Commits on Mar 11, 2018

1. Improved the singularize method in inflect …

   Though 95% accuracy was previously achieved on measuring via CELEX
   English morphology word forms, the following changes have incremented
   the accuracy to 99%
   
   1. Added more words to the set singular_uninflected
   
   2. In the singularize method, changed the if condition for the set
      singular_uninflected from
         if x.endswith(w): return word
      to
         if x == w or w == x + "s": return x
      because the former statement considered the words in the set to be
      word endings. Hence, it affected words with prefix to the words in
      the set.
      The new condition checks if the word passed in the argument is
      present in the given list as it is or with a succeeding "s" and then
      returns the word's singular form from the list and not the word,
      which may be passed in a plural form.
   
   3. Added more words to the list singular_uncountable categorized via
      commenting such as abstract ideas and expressions, natural phenomena,
      general, etc for ease in reading and understanding
   
   4. Added more words to the list singular_ie and dicts singular_irregular
   
   5. Certain words which could be grouped via regex instead of adding in the
      above mentioned lists and dictionaries were written in the form of
      regular expressions (regex) in the singular_rules.
   
   6. In singularize method, changed the if condition for the dictionary
      singular_irregular from
         if w.endswith(x):
      to
         if x == w:
      because the former considered the word or key x in the dict to be an
      ending to the word passed as an argument to the singularize method.
      The latter condition checks whether the word w passed as argument is
      present in the dict by equating it to x. If True, it returns the
      singularized form of word w, that is, singular_irregular[x]
   
   7. Added more regex expressions to the list singular_rules to suit the
      singularization rules and improve accuracy for the singularize method
   
   8. Henceworth, this commit solves the following issues opened currently
      Issue - singularized on - earlier effect - current effect
      141 , 175   - flour     - flmy           - flour
      141         - colour    - colmy          - colour
      141         - your      - ymy            - your
      141         - olives    - olife          - olive
      176         - hummus    - hummu          - hummus
   
      [141](clips#141)
      [175](clips#175)
      [176](clips#176)
   
   9. The words added to sets singular_uninflected and singular_uncountable
      were also added to the lists in dict plural_categories["uninflected"]
      and plural_categories["uncountable"] for consistency.
   
   It is to keep in mind that the 99% accuracy is reported after being
   tested from the corpora/test_en.py and is subject to the dataset of CELEX
   English morphology word forms only.

   

   TanyaaCJain committed Mar 11, 2018
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