Machine Learning Use-Cases in Human Language Processing

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Machine Learning Use-Cases in Human Language Processing

Advanced Technologies

Over the past few years, we’ve seen amazing improvements in machine learning applied to natural languages. New applications have emerged, and some of them are likely to change how humans communicate with each other and with their computers.

In our previous blog post, we described how machines learn to “understand” and use natural language.

Here is a shortlist of some of the most impactful real-life uses for language processing.

Machine learning-powered question answering

Personal assistants like Siri, Cortana, or Alexa are by now famous for being able to take commands and answer general questions in natural language. But there are other, more focused applications for language processing, as well.

At Tremend we have been working on an AI-powered solution that helps tech support staff find answers faster. It works like an automated assistant that “reads” chat conversations to understand the problem and suggest a possible way to solve it.

The solution is built using Keras, a high-level neural networks API, on top of TensorFlow, an open-source software library for numerical computation using data flow graphs. The system has a long short-term memory (LSTM) architecture – a recurrent neural network (RNN) that builds context correlation over several iterations. The LSTM approach allows forward and backward connections between neurons.

In practice, it means that the backend AI keeps adding input to the machine whenever the client gives more details about the problem, and whenever the support operator asks questions. As the conversation continues, the context used by the AI becomes richer, allowing for greater precision in pointing towards a solution.

This is not far from a full-fledged chatbot, a topic that we have also covered in a recent post. And it is also close to another fascinating application of NLP – speech analytics.

Machine Translation

Translation based on machine learning has come a long way since 2005 when Altavista would translate “The spirit is willing, but the flesh is weak” into Russian as “The vodka is excellent but the meat is lousy”. There are now several devices and apps specialized in translating plain language.

Recently, Google launched the Pixel Buds, and before that, Microsoft’s Skype was capable of translating live conversations between 8 languages and written chats between 50 languages. The Dash Pro earphones too promise to translate between 40 languages, based on the iTranslate Android app.

As the AI behind the solution becomes more powerful, machine translation is becoming mainstream. Professional translators may be skeptical about it, but the day when businesses will also switch to computer translation is coming closer. Next step for machines: answering questions.

Speech Analytics

There is a growing buzz around the use of analytics to measure emotional responses. AI has made it possible to correlate the use of language with the human voice, so as to find patterns and try to understand the speaker’s emotions.

When people speak, their language is not as strictly filtered as when using email or social media statuses. AI-backed speech analytics can pinpoint spontaneous emotions, by identifying specific words during conversations. Add voice tone recognition and facial expression analysis and you get impressive machines that read a wide range of human emotions.

Applications of speech analytics are diverse, ranging from call-center message optimization to marketing and health tracking. Not to mention the new level of understanding between man and machine. This brings us to talking interfaces.

Natural language interfaces to computer systems

In a previous blog post about computer interfaces, we quoted a UX expert as saying that “the best interface is no interface”. Talking to computers directly and hearing them reply in human language fits that description quite well. Siri, Cortana, and Alexa are the most prominent examples. For the time being, they can answer questions and integrate with smart home applications. In time, however, they will play a bigger role in controlling computer systems, as more objects around us become smart and connected.

Machine learning is getting better at taking natural language into new digital areas. YouTube and Facebook’s AI systems generate automatic captions for pictures and videos. Semantic search improves results by understanding intent and context, not just keywords. Chatbots are the new rage in brand-customer communication. And smart-talk gets a whole new meaning.

For 12 years Tremend has successfully delivered end-to-end solutions ranging from complex banking platforms to mobile, eCommerce solutions, and embedded software. We use advanced technologies like Artificial Intelligence, machine learning, IoT, blockchain, and microservices for clients from industries such as banking, finance, telecom, and automotive.

Feel free to contact us for support in developing your own software projects.