Fix/doc improvements

docs/src/LM.md

@@ -1,16 +1,16 @@
-#  Statistical Language Model 
+#  Statistical Language Models
 
-**TextAnalysis** provide following different Language Models 
+**TextAnalysis** provides the following different language models: 
 
-- **MLE** - Base Ngram model.
-- **Lidstone** - Base Ngram model with Lidstone smoothing.
-- **Laplace** - Base Ngram language model with Laplace smoothing.
-- **WittenBellInterpolated** - Interpolated Version of witten-Bell algorithm.
-- **KneserNeyInterpolated** - Interpolated  version of Kneser -Ney smoothing.
+- **MLE** - Base n-gram model using Maximum Likelihood Estimation.
+- **Lidstone** - Base n-gram model with Lidstone smoothing.
+- **Laplace** - Base n-gram language model with Laplace smoothing.
+- **WittenBellInterpolated** - Interpolated version of the Witten-Bell algorithm.
+- **KneserNeyInterpolated** - Interpolated version of Kneser-Ney smoothing.
 
 ## APIs
 
-To use the API, we first *Instantiate* desired model and then load it with train set
+To use the API, first instantiate the desired model and then train it with a training set:
 
 ```julia
 MLE(word::Vector{T}, unk_cutoff=1, unk_label="<unk>") where { T <: AbstractString}
@@ -25,31 +25,31 @@ KneserNeyInterpolated(word::Vector{T}, discount:: Float64=0.1, unk_cutoff=1, unk
 
 (lm::<Languagemodel>)(text, min::Integer, max::Integer)
 ```
-Arguments:
+**Arguments:**
 
- * `word` : Array of  strings to store vocabulary.
+ * `word`: Array of strings to store the vocabulary.
 
  * `unk_cutoff`: Tokens with counts greater than or equal to the cutoff value will be considered part of the vocabulary.
 
- * `unk_label`: token for unknown labels 
+ * `unk_label`: Token for unknown labels.
 
- *  `gamma`: smoothing argument gamma 
+ * `gamma`: Smoothing parameter gamma.
 
- * `discount`:  discounting factor for `KneserNeyInterpolated`
+ * `discount`: Discounting factor for `KneserNeyInterpolated`.
 
-   for more information see docstrings of vocabulary
+For more information, see the docstrings of the vocabulary functions.
 
 ```julia
 julia> voc = ["my","name","is","salman","khan","and","he","is","shahrukh","Khan"]
 
 julia> train = ["khan","is","my","good", "friend","and","He","is","my","brother"]
-# voc and train are used to train vocabulary and model respectively
+# voc and train are used to train the vocabulary and model respectively
 
 julia> model = MLE(voc)
 MLE(Vocabulary(Dict("khan"=>1,"name"=>1,"<unk>"=>1,"salman"=>1,"is"=>2,"Khan"=>1,"my"=>1,"he"=>1,"shahrukh"=>1,"and"=>1…), 1, "<unk>", ["my", "name", "is", "salman", "khan", "and", "he", "is", "shahrukh", "Khan", "<unk>"]))
 
 julia> print(voc)
-11-element Array{String,1}:
+11-element Vector{String}:
  "my"
  "name"
  "is"
@@ -62,42 +62,41 @@ julia> print(voc)
  "Khan"
  "<unk>"
 
-# you can see "<unk>" token is added to voc 
-julia> fit = model(train,2,2) #considering only bigrams
+# You can see the "<unk>" token is added to voc 
+julia> fit = model(train,2,2) # considering only bigrams
 
-julia> unmaskedscore = score(model, fit, "is" ,"<unk>") #score output P(word | context) without replacing context word with "<unk>"
+julia> unmaskedscore = score(model, fit, "is" ,"<unk>") # score output P(word | context) without replacing context word with "<unk>"
 0.3333333333333333
 
 julia> masked_score = maskedscore(model,fit,"is","alien")
 0.3333333333333333
-#as expected maskedscore is equivalent to unmaskedscore with context replaced with "<unk>"
+# As expected, maskedscore is equivalent to unmaskedscore with context replaced with "<unk>"
 
 ```
 
 !!! note
 
-    When you call `MLE(voc)` for the first time, It will update your vocabulary set as well. 
+    When you call `MLE(voc)` for the first time, it will update your vocabulary set as well. 
 
-## Evaluation Method
+## Evaluation Methods
 
 ### `score` 
 
-used to evaluate the probability of word given context (*P(word | context)*)
+Used to evaluate the probability of a word given its context (*P(word | context)*):
 
 ```@docs
 score
 ```
 
-Arguments:
+**Arguments:**
 
-1. `m` : Instance of `Langmodel` struct.
-2. `temp_lm`: output of function call of instance of `Langmodel`.
-3. `word`: string of word 
-4. `context`: context of given word
+1. `m`: Instance of `Langmodel` struct.
+2. `temp_lm`: Output of function call of instance of `Langmodel`.
+3. `word`: String of the word.
+4. `context`: Context of the given word.
 
-- In case of `Lidstone` and `Laplace` it apply smoothing and, 
-
-- In Interpolated language model, provide `Kneserney` and `WittenBell` smoothing 
+- For `Lidstone` and `Laplace` models, smoothing is applied.
+- For interpolated language models, `KneserNey` and `WittenBell` smoothing are provided. 
 
 ### `maskedscore` 
 ```@docs
@@ -121,28 +120,28 @@ entropy
 perplexity
 ```
 
-##  Preprocessing
+## Preprocessing
 
- For Preprocessing following functions:
+The following functions are available for preprocessing:
 ```@docs
 everygram
 padding_ngram
 ```
 
 ## Vocabulary 
 
-Struct to store Language models vocabulary
+A struct to store language model vocabulary.
 
-checking membership and filters items by comparing their counts to a cutoff value
+It checks membership and filters items by comparing their counts to a cutoff value.
 
-It also Adds a special "unknown" tokens which unseen words are mapped to
+It also adds a special "unknown" token which unseen words are mapped to:
 
 ```@repl
 using TextAnalysis
 words = ["a", "c", "-", "d", "c", "a", "b", "r", "a", "c", "d"]
 vocabulary = Vocabulary(words, 2) 
 
-# lookup a sequence or words in the vocabulary
+# Look up a sequence of words in the vocabulary
 
 word = ["a", "-", "d", "c", "a"]
 

docs/src/classify.md

@@ -1,25 +1,25 @@
 # Classifier
 
-Text Analysis currently offers a Naive Bayes Classifier for text classification.
+TextAnalysis currently offers a Naive Bayes Classifier for text classification.
 
-To load the Naive Bayes Classifier, use the following command -
+To load the Naive Bayes Classifier, use the following command:
 
     using TextAnalysis: NaiveBayesClassifier, fit!, predict
 
 ## Basic Usage
 
-Its usage can be done in the following 3 steps.
+It can be used in the following 3 steps:
 
-1- Create an instance of the Naive Bayes Classifier model -
+1. Create an instance of the Naive Bayes Classifier model:
 ```@docs
 NaiveBayesClassifier
 ```
 
-2- Fitting the model weights on input -
+2. Fit the model weights on training data:
 ```@docs
 fit!
 ```
-3- Predicting for the input case -
+3. Make predictions on new data:
 ```@docs
 predict
 ```