As we have mentioned before in this blog, structured data is invaluable for businesses looking to extract relevant information from text. Whereas the problem used to be how to get enough useful data for the results to be meaningful, the challenge today is how to process the large amounts of it that are available. This task becomes almost impossible to achieve without the right tools because,on top of being vast, the data is most often unstructured. At Bitext, we offer a range of Text Analytics Tools that allow users to structure their raw data to extract the information most relevant to their goals.
An ontology is a data structure that groups entities in domains or types (for example the entities ‘dog’ and ‘cat’ are grouped under the type ‘animals’), and establishes relations between those entities. Its uses in Computational Linguistics are vast, one of the most interesting for us is the application of ontologies for chatbot training. When two humans communicate, they have a shared knowledge of the world that they presume in any spoken interaction. However, a chatbot lacks this indispensable knowledge. An ontology can help the chatbot discern that a person can walk a dog, but a dog walking a person is not something possible in our world.
If you’re building a chatbot, there’s a high chance you’ve sometimes thought “Why can’t people talk in a simpler, ordered way?”. Well, as linguistics experts, here at Bitext we have good and bad news.
The market for smart speakers is heating up: in 2014, Amazon quietly released the Echo, a 9-inch tall cylinder speaker controlled by a cloud-based voice assistant that goes by the name Alexa. Following the Amazon Echo’s popularity, Alphabet released the Google Home in late 2015. At the end of December 2017, Apple will also be joining the fray with the Siri-powered HomePod. And there is one more to come, Microsoft also announced Invoke, a Cortana-powered speaker (August 2017).