The aim of natural language processing is to help computers come to terms with the complexities of human speech and natural text. With "NLP", all computers would have the ability to directly understand human language, and there are dozens of different NLP tasks being incorporated into software today, including:
Parsing and sentence segmentation: Natural language processing systems can analyze parts of a sentence to deliver better insights into the entire sentence.
Deep analytics: NLP systems can apply advanced processing techniques to extra specific information from multi-source data sets.
Named entity extraction: In a data mining process, a named entity can be discovered and analyzed by natural language processing systems.
Machine translation: NLP solutions are increasingly being used for translation programs, where one language is automatically translated into another (like when you use the Google translate feature)
Automatic summarization: Natural language processing can be used to create a summary of a huge piece of text.
As pioneers in the world of cloud computing, analytics, and artificial intelligence, Google has taken their own approach into the world of Natural Language Processing. The Natural Language service dissects the meaning and structure of text through the easy-to-use REST API machine learning models. You can use this solution to extract important information from text documents, articles, and academic texts, and even get to the bottom of customer sentiment.
With natural language processing, you can discover the context behind what people say on social media and determine intent from consumer conversations too. Essentially, Google Cloud Natural Language, or "Cloud NL", accesses the machine learning solutions used by the Google Assistant and Google Search, and delivers that power to you so that you can perform full sentiment and syntax analysis.
All you need to do to get started is work with a few simple lines of App script from the Google open network.
Like many experts in the technology space today, Google believes that machine learning will be a fundamental feature of transformation in the years to come. As such, they've spent a great deal of time and effort exploring the possibilities of AI. While Google might not be the only organization out there with an interest in what machine learning can do, they do have an advantage over some of the alternative options in the machine learning space.
Google has an astronomical amount of data under its belt. Decades of crawling the web have given the company an unprecedented amount of information through which it can train it's AI models. What's more, the move into video and voice that Google made means that it has one of the biggest datasets in speech and audio too.
Everything from Google's purchase of Deepmind, to its open-source strategy with TensorFlow prove how dedicated the cloud system is to leading the way with machine learning. Today, through the Natural Language system, you can access:
Entity Recognition: Identify entities like people, locations, products, and events by label.
Syntax Analysis: Extract sentences, parts of speech, and tokens from the text.
Sentiment Analysis: Discover the emotional context in a block of information.
Multi-language: Analyze text in a range of popular languages including French, Spanish, English, Italian, German, and Korean.
Content Classification: Classify each document using pre-set algorithms.
Integrated REST API: Use the REST API to integrate with cloud storage systems.
It's hard to over-estimate the potential benefits of natural language processing with a giant like Google. In the right circumstances, NLP can be harnessed to help brands improve the efficacy of their documentation solutions and identify useful information from huge databases. Overall, it leads to better performance and greater customer service solutions through:
The analysis of customer insights: Extract useful information from text about how your customers feel about your brand, and what they expect from your company.
Content classification: You can build relationship graphs examining how people in your network respond to different types of content, from news to articles.
Multi-lingual support: Reach out to people around the world combining the Google NLP API with Cloud Speech to extract insights from conversations. Then use the "Translation" API to translate the text.
Machine learning: The NLP API from Google uses the same technology in machine learning that powers the Google search solution, and the Google Assistant.
Although there's no one-size-fits-all model for natural language processing success, it's safe to say that the system can help companies to better understand their markets, their customers, and their position in their industry, through the easier extraction of actionable insights from big data.
It's an exciting time for companies to be involved in the world of Natural Language Processing. You don't have to be one of the most technologically savvy companies on the market to understand the potential of machine learning. Today, there are a multitude of innovators and organizations in the marketplace who are beginning to discover new ways of powering growth through AI solutions and big data technology.
When used correctly, the right information and the right Google cloud strategies can help you to get that all-important edge in your chosen industry. What's more, since the global NLP market is expected to see a value of about $16 billion by 2021, it's safe to say that the tech giants around the world are going to continue delivering new and exciting opportunities in this area.
While there's always an option to build your own machine learning models if you have the capacity to do so, Google makes tapping into NLP and other useful big data easier through a range of machine learning APIs that are already trained to deliver the results that your company needs. To learn more about Google APIs, NLP, and what you can do to give your business a machine learning edge, reach out to Coolhead Tech today!