Natural Language Processing (NLP)

Overview

Natural language processing (NLP) refers to the branch of computer science — and more specifically, the branch of artificial intelligence or AI — concerned with giving computers the ability to understand the text and spoken words in much the same way human beings can.

NLP 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 in the form of text or voice data and to ‘understand’ its full meaning, complete with the speaker or writer’s intent and sentiment.

Challenges

Everything we express (either verbally or in written) carries huge amounts of information. The topic we choose, our tone, our selection of words, everything adds some type of information that can be interpreted and value extracted from it. In theory, we can understand and even predict human behavior using that information.

But there is a problem: one person may generate hundreds or thousands of words in a declaration, each sentence with its corresponding complexity. If you want to scale and analyze several hundred, thousands, or millions of people or declarations in a given geography, then the situation is unmanageable.

Data generated from conversations, declarations, or even tweets are examples of unstructured data. Unstructured data doesn’t fit neatly into the traditional row and column structure of relational databases, and represent the vast majority of data available in the actual world. It is messy and hard to manipulate. Nevertheless, thanks to the advances in disciplines like machine learning a big revolution are going on regarding this topic. Nowadays it is no longer about trying to interpret a text or speech based on its keywords (the old-fashioned mechanical way), but about understanding the meaning behind those words (the cognitive way). This way it is possible to detect figures of speech like irony, or even perform sentiment analysis.

Use Cases

Natural language processing is the driving force behind machine intelligence in many modern real-world applications. Here are a few examples:

1. Machine translation: Google Translate is an example of widely available NLP technology at work. Truly useful machine translation involves more than replacing words in one language with words of another. Effective translation has to capture accurately the meaning and tone of the input language and translate it to text with the same meaning and desired impact in the output language. Machine translation tools are making good progress in terms of accuracy. A great way to test any machine translation tool is to translate text to one language and then back to the original.
2. Disease Prediction: NLP enables the recognition and prediction of diseases based on electronic health records and patient’s own speech. This capability is being explored in health conditions that go from cardiovascular diseases to depression and even schizophrenia. For example, Amazon Comprehend Medical is a service that uses NLP to extract disease conditions, medications, and treatment outcomes from patient notes, clinical trial reports, and other electronic health records.
3. Virtual assistants and chatbots: Virtual assistants such as Google Assistant, Apple’s Siri, and Amazon’s Alexa use speech recognition to recognize patterns in voice commands and natural language generation to respond with appropriate action or helpful comments. Chatbots perform the same magic in response to typed text entries. The best of these also learn to recognize contextual clues about human requests and use them to provide even better responses or options over time. The next enhancement for these applications is question answering, the ability to respond to our questions — anticipated or not — with relevant and helpful answers in their own words.
4. Sentiment analysis: NLP has become an essential business tool for uncovering hidden data insights from social media channels. Sentiment analysis can analyze language used in social media posts, responses, reviews, and more to extract attitudes and emotions in response to products, promotions, and events–information companies can use in product designs, advertising campaigns, and more.
5. Legal Assistance: LegalMation (Powered by IBM Watson NLP technology) developed a platform to automate routine litigation tasks and help legal teams save time, drive down costs and shift strategic focus.
6. Stock Market: Having an insight into what is happening and what people are talking about can be very valuable to financial traders. NLP is being used to track news, reports, comments about possible mergers between companies, everything can be then incorporated into a trading algorithm to generate massive profits. Remember: buy the rumor, sell the news.
7. Healthcare Industry: Companies like Winterlight Labs are making huge improvements in the treatment of Alzheimer’s disease by monitoring cognitive impairment through speech and they can also support clinical trials and studies for a wide range of central nervous system disorders. Following a similar approach, Stanford University developed Woebot, a chatbot therapist with the aim of helping people with anxiety and other disorders.

Future of NLP

NLP is particularly booming in the healthcare industry. This technology is improving care delivery, disease diagnosis, and bringing costs down while healthcare organizations are going through a growing adoption of electronic health records. The fact that clinical documentation can be improved means that patients can be better understood and benefited through better healthcare. The goal should be to optimize their experience, and several organizations are already working on this.

Although the future looks extremely challenging and full of threats for NLP, the discipline is developing at a very fast pace (probably like never before) and we are likely to reach a level of advancement in the coming years that will make complex applications look possible.