forked from mrlan/EnglishPal
combine_words_through_grammar replaced with stem_words function using nltk stemmer
parent
d6adc70d4f
commit
16ab48d162
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@ -7,6 +7,7 @@
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import pickle
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import math
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from nltk.stem import WordNetLemmatizer
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from wordfreqCMD import remove_punctuation, freq, sort_in_descending_order, sort_in_ascending_order
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@ -75,27 +76,11 @@ def revert_dict(d):
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return d2
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def combine_words_through_grammar(lst,d): #通过语法合并同一单词的不同形式
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lst1=lst
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for index,word in enumerate(lst):
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change_word=''
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if word.endswith('ies'): #语法条件匹配
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change_word=word[:-3]+'y'
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elif word.endswith('es'):
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change_word=word[:-2]
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elif word.endswith('s'):
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change_word=word[:-1]
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elif word.endswith('ed'):
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change_word=word[:-2]
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elif word.endswith('en'):
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change_word=word[:-2] + 'an'
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else:
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pass
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for word2 in d:
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if change_word==word2:
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lst1[index]=change_word
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break
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return lst1
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def stem_words(list_of_words): # It reduces words to the root word (eg. ate, eaten -> eat; leaves, leaf -> leaf)
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wnl = WordNetLemmatizer()
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lst1 = [wnl.lemmatize(w) for w in list_of_words]
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return [wnl.lemmatize(w, pos='v') for w in lst1] # stem by verb: 'v' represents verb
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def user_difficulty_level(d_user, d):
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d_user2 = revert_dict(d_user) # key is date, and value is a list of words added in that date
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@ -104,7 +89,7 @@ def user_difficulty_level(d_user, d):
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for date in sorted(d_user2.keys(), reverse=True): # most recently added words are more important while determining user's level
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lst = d_user2[date] # a list of words
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#print(lst)
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lst=combine_words_through_grammar(lst,d) #合并单词的不同形式
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lst = stem_words(lst) # this call returns a list of words reduced to root word
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#print(lst)
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lst2 = [] # a list of tuples, (word, difficulty level)
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for word in lst:
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@ -116,7 +101,7 @@ def user_difficulty_level(d_user, d):
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for t in lst3:
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word = t[0]
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hard = t[1]
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#print('WORD %s HARD %4.2f' % (word, hard))
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print('WORD %s HARD %4.2f' % (word, hard))
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geometric = geometric * (hard)
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count += 1
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if count >= 10:
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