics so you can take action and discover trends.
  • Social media analytics. Track awareness and sentiment about specific topics and identify key influencers.

Everyday NLP examples 

There are many common and practical applications of NLP in our everyday lives. Beyond conversing with virtual assistants like Alexa or Siri, here are a few more examples:

  • Have you ever looked at the emails in your spam folder and noticed similarities in the subject lines? You’re seeing Bayesian spam filtering, a statistical NLP technique that compares the words in spam to valid emails to identify junk mail.
  • Have you ever missed a phone call and read the automatic transcript of the voicemail in your email inbox or smartphone app? That’s speech-to-text conversion, an NLP capability.
  • Have you ever navigated a website by using its built-in search bar, or by selecting suggested topic, entity or category tags? Then you’ve used NLP methods for search, topic modeling, entity extraction, and content categorization.

Here are the benefits of NLP;

Large volumes of textual data via Natural Language Processing

Natural language processing helps computers communicate with humans in their own language and scales other language-related tasks.

For example, NLP makes it possible for computers to read a text, hear speech, interpret it, measure sentiment and determine which parts are important.

Today’s machines can analyze more language-based data than humans, without fatigue and in a consistent, unbiased way.

Structuring a highly unstructured data source

Human language is astoundingly complex and diverse. We express ourselves in infinite ways, both verbally and in writing. Not only are there hundreds of languages and dialects, but within each language is a unique set of grammar and syntax rules, terms and slang.

When we write, we often misspell or abbreviate words, or omit punctuation. When we speak, we have regional accents, and we mumble, stutter and borrow terms from other languages.

While supervised and unsupervised learning, and specifically deep learning, are now widely used. Especially for modeling human language, there’s also a need for syntactic and semantic understanding and domain expertise that are not necessarily present in these machine learning approaches.

NLP is important because it helps resolve ambiguity in language and adds useful numeric structure to the data for many downstream applications, such as speech recognition or text analytics.

Examples of Natural Language Processing Tools

With the advent of the internet, we are more connected than ever before possible. But with people speaking thousands of different languages, there definitely exists a language barrier during communication.

Dictionaries exist but they are too much of a hassle to use. While during a conversation and people wanted foreign words to be translated instantly. For this reason, many translation tools exist to circumvent this problem.

But which are the best tools for translation out there?

1. Google Translate

Google is one of the biggest companies in the world and they are undoubtedly the pioneers of the free online translation tool with Google Translate.

You can use this tool with the web interface or the mobile application version which exists for both IOS and Android. It is now possible to always have a translator wherever you go.

What’s more, is that Google Translate has an API that can be used by developers. In general, to integrate it into their applications and browser extensions.

The team at Google unveiled Translate in April 2006 to the public and used transcript data from the European Parliament and the United Nations for its backend database. It works by first converting the foreign language into English and then to the requested language.

It has a simple interface and is very easy to use which makes it one of the most popular options when it comes to online translating tools.

2. Bing Translator

After the behemoth that is Google Translate, we have Bing Translate which is powered by Microsoft Translator, which is a multilingual machine translation service.

You can find it on products from Yammer, Skype, Apple, and Android apart from Microsoft. You can find this translator in both a free and paid version.

What is interesting about this translator is the fact that it is data-driven. Not forgetting, this means that developers didn’t write strict rules for translation. Instead, the system trains the algorithm to understand how parallel texts were translated when it comes to natural languages.

This lets the translator learn how to translate text from completely new languages much quicker.

3. Day Translator

The Day Translator was developed by Day Translation Inc. Not to mention that it is a free, easy-to-use translator with a similar UI to both Google and Microsoft’s offerings.

But the problem with those translators is that, though fast they are not as accurate as they should be. Further, the algorithms are still learning. And it will take a while for them to get as good as a human translator.

Day Translator, on the other hand, uses a database that was compiled by professional human translators in the past. This makes them much more capable of understanding the context and nuance of a language and giving you effective translations.

It is also free to use and currently supports 76 languages which makes it highly appealing.

Takeaway,

A subfield of NLP called Natural Language Understanding (NLU) has begun to rise in popularity because of its potential in cognitive and AI applications.

NLU goes beyond the structural understanding of language to interpret intent, resolve context and word ambiguity, and even generate well-formed human language on its own. Also, NLU algorithms must tackle the extremely complex problem of semantic interpretation.

The evolution of NLP toward NLU has a lot of important implications for businesses and consumers alike. Imagine the power of an algorithm that can understand the meaning and nuance of human language in many contexts. From medicine to law to the classroom.

As the volumes of unstructured information continue to grow exponentially, we will benefit from computers’ tireless ability to help us make sense of it all.

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