Some small tweaks and conformance things

pymathics/natlang/linguistic_data.py

@@ -2,7 +2,7 @@
 """
 Linguistic Data
 
-See <url>:WMA link:https://reference.wolfram.com/language/guide/LinguisticData.html</url> guide.
+See the corresponding <url>:WMA:https://reference.wolfram.com/language/guide/LinguisticData.html</url> guide.
 
 """
 # This module uses nltk.
@@ -66,7 +66,7 @@ class DictionaryLookup(_WordListBuiltin):
  = {baker, baker's dozen, baker's eczema}
  """
 
- summary_text = "Lookup words matching a pattern in a dictionary"
+ summary_text = "lookup words matching a pattern in our word dictionary"
 
  def compile(self, pattern, evaluation):
  re_patt = to_regex(pattern, evaluation)
@@ -131,7 +131,7 @@ class DictionaryWordQ(_WordNetBuiltin):
  = False
  """
 
- summary_text = "Check if a word is in the dictionary"
+ summary_text = "check if a word is in our word dictionary"
 
  def eval(self, word, evaluation: Evaluation, options: dict):
  "DictionaryWordQ[word_String, OptionsPattern[DictionaryWordQ]]"
@@ -170,7 +170,7 @@ class RandomWord(_WordListBuiltin):
  </dl>
  """
 
- summary_text = "generate a random word of a given kind"
+ summary_text = "generate a random word"
 
  def _random_words(self, type, n, evaluation: Evaluation, options: dict):
  words = self._words(self._language_name(evaluation, options), type, evaluation)
@@ -412,7 +412,7 @@ class WordList(_WordListBuiltin):
  Evaluate the average length over all the words in the dictionary:
  >> N[Mean[StringLength /@ WordList[]], 3]
  = 11.6
- Now, restricted to adjetives:
+ Now, restricted to adjectives:
  >> N[Mean[StringLength /@ WordList["Adjective"]], 2]
  = 9.3
  """

pymathics/natlang/nltk.py

@@ -13,6 +13,7 @@
 from mathics.core.evaluation import Evaluation
 from mathics.core.symbols import strip_context
 
+# Don't consider this for user documentation
 no_doc = True
 
 

pymathics/natlang/normalization.py

@@ -2,7 +2,9 @@
 
 Text Normalization
 
-See <url>:WMA link: https://reference.wolfram.com/language/guide/TextNormalization.html</url> guide.
+See the corresponding <url>
+:WMA:
+https://reference.wolfram.com/language/guide/TextNormalization.html</url> guide.
 
 
 This module uses spacy as a backend.

pymathics/natlang/textual_analysis.py

@@ -2,7 +2,8 @@
 """
 Text Analysis
 
-<url>:See WMA guide:https://reference.wolfram.com/language/guide/TextAnalysis.html</url>
+See the corresponding <url>:WMA:
+https://reference.wolfram.com/language/guide/TextAnalysis.html</url> guide.
 """
 
 # This module uses both enchant, nltk and spacy. Maybe we want to split this further.
@@ -88,7 +89,7 @@ class SpellingCorrectionList(Builtin):
 
  _dictionaries = {}
 
- summary_text = "Look for spelling correction candidates of a word"
+ summary_text = "look for spelling correction candidates of a word"
 
  def eval(
  self, word: String, evaluation: Evaluation, options: dict
@@ -130,7 +131,7 @@ class WordCount(_SpacyBuiltin):
  = 4
  """
 
- summary_text = "Count the words in a text"
+ summary_text = "count words in a text"
 
  def eval(self, text, evaluation: Evaluation, options: dict):
  "WordCount[text_String, OptionsPattern[WordCount]]"
@@ -154,9 +155,7 @@ class WordFrequency(_SpacyBuiltin):
 
  ## Problem with import for certain characters in the text.
  ## >> text = Import["ExampleData/EinsteinSzilLetter.txt"];
- >> text = "I have a dairy cow, it's not just any cow. \
-She gives me milkshake, oh what a salty cow. She is the best \
-cow in the county.";
+ >> text = "I have a dairy cow, it's not just any cow. She gives me milkshake, oh what a salty cow. She is the best cow in the county.";
 
  >> WordFrequency[text, "a" | "the"]
  = 0.121212
@@ -232,7 +231,7 @@ class WordSimilarity(_SpacyBuiltin):
  "idxfmt": "Indices must be integers or lists of integers of the same length.",
  },
  )
- summary_text = "Measure the similarity of two texts"
+ summary_text = "measure similarity of two texts"
 
  def eval(
  self, text1: String, text2: String, evaluation: Evaluation, options: dict

pymathics/natlang/util.py

@@ -4,6 +4,9 @@
 utils
 """
 
+# Don't consider this for user documentation
+no_doc = True
+
 
 def merge_dictionaries(a, b):
  c = a.copy()