Prediction

Counting n-grams is a common pre-processing step for computing sentence and word probabilities over a corpus. Thankfully, this task is embarrassingly parallel and is a natural fit for distributed processing frameworks like Cloud Dataflow. This article provides an implementation of n-gram counting using Cloud Dataflow that is able to efficiently compute n-grams in parallel over massive datasets. The Algorithm Cloud Dataflow uses a programming abstraction called PCollections which are collections of data that can be operated on in parallel (Parallel Collections). When programming for Cloud Dataflow you treat each operation as a transformation of a parallel collection that returns another parallel collection for further processing. This style of development is similar to the traditional Unix philosophy of piping the output of one command to another for further processing. ...

A common method of reducing the complexity of n-gram modeling is using the Markov Property. The Markov Property states that the probability of future states depends only on the present state, not on the sequence of events that preceded it. This concept can be elegantly implemented using a Markov Chain storing the probabilities of transitioning to a next state. ...

One of the most widely used methods natural language is n-gram modeling. This article explains what an n-gram model is, how it is computed, and what the probabilities of an n-gram model tell us. ...

The Google Prediction API offers the power of Google’s machine learning algorithms over a RESTful API interface. The machine learning algorithms themselves are a complete black box. As a user you upload the training data and, once it has been analyzed, start classifying new observations based on the analysis of the training data. I recently spent some time investigating how to use the API to determine the sentiment of a tweet. This article collects my thoughts on the experience and a few recommendations for future work. ...