In general, 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.
Evolution of 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.
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.
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.
Key Areas of NLP where you can apply Machine 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 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.
Deep Learning has allowed us to to make progress on Abstractive Text Summarization. This is a sequence to sequence task 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.
Why is Natural Language Processing important?
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. Considering the staggering amount of unstructured data that’s generated every day, from medical records to social media, automation will be critical to fully analyze text and speech data efficiently.
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.
Natural Language Processing Online 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. Even though, they are only limited by how advanced the software is. What’s more, is that Google Translate has an API which 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 MicrosoftTranslator, which is a multilingual machine translation service. This API is integrated into a plethora of products both consumer level and enterprise level. 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 in 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.
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- Sage Publication: Journal of Information Science