Deep Text: Facebook's Effort to Better Understand Textual Content

Facebook this week pulled the curtains off Deep Text, its new deep-learning-based text-understanding engine, which it said can analyze the textual content in several-thousand posts per second. The social network said in a blog post by engineer Ahmad Abdulkader, applied machine learning platform team technical program manager Aparna Lakshmiratan and research scientist Joy Zhang that the aim of Deep Text is to better understand text across Facebook and to reduce reliance on language-dependent knowledge, adding that the technology is already being tested on Messenger and offering the example that Deep Text can decide whether a user is looking for a taxi by distinguishing between "I just came out of the taxi" and "I need a ride." Text understanding includes multiple tasks, such as general classification to determine what a post is about--basketball, for example--and recognition of entities, like the names of players, stats from a game and other meaningful information. But to get closer to how humans understand text, we need to teach the computer to understand things like slang and word-sense disambiguation. As an example, if someone says, "I like blackberry," does that mean the fruit or the device?