Merge pull request #361 from HLasse/docs-information-theory

docs: fix grammar in information theory docs

docs/information_theory.rst

@@ -6,14 +6,14 @@ from the text. These include:
 
 - `{doc/span}._.entropy`: the Shannon entropy of the text using the `token.prob` as the probability
   of each token. Entropy is defined as :math:`H(X) = -\sum_{i=1}^n p(x_i) \log_e p(x_i)`. Where :math:`p(x_i)` is the probability of the token :math:`x_i`.
-- `{doc/span}._.perplexity`: the perplexity of the text. perplexity is a measurement of how well a
+- `{doc/span}._.perplexity`: the perplexity of the text. Perplexity is a measurement of how well a
   probability distribution or probability model predicts a sample. Perplexity is defined as :math:`PPL(X) = e^{-H(X)}`, where :math:`H(X)` is the entropy of the text.
-- `{doc/span}._.per_word_perplexity`: The perplexity of the text, divided by the number of words. Can se considered the length normalized perplexity.
+- `{doc/span}._.per_word_perplexity`: The perplexity of the text, divided by the number of words. Can be considered the length-normalized perplexity.
 
-These information theoretic measures is for example often used to describe the complexity of a text. 
+These information theoretic measures are for example often used to describe the complexity of a text. 
 The higher the entropy, the more complex the text is.
-Similarly, one could imagine filtering text based on the per word perplexity given the assumption that
-highly surprising text is in fact non-coherent text pieces.
+Similarly, one could imagine filtering text based on the per-word perplexity given the assumption that
+highly surprising text are in fact non-coherent text pieces.
 
 .. note:: The information theory components require an available lexeme prop table from spaCy which is not available for all languages. A warning will be raised and values set to np.nan if the table cannot be found for the language.