diff --git a/requirements.txt b/requirements.txt
index 58c53ba..6fb272a 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-pandas
+pandas==0.16.2
 scikit-learn
 statsmodels
 gensim
@@ -7,3 +7,6 @@ pyth
 pymongo
 MySQL-python
 scipy
+unidecode
+multiprocess
+nltk
diff --git a/rosetta/tests/test_text.py b/rosetta/tests/test_text.py
index d26d470..3bf9f90 100644
--- a/rosetta/tests/test_text.py
+++ b/rosetta/tests/test_text.py
@@ -323,16 +323,6 @@ def test_dirichlet_expectation(self):
             [-0.13470677,  -13.32429878]]).T
         assert_allclose(result, benchmark, atol=1e-4)
 
-    def test_expElogbeta(self):
-        # Make sure equal to exponential of dirichlet_expectation when we
-        # pass in all ones
-        lda = self.choose_lda('lda')
-        lda._lambda_word_sums = pd.Series(
-            np.ones(lda.num_topics), index=lda.topics)
-        result = lda._expElogbeta
-        benchmark = np.exp(lda._dirichlet_expectation(lda.pr_token_topic))
-        assert_frame_equal(result, benchmark)
-
     def test_predict_1(self):
         # Use fact that w0  <--> topic_0,  w1 <--> topic_1
         lda = self.choose_lda('lda_2')
diff --git a/rosetta/text/vw_helpers.py b/rosetta/text/vw_helpers.py
index dfcb857..93f8efc 100644
--- a/rosetta/text/vw_helpers.py
+++ b/rosetta/text/vw_helpers.py
@@ -332,6 +332,9 @@ def _set_probabilities(self, topics, predictions):
         self.pr_doc = doc_sums / doc_sums.sum()
         self.pr_doc_topic = predictions / predictions.sum().sum()
 
+        lam = self._lambda_word_sums * self.pr_token_topic
+        self._constExpElogbeta = np.exp(self._dirichlet_expectation(lam + EPS))
+
     def prob_token_topic(self, token=None, topic=None, c_token=None,
                          c_topic=None):
         """
@@ -565,9 +568,7 @@ def _expElogbeta(self):
         topic-word weights.
         """
         # Get lambda, the dirichlet parameter originally returned by VW.
-        lam = self._lambda_word_sums * self.pr_token_topic
-
-        return np.exp(self._dirichlet_expectation(lam + EPS))
+        return self._constExpElogbeta
 
     def _dirichlet_expectation(self, alpha):
         """