diff --git a/scikits/learn/datasets/mlcomp.py b/scikits/learn/datasets/mlcomp.py
index fa14da4015ed21af274b528c7b1ada5da384561c..6bdc20fa9962b5b99ee32916b07c7f586d4df0f2 100644
--- a/scikits/learn/datasets/mlcomp.py
+++ b/scikits/learn/datasets/mlcomp.py
@@ -6,7 +6,8 @@ import os
 import numpy as np
 from scikits.learn.datasets.base import load_text_files
 from scikits.learn.feature_extraction.text import HashingVectorizer
-from scikits.learn.feature_extraction.sparse.text import SparseHashingVectorizer
+from scikits.learn.feature_extraction.sparse.text import HashingVectorizer as \
+                                                         SparseCountVectorizer
 
 
 def _load_document_classification(dataset_path, metadata, set_=None):
diff --git a/scikits/learn/feature_extraction/sparse/__init__.py b/scikits/learn/feature_extraction/sparse/__init__.py
index 890ce8f7f14e58c0b8abe1f998cb92f7cfe7cf63..fa0f61b5bd0db51b5ab30989174ba4560966983e 100644
--- a/scikits/learn/feature_extraction/sparse/__init__.py
+++ b/scikits/learn/feature_extraction/sparse/__init__.py
@@ -1,3 +1,3 @@
 
-from .text import SparseCountVectorizer, SparseTfidfTransformer, \
-                  SparseVectorizer, SparseHashingVectorizer
+from .text import CountVectorizer, TfidfTransformer, Vectorizer, \
+                  HashingVectorizer
diff --git a/scikits/learn/feature_extraction/sparse/text.py b/scikits/learn/feature_extraction/sparse/text.py
index 1ba7dd71746f1cb3c64ad94f83d2def54aa3b9e7..290f4d9f4b17f050c684cb9267d0b77b922c770d 100644
--- a/scikits/learn/feature_extraction/sparse/text.py
+++ b/scikits/learn/feature_extraction/sparse/text.py
@@ -11,12 +11,12 @@ import scipy.sparse as sp
 from ..text import BaseCountVectorizer, BaseTfidfTransformer, BaseVectorizer, \
                    DEFAULT_ANALYZER
 
-class SparseCountVectorizer(BaseCountVectorizer):
+class CountVectorizer(BaseCountVectorizer):
 
     def _init_matrix(self, shape):
         return sp.dok_matrix(shape, dtype=self.dtype)
 
-class SparseTfidfTransformer(BaseTfidfTransformer):
+class TfidfTransformer(BaseTfidfTransformer):
 
     def fit(self, X, y=None):
         """
@@ -73,11 +73,11 @@ class SparseTfidfTransformer(BaseTfidfTransformer):
 
         return X
 
-class SparseVectorizer(BaseVectorizer):
+class Vectorizer(BaseVectorizer):
     """
     Convert a collection of raw documents to a sparse matrix.
 
-    Equivalent to SparseCountVectorizer followed by SparseTfidfTransformer.
+    Equivalent to CountVectorizer followed by TfidfTransformer.
     """
 
     def __init__(self,
@@ -85,10 +85,10 @@ class SparseVectorizer(BaseVectorizer):
                  use_tf=True,
                  use_idf=True,
                  normalize=False):
-        self.tc = SparseCountVectorizer(analyzer, dtype=np.float64)
-        self.tfidf = SparseTfidfTransformer(use_tf, use_idf, normalize)
+        self.tc = CountVectorizer(analyzer, dtype=np.float64)
+        self.tfidf = TfidfTransformer(use_tf, use_idf, normalize)
 
-class SparseHashingVectorizer(object):
+class HashingVectorizer(object):
     """Compute term freq vectors using hashed term space in a sparse matrix
 
     The logic is the same as HashingVectorizer but it is possible to use much
diff --git a/scikits/learn/feature_extraction/tests/test_text.py b/scikits/learn/feature_extraction/tests/test_text.py
index f741ce6a486035e0552dbbd6e2a02843aedb0b96..2ce2e325b1ab5ee7855968c961bd29010a1bb19e 100644
--- a/scikits/learn/feature_extraction/tests/test_text.py
+++ b/scikits/learn/feature_extraction/tests/test_text.py
@@ -1,14 +1,19 @@
 from scikits.learn.feature_extraction.text import CharNGramAnalyzer
+from scikits.learn.feature_extraction.text import WordNGramAnalyzer
+from scikits.learn.feature_extraction.text import strip_accents
+
 from scikits.learn.feature_extraction.text import CountVectorizer
-from scikits.learn.feature_extraction.text import HashingVectorizer
 from scikits.learn.feature_extraction.text import TfidfTransformer
 from scikits.learn.feature_extraction.text import Vectorizer
-from scikits.learn.feature_extraction.sparse.text import SparseCountVectorizer
-from scikits.learn.feature_extraction.sparse.text import SparseHashingVectorizer
-from scikits.learn.feature_extraction.sparse.text import SparseTfidfTransformer
-from scikits.learn.feature_extraction.sparse.text import SparseVectorizer
-from scikits.learn.feature_extraction.text import WordNGramAnalyzer
-from scikits.learn.feature_extraction.text import strip_accents
+from scikits.learn.feature_extraction.text import HashingVectorizer
+
+import scikits.learn.feature_extraction.sparse.text as st
+
+SparseCountVectorizer = st.CountVectorizer
+SparseTfidfTransformer = st.TfidfTransformer
+SparseVectorizer = st.Vectorizer
+SparseHashingVectorizer = st.HashingVectorizer
+
 from scikits.learn.grid_search import GridSearchCV
 from scikits.learn.pipeline import Pipeline
 from scikits.learn.svm import LinearSVC as DenseLinearSVC