How Natural Language Processing (NLP) Helps In Communication

While Natural Language Processing (NLP) 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. Of course, yes! As a human, you may speak and write in English, Spanish, or Chinese, but a computer’s language is native.

This means, that the 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. Indeed, before the rise of NLP, programmers used what is known as punch cards.

More so, in order to communicate with the first computers 70 years ago. This manual and arduous process was 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.

And then, with time, it adapts its algorithm to play that song – and others like it – the next time you listen to that music station. Let’s consider 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 like Machine Learning (ML), Deep Learning, and the like. So, what role does Natural Language Processing (NLP) play in all this, and how does it work?

Understanding The Natural Language Processing (NLP) Basics

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that helps computers understand, interpret and manipulate human language. In addition, NLP draws from many disciplines including computer science and computational linguistics. Perse, in its pursuit, fills the gap between human communication and computer understanding.

NLP is concerned with giving computers the ability to understand the text and spoken words in much the same way human beings can. Eventually, it combines computational linguistics — rule-based modeling of human language — with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language.

Particularly, in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly — even in real time. There’s a good chance you’ve interacted with NLP.

More so, in the form of voice-operated GPS systems, digital assistants, speech-to-text dictation software, customer service chatbots, and other consumer conveniences. But, NLP also plays a growing role in enterprise solutions that help streamline business operations, increase employee productivity, and simplify mission-critical business processes.

Where Natural Language Processing (NLP) Matters The Most

To begin with, human language is often filled with ambiguities that make it incredibly difficult to write software — that accurately determines the intended meaning of text or voice data. For instance, we may consider things like homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage of exceptions, and variations in sentence structure.

Well, these are just a few of the irregularities of human language that take humans years to learn. For such and other reasons, these are some of the items on the list that programmers must teach natural language-driven applications day in and day out. Specifically, so as to recognize/understand accurately from the start, if those applications are going to be useful.

With that in mind, there are many common and practical applications of Natural Language Processing (NLP) in our everyday lives. Beyond conversing with virtual assistants like Alexa or Siri, there are a few more examples to note.

Everyday Natural Language Processing (NLP) 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.

In a nutshell, 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, and more elemental pieces.

In particular, so as to try and understand the relationships between the pieces, and explore how the pieces work together to create meaning.

There are some key underlying tasks that are often used in higher-level NLP capabilities.

Consider the following:
  • 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.

As professional webmasters, we have built many web-based applications that solve practical NLP problems through the use of machine learning. In the next section, we are going to 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 ratings, and detecting if an email is spam or not. Deep Learning has made it easy to use RNNs and CNNs for Text Classification.

Be that as it may, together with our partners, we did a mini-project using IMDB reviews to classify a review into either a positive or negative review. We used a Multi-Layered RNN to classify reviews with an accuracy of 83%. For more detail, please see more from the GitHub link here to learn more. Besides RNN, CNNs can also be used for text classification.

This blog explains it beautifully. We also 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.

We 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 this affiliate blog here for more details.

FAQ Answers

NLP can also help find answers to questions 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.

All in all, 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.

Content 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. Or even, in terms of subject-matter expertise so as to classify content into meaningful topics that you can take action on and discover new trends.

As well as in your overall social media analytics strategy as you try to track awareness and sentiment about specific topics and identify key influencers.

The Main Natural Language Processing (NLP) Benefits 

The first and obvious one is handling large volumes of textual data. Whereby, 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. Moving on, the second benefit is structuring a highly unstructured data source. Keep in mind, that 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, 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.

The Key Toolkits That Help Drive NLP Ahead

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 pioneer 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, which 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 more quickly.

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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 Notes:

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. We hope the above-revised guide on Natural Language Processing was helpful to you. But, if you’ll have additional contributions or even questions, please Contact Us and let us know.

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