An N-gram is a sequence of N words: a 2-gram (or bigram) is a two-word sequence of words like “lütfen ödevinizi”, “ödevinizi çabuk”, or ”çabuk veriniz”, and a 3-gram (or trigram) is a three-word sequence of words like “lütfen ödevinizi çabuk”, or “ödevinizi çabuk veriniz”.
To keep a language model from assigning zero probability to unseen events, we’ll have to shave off a bit of probability mass from some more frequent events and give it to the events we’ve never seen. This modification is called smoothing or discounting.
The simplest way to do smoothing is to add one to all the bigram counts, before we normalize them into probabilities. All the counts that used to be zero will now have a count of 1, the counts of 1 will be 2, and so on. This algorithm is called Laplace smoothing.
One alternative to add-one smoothing is to move a bit less of the probability mass from the seen to the unseen events. Instead of adding 1 to each count, we add a fractional count k. This algorithm is therefore called add-k smoothing.
Install the latest version of Git.
In order to work on code, create a fork from GitHub page. Use Git for cloning the code to your local or below line for Ubuntu:
git clone <your-fork-git-link>
A directory called NGram-Swift will be created. Or you can use below link for exploring the code:
git clone https://github.com/starlangsoftware/NGram-Swift.git
To import projects from Git with version control:
XCode IDE, select Clone an Existing Project.
In the Import window, paste github URL.
Result: The imported project is listed in the Project Explorer view and files are loaded.
After being done with the downloading and opening project, select Build option from Product menu. After compilation process, user can run NGram-Swift.
To create an empty NGram model:
a = NGram(N: 2)
this creates an empty NGram model.
To add an sentence to NGram
func addNGramSentence(symbols: [Symbol], sentenceCount: Int = 1)
var text1: [String] = ["<s>", "ali", "topu", "at", "mehmet", "ayşeye", "gitti", "</s>"] var text2: [String] = ["<s>", "ali", "top", "at", "ayşe", "eve", "gitti", "</s>"] nGram = NGram(N: 2) nGram.addNGramSentence(text1) nGram.addNGramSentence(text2)
with the lines above, an empty NGram model is created and the sentences text1 and text2 are added to the bigram model.
Another possibility is to create an Ngram from a corpus consisting of two dimensional String array such as
var simpleCorpus : [[String]] = ... nGram = NGram(N: 1, corpus: simpleCorpus)
NoSmoothing class is the simplest technique for smoothing. It doesn't require training. Only probabilities are calculated using counters. For example, to calculate the probabilities of a given NGram model using NoSmoothing:
let simpleSmoothing = NoSmoothing<String>() a.calculateNGramProbabilitiesSimple(simpleSmoothing: simpleSmoothing)
LaplaceSmoothing class is a simple smoothing technique for smoothing. It doesn't require training. Probabilities are calculated adding 1 to each counter. For example, to calculate the probabilities of a given NGram model using LaplaceSmoothing:
let simpleSmoothing = LaplaceSmoothing<String>() a.calculateNGramProbabilitiesSimple(simpleSmoothing: simpleSmoothing)
GoodTuringSmoothing class is a complex smoothing technique that doesn't require training. To calculate the probabilities of a given NGram model using GoodTuringSmoothing:
let simpleSmoothing = GoodTuringSmoothing<String>() a.calculateNGramProbabilitiesSimple(simpleSmoothing: simpleSmoothing)
AdditiveSmoothing class is a smoothing technique that requires training.
var validationCorpus : [[String]] = ... let additiveSmoothing = AdditiveSmoothing<String>() a.calculateNGramProbabilitiesTrained(corpus: validationCorpus, trainedSmoothing: additiveSmoothing)
To find the probability of an NGram:
func getProbability(_ args: Symbol...) -> Double
For example, to find the bigram probability:
To find the trigram probability:
a.getProbability("jack", "reads", "books")