What is RNN language model?
Recurrent Neural Net Language Model (RNNLM) is a type of neural net language models which contains the RNNs in the network. Since an RNN can deal with the variable length inputs, it is suitable for modeling the sequential data such as sentences in natural language.
What is a language representation model?
Language modeling (LM) is the use of various statistical and probabilistic techniques to determine the probability of a given sequence of words occurring in a sentence. Language models analyze bodies of text data to provide a basis for their word predictions.
How are n grams used in language technology?
N-grams of texts are extensively used in text mining and natural language processing tasks. They are basically a set of co-occurring words within a given window and when computing the n-grams you typically move one word forward (although you can move X words forward in more advanced scenarios).
What are language models enumerate the applications and various types of language models?
Language modeling is used in speech recognition, machine translation, part-of-speech tagging, parsing, Optical Character Recognition, handwriting recognition, information retrieval and other applications. In speech recognition, sounds are matched with word sequences.
What is the difference between recurrent neural networks and hidden Markov models?
Hidden Markov Models (HMMs) are much simpler than Recurrent Neural Networks (RNNs), and rely on strong assumptions which may not always be true. If the assumptions are true then you may see better performance from an HMM since it is less finicky to get working.
What is the difference between HMMs and neural networks?
HMMs can be used to predict hidden state, albeit only of the kind that the forward model is expecting. Neural networks can be used to predict a not yet observed state, e.g. future states for which predictors are available. This sort of state is not hidden in principle, it just hasn’t been observed yet. When would you use one rather than the other?
Is hidden Markov model a time series model?
Nevertheless they are proper time series models. Hidden Markov Models can be used to generate a language, that is, list elements from a family of strings.
What is the difference between learning in RNN and HMM?
The learning in HMM is much more complicated than that in RNN. In HMM it usually utilized the Baum-Welch algorithm (a special case of Expectation-Maximization algorithm) while in RNN it is usually the gradient descent. Which sequential input problems are best suited for each?