BEST N-GRAM MODELING FOR YOUR BUSINESS

At least there are 3 reasons why should you use this model:

1. The model based on n-previous words. This ensures that your next word prediction based on the previous words so it will keep following the context.

2. Using Katz back-off algorithm. This algorithm is used to ensure if the target sequence of words didn't find in the current n-gram model, it will search the n-1 model.

3. The simplest model with not to high difference performance. Another algorithm that we can use to predict the next word using previous sequence of words is RNN or LSTM algorithm. But it is complicated with no guarantee it can much outperform the n-gram model. The simpler is better.

As stated in the previous slide that this model is based on the previous sequence words. The question is, how to decide how many n previous sequence must be used and how to decide which next words must be suggested?

The answer is MLE (MAXIMUM LIKELIHOOD ESTIMATION). It is modeled as follow:

\[ P(y|x) = \frac{C(xy)}{C(x)} \]

The more sequence means the more strict rules, so balance between the n-previous sequence is done carefully in my model application.

To ensure that your company produces natural suggestion for your client, I use natural data from twitter, blog, and news to imitate human's natural typing behavior.

Our next goal is to create more personalized typing suggestion for each person. It will be free for you as a big update if you use our apps.

In order to give you the best service and to ensure that you keep being close to the technology around the market, we give you 5 years update warranty. So, what are you waiting for? Grab it fast!