Filter - language form corpus in StopwordsFilter

orangecontrib/text/annotate_documents.py

@@ -289,7 +289,7 @@ def _hypergeom_clusters(
 
     corpus_ = Corpus.from_file("book-excerpts")
     for pp in (LowercaseTransformer(), RegexpTokenizer(r"\w+"),
-               StopwordsFilter("English"), FrequencyFilter(0.1)):
+               StopwordsFilter("en"), FrequencyFilter(0.1)):
         corpus_ = pp(corpus_)
 
     transformed_corpus = BowVectorizer().transform(corpus_)

orangecontrib/text/preprocess/filter.py

@@ -1,5 +1,5 @@
 from itertools import compress
-from typing import List, Callable
+from typing import List, Callable, Optional
 import os
 import re
 
@@ -11,6 +11,7 @@
 from Orange.util import wrap_callback, dummy_callback
 
 from orangecontrib.text import Corpus
+from orangecontrib.text.language import ISO2LANG
 from orangecontrib.text.misc import wait_nltk_data
 from orangecontrib.text.preprocess import TokenizedPreprocessor
 
@@ -71,13 +72,32 @@ class StopwordsFilter(BaseTokenFilter, FileWordListMixin):
     """ Remove tokens present in NLTK's language specific lists or a file. """
     name = 'Stopwords'
 
-    @wait_nltk_data
-    def __init__(self, language='English', path: str = None):
+    # nltk uses different language nams for some languages
+    nltk_mapping = {"Slovenian": "Slovene"}
+
+    def __init__(
+        self,
+        language: Optional[str] = "en",
+        path: Optional[str] = None,
+    ):
+        """
+        Parameters
+        ----------
+        language
+            The language code in ISO format for NLTK stopwords selection.
+            If None, only words from file are used (NLTK stopwords are not used).
+        path
+            The path to the file with its stopwords will be used if present.
+            The file must contain a newline-separated list of words.
+        """
         super().__init__()
         FileWordListMixin.__init__(self, path)
-        self.__stopwords = set(x.strip() for x in
-                               stopwords.words(language.lower())) \
-            if language else []
+        self.__stopwords = set()
+        if language:
+            # transform iso code to NLTK's language name
+            language = ISO2LANG[language]
+            language = self.nltk_mapping.get(language, language).lower()
+            self.__stopwords = set(x.strip() for x in stopwords.words(language))
 
     @staticmethod
     @wait_nltk_data

orangecontrib/text/tests/test_preprocess.py

@@ -430,7 +430,7 @@ def _check(self, token):
         self.assertEqual(filtered, ['a'])
 
     def test_stopwords(self):
-        f = preprocess.StopwordsFilter('english')
+        f = preprocess.StopwordsFilter("en")
         self.assertFalse(f._check('a'))
         self.assertTrue(f._check('filter'))
         with self.corpus.unlocked():
@@ -440,7 +440,7 @@ def test_stopwords(self):
         self.assertEqual(len(corpus.used_preprocessor.preprocessors), 2)
 
     def test_stopwords_slovene(self):
-        f = preprocess.StopwordsFilter('slovene')
+        f = preprocess.StopwordsFilter("sl")
         self.assertFalse(f._check('in'))
         self.assertTrue(f._check('abeceda'))
         with self.corpus.unlocked():

orangecontrib/text/widgets/owannotator.py

@@ -618,7 +618,7 @@ def onDeleteWidget(self):
 
     corpus_ = Corpus.from_file("book-excerpts")
     for pp in (LowercaseTransformer(), RegexpTokenizer(r"\w+"),
-               StopwordsFilter("English"), FrequencyFilter(0.1)):
+               StopwordsFilter("en"), FrequencyFilter(0.1)):
         corpus_ = pp(corpus_)
 
     transformed_corpus = BowVectorizer().transform(corpus_)

orangecontrib/text/widgets/tests/test_owannotator.py

@@ -21,7 +21,7 @@
 
 def preprocess(corpus: Corpus) -> Corpus:
     for pp in (LowercaseTransformer(), RegexpTokenizer(r"\w+"),
-               StopwordsFilter("English"), FrequencyFilter(0.25, 0.5)):
+               StopwordsFilter("en"), FrequencyFilter(0.25, 0.5)):
         corpus = pp(corpus)
     corpus = BowVectorizer().transform(corpus)
     return add_embedding(corpus, 4)