If you have trained a chatbot yourself or you have read our previous posts about the topic for Dialogflow (former API.ai) and LUIS, you already know that creating a functioning chatbot is a long and tedious process. On top of the complexity, even if you fulfill an “appropriate” training, this does not ensure that you will obtain a chatbot able to understand various inputs from users.
As Amazon makes its first foray into the Asian market by releasing Amazon Echo in India and Japan, we can’t help but ask ourselves: Does Alexa work in India? What is the present and future of Chatbots and VAs?
Have you found any conversational bot in Facebook Messenger lately? While many companies are putting efforts in developing bots to communicate with their clients they seem to have forgotten that UX is the real key to gain people's loyalty.
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.
Usually in this blog we write about text analysis products such as lemmatizers or parsers and how they can help to solve issues in products that need an accurate understanding of text to function. But today, we want to show you also what is behind our technology, how we are able to create it. That is why we decided to interview one of our expert linguists, Clara Garcia, to provide some insights.