When reading an article, we humans are able to easily identify the topics the article talks about. An interesting question is: can we automate this process, i.e., train a machine to find out the underlying topics in articles? In this post, a very popular topic modeling method, Latent Dirichlet ...

In a Markov model, we generally assume that the states are directly observable or one state corresponds to one observation/event only. However, this is not always true. A good example would be: in speech recognition, we are supposed to identify a sequence of words given a sequence of utterances ...

There has been many occasions that we have incomplete sentences that are needed to completed. One example is that in speech recognition noisy environment can lead to unrecognizable words, but we still hope to recover and understand the complete sentence (e.g., by inference); another example is sentence completion questions ...