minor spelling tweaks for docs

docs/faqs.md

@@ -26,7 +26,7 @@ This allows to build vectors even for misspelled words or concatenation of words
 
 ## Why is the hierarchical softmax slightly worse in performance than the full softmax? 
 
-The hierachical softmax is an approximation of the full softmax loss that allows to train on large number of class efficiently. This is often at the cost of a few percent of accuracy. 
+The hierarchical softmax is an approximation of the full softmax loss that allows to train on large number of class efficiently. This is often at the cost of a few percent of accuracy.
 Note also that this loss is thought for classes that are unbalanced, that is some classes are more frequent than others. If your dataset has a balanced number of examples per class, it is worth trying the negative sampling loss (-loss ns -neg 100).
 However, negative sampling will still be very slow at test time, since the full softmax will be computed.
 

docs/options.md

@@ -17,8 +17,8 @@ The following arguments are mandatory:
   -verbose            verbosity level [2]
 
   The following arguments for the dictionary are optional:
-  -minCount           minimal number of word occurences [5]
-  -minCountLabel      minimal number of label occurences [0]
+  -minCount           minimal number of word occurrences [5]
+  -minCountLabel      minimal number of label occurrences [0]
   -wordNgrams         max length of word ngram [1]
   -bucket             number of buckets [2000000]
   -minn               min length of char ngram [3]

docs/supervised-tutorial.md

@@ -29,7 +29,7 @@ $ cd fastText-0.1.0
 $ make
 ```
 
-Running the binary without any argument will print the high level documentation, showing the different usecases supported by fastText:
+Running the binary without any argument will print the high level documentation, showing the different use cases supported by fastText:
 
 ```bash
 >> ./fasttext