This is blog is simply a public account of my personal learning process, experimentation and self-discovery in developing ways to process human discourse to better understand human behavior in politics, financial markets. That said, this blog will likely be a collection of notes rather than a massive brain dump.
Wikipedia defines "Language Model" as " a probability distribution over sequences of words. Given such a sequence, say of length m , it assigns a probability to the whole sequence." The Stanford NLP Group similarly implies this definition through the description of the language modeling in the context of Information Retrieval . The equation above refers to the chain rule defined by: See chain-rule definition in the NLP Review of Basic Probability Theory . Generating a probability distribution is one part of building a usable language processing infrastructure. A useful statistical language model typically depends on the specific need, or problem you want to solve, and of course the domain of your problem. Thus the ability to cluster and partition sequences of words based on their likely occurrence given a query as input can serve as the starting point for connecting probability distri...
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