diff --git a/spacy/errors.py b/spacy/errors.py
index ff71b60eb..79ed5ecdb 100644
--- a/spacy/errors.py
+++ b/spacy/errors.py
@@ -127,13 +127,12 @@ class Warnings(object):
             "this, download a newer compatible model or retrain your custom "
             "model with the current spaCy version. For more details and "
             "available updates, run: python -m spacy validate")
-    W033 = ("Training a new {model} using a model with no lexeme normalization "
-            "table. This may degrade the performance of the model to some "
-            "degree. If this is intentional or the language you're using "
-            "doesn't have a normalization table, please ignore this warning. "
-            "If this is surprising, make sure you have the spacy-lookups-data "
-            "package installed. The languages with lexeme normalization tables "
-            "are currently: da, de, el, en, id, lb, pt, ru, sr, ta, th.")
+    W033 = ("Training a new {model} using a model with an empty lexeme "
+            "normalization table. This may degrade the performance to some "
+            "degree. If this is intentional or this language doesn't have a "
+            "normalization table, please ignore this warning.")
+    W034 = ("Please install the package spacy-lookups-data in order to include "
+            "the default lexeme normalization table for the language '{lang}'.")
 
 
 @add_codes
diff --git a/spacy/pipeline/pipes.pyx b/spacy/pipeline/pipes.pyx
index b28f34a7a..ea40e3ae0 100644
--- a/spacy/pipeline/pipes.pyx
+++ b/spacy/pipeline/pipes.pyx
@@ -519,6 +519,12 @@ class Tagger(Pipe):
             warnings.warn(Warnings.W022)
         if len(self.vocab.lookups.get_table("lexeme_norm", {})) == 0:
             warnings.warn(Warnings.W033.format(model="part-of-speech tagger"))
+            try:
+                import spacy_lookups_data
+            except ImportError:
+                if self.vocab.lang in ("da", "de", "el", "en", "id", "lb", "pt",
+                        "ru", "sr", "ta", "th"):
+                    warnings.warn(Warnings.W034.format(lang=self.vocab.lang))
         orig_tag_map = dict(self.vocab.morphology.tag_map)
         new_tag_map = OrderedDict()
         for raw_text, annots_brackets in get_gold_tuples():
diff --git a/spacy/syntax/nn_parser.pyx b/spacy/syntax/nn_parser.pyx
index 6944e9113..145c382a5 100644
--- a/spacy/syntax/nn_parser.pyx
+++ b/spacy/syntax/nn_parser.pyx
@@ -604,6 +604,12 @@ cdef class Parser:
     def begin_training(self, get_gold_tuples, pipeline=None, sgd=None, **cfg):
         if len(self.vocab.lookups.get_table("lexeme_norm", {})) == 0:
             warnings.warn(Warnings.W033.format(model="parser or NER"))
+            try:
+                import spacy_lookups_data
+            except ImportError:
+                if self.vocab.lang in ("da", "de", "el", "en", "id", "lb", "pt",
+                        "ru", "sr", "ta", "th"):
+                    warnings.warn(Warnings.W034.format(lang=self.vocab.lang))
         if 'model' in cfg:
             self.model = cfg['model']
         if not hasattr(get_gold_tuples, '__call__'):