diff --git a/lda2vec/__init__.py b/lda2vec/__init__.py
index 1f785b8b..f7682e01 100644
--- a/lda2vec/__init__.py
+++ b/lda2vec/__init__.py
@@ -1,10 +1,10 @@
-import dirichlet_likelihood
-import embed_mixture
-import tracking
-import preprocess
-import corpus
-import topics
-import negative_sampling
+import lda2vec.dirichlet_likelihood
+import lda2vec.embed_mixture
+import lda2vec.tracking
+import lda2vec.preprocess
+import lda2vec.corpus
+import lda2vec.topics
+import lda2vec.negative_sampling
 
 dirichlet_likelihood = dirichlet_likelihood.dirichlet_likelihood
 EmbedMixture = embed_mixture.EmbedMixture
diff --git a/lda2vec/corpus.py b/lda2vec/corpus.py
index b93aae0a..c46c7704 100644
--- a/lda2vec/corpus.py
+++ b/lda2vec/corpus.py
@@ -576,7 +576,7 @@ def compact_word_vectors(self, vocab, filename=None, array=None,
                     choice = np.array(keys_raw)[idx][np.argmin(d)]
                     # choice = difflib.get_close_matches(word, choices)[0]
                     vector = model[choice]
-                    print compact, word, ' --> ', choice
+                    print(compact, word, ' --> ', choice)
                 except IndexError:
                     pass
             if vector is None:
diff --git a/lda2vec/preprocess.py b/lda2vec/preprocess.py
index fab84957..b6fbee42 100644
--- a/lda2vec/preprocess.py
+++ b/lda2vec/preprocess.py
@@ -1,4 +1,4 @@
-from spacy.en import English
+from spacy.lang.en import English
 from spacy.attrs import LOWER, LIKE_URL, LIKE_EMAIL
 
 import numpy as np
diff --git a/lda2vec/topics.py b/lda2vec/topics.py
index 579ae7b1..daa6dfff 100644
--- a/lda2vec/topics.py
+++ b/lda2vec/topics.py
@@ -103,7 +103,7 @@ def print_top_words_per_topic(data, top_n=10, do_print=True):
         top_words = [data['vocab'][i].strip().replace(' ', '_') for i in top]
         msg = ' '.join(top_words)
         if do_print:
-            print prefix + msg
+            print(prefix + msg)
         lists.append(top_words)
     return lists