While consumer applications of chatbots are more talked about, enterprises see specific value in finely honed chatbots. Three famous examples of these programs are, Apple’s Siri, Google Assistant, and Amazon Alexa. The remainder of this post discusses deep learning applications in NLP that have made significant strides, some of their core challenges, and where they stand today.Ī chatbot is a computer program that simulates a human-like conversation with the user of the program. Word vectors or word embeddings as they are referred to are directly used as a statistical parameter input to the Neural Networks. The similarity between words in a given vector space translates to closeness in the meaning/context of the usage of words in natural language. This is a technique where words represent real-valued vectors in multi-dimensional space. A variation of RNNs, LSTMs are pushing the boundaries of statistical modeling in the NLP domain.Īnother significant breakthrough in the field of NLP deep learning is Word Embeddings. Certain flaws in RNNs such as vanishing gradients were rectified and this brought forth LSTMs (Long Short-Term Memory). RNN is the implementation of a statistical modeling technique that is now positively influencing the perception of NLP applications. Recurrent Neural Networks (RNN) open up possibilities with its ability to work with and memorize sequential data. Underwriting and New Business Enrollment.
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