diff --git a/itest/src/edu/stanford/nlp/pipeline/TokensRegexNERAnnotatorITest.java b/itest/src/edu/stanford/nlp/pipeline/TokensRegexNERAnnotatorITest.java
index 635f4b34e2..69191d9e6f 100644
--- a/itest/src/edu/stanford/nlp/pipeline/TokensRegexNERAnnotatorITest.java
+++ b/itest/src/edu/stanford/nlp/pipeline/TokensRegexNERAnnotatorITest.java
@@ -161,12 +161,12 @@ public void testBasicMatching() throws Exception {
   }
 
   /**
-   * The LOCATION on Ontario Place should not be overwritten since Ontario (STATE_OR_PROVINCE)
-   * does not span Ontario Place.  Native American Church will overwrite ORGANIZATION with
+   * The LOCATION on Ontario Lake should not be overwritten since Ontario (STATE_OR_PROVINCE)
+   * does not span Ontario Lake.  Native American Church will overwrite ORGANIZATION with
    * RELIGION.
    */
   public void testOverwrite() throws Exception {
-    String str = "I like Ontario Place , and I like the Native American Church , too .";
+    String str = "I like Ontario Lake , and I like the Native American Church , too .";
     Annotation document = createDocument(str);
     annotator.annotate(document);
     List<CoreLabel> tokens = document.get(CoreAnnotations.TokensAnnotation.class);
diff --git a/scripts/makeSerialized.csh b/scripts/makeSerialized.csh
index 97f18ac419..5ddef67777 100755
--- a/scripts/makeSerialized.csh
+++ b/scripts/makeSerialized.csh
@@ -40,7 +40,7 @@ set ctb=/afs/ir/data/linguistic-data/Chinese-Treebank/6/data/utf8/bracketed
 # [(1, 40), (901, 931), (1018, 1018), (1020, 1020), (1036, 1036), (1044, 1044), (1060, 1061), (1072, 1072), (1118, 1119), (1132, 1132), (1141, 1142), (1148, 1148), (2165, 2180), (2295, 2310), (2570, 2602), (2800, 2819)]
 set ctb7train=/u/nlp/data/chinese/ctb7/train.mrg
 set ctb7test=/u/nlp/data/chinese/ctb7/test.mrg
-set negra=/afs/ir/data/linguistic-data/NEGRA/penn-format-train-dev-test
+set negra=/u/nlp/data/GermanACL08/negra/penn-format-train-dev-test
 
 set host=`hostname | cut -d. -f1`
 
diff --git a/src/edu/stanford/nlp/ie/AbstractSequenceClassifier.java b/src/edu/stanford/nlp/ie/AbstractSequenceClassifier.java
index 5ebd403873..13f10096e2 100644
--- a/src/edu/stanford/nlp/ie/AbstractSequenceClassifier.java
+++ b/src/edu/stanford/nlp/ie/AbstractSequenceClassifier.java
@@ -1052,11 +1052,18 @@ private void classifyAndWriteAnswers(Collection<List<IN>> documents,
             IOUtils.encodedOutputStreamPrintWriter(System.out, flags.outputEncoding, true), readerWriter);
   }
 
+  /** Does nothing by default.  Children classes can override if necessary */
+  public void dumpFeatures(Collection<List<IN>> documents) {}
+
   public void classifyAndWriteAnswers(Collection<List<IN>> documents,
                                       PrintWriter printWriter,
                                       DocumentReaderAndWriter<IN> readerWriter)
     throws IOException
   {
+    if (flags.exportFeatures != null) {
+      dumpFeatures(documents);
+    }
+
     Timing timer = new Timing();
 
     Counter<String> entityTP = new ClassicCounter<String>();
diff --git a/src/edu/stanford/nlp/ie/crf/CRFClassifier.java b/src/edu/stanford/nlp/ie/crf/CRFClassifier.java
index be4b834184..50c5a3fe86 100644
--- a/src/edu/stanford/nlp/ie/crf/CRFClassifier.java
+++ b/src/edu/stanford/nlp/ie/crf/CRFClassifier.java
@@ -1101,6 +1101,18 @@ private double[] makeDatumUsingEmbedding(List<IN> info, int loc, List<FeatureFac
     return featureValArr;
   }
 
+  @Override
+  public void dumpFeatures(Collection<List<IN>> docs) {
+    if (flags.exportFeatures != null) {
+      Timing timer = new Timing();
+      timer.start();
+      CRFFeatureExporter<IN> featureExporter = new CRFFeatureExporter<IN>(this);
+      featureExporter.printFeatures(flags.exportFeatures, docs);
+      long elapsedMs = timer.stop();
+      System.err.println("Time to export features: " + Timing.toSecondsString(elapsedMs) + " seconds");
+    }
+  }
+
   @Override
   public List<IN> classify(List<IN> document) {
     if (flags.doGibbs) {
@@ -1599,11 +1611,7 @@ public void train(Collection<List<IN>> objectBankWrapper, DocumentReaderAndWrite
     }
 
     if (flags.exportFeatures != null) {
-      timer.start();
-      CRFFeatureExporter<IN> featureExporter = new CRFFeatureExporter<IN>(this);
-      featureExporter.printFeatures(flags.exportFeatures, docs);
-      elapsedMs = timer.stop();
-      System.err.println("Time to export features: " + Timing.toSecondsString(elapsedMs) + " seconds");
+      dumpFeatures(docs);
     }
 
     for (int i = 0; i <= flags.numTimesPruneFeatures; i++) {
diff --git a/src/edu/stanford/nlp/ie/crf/CRFFeatureExporter.java b/src/edu/stanford/nlp/ie/crf/CRFFeatureExporter.java
index 9b6c2cecc8..5f068a46cb 100644
--- a/src/edu/stanford/nlp/ie/crf/CRFFeatureExporter.java
+++ b/src/edu/stanford/nlp/ie/crf/CRFFeatureExporter.java
@@ -8,6 +8,7 @@
 
 import java.io.IOException;
 import java.io.PrintWriter;
+import java.util.ArrayList;
 import java.util.Collection;
 import java.util.Collections;
 import java.util.List;
@@ -79,7 +80,9 @@ private String getFeatureString(List<IN> document) {
 
       List<List<String>> features = d.asFeatures();
       for (Collection<String> cliqueFeatures : features) {
-        for (String feat : cliqueFeatures) {
+        List<String> sortedFeatures = new ArrayList<String>(cliqueFeatures);
+        Collections.sort(sortedFeatures);
+        for (String feat : sortedFeatures) {
           feat = ubPrefixFeatureString(feat);
           sb.append(delimiter);
           sb.append(feat);
diff --git a/src/edu/stanford/nlp/sentiment/SentimentPipeline.java b/src/edu/stanford/nlp/sentiment/SentimentPipeline.java
index 26a00311eb..b3386edd5e 100644
--- a/src/edu/stanford/nlp/sentiment/SentimentPipeline.java
+++ b/src/edu/stanford/nlp/sentiment/SentimentPipeline.java
@@ -38,8 +38,11 @@
  * <code>-parserModel</code> Which parser model to use, defaults to englishPCFG.ser.gz <br>
  * <code>-sentimentModel</code> Which sentiment model to use, defaults to sentiment.ser.gz <br>
  * <code>-file</code> Which file to process. <br>
+ * <code>-fileList</code> A comma separated list of files to process. <br>
  * <code>-stdin</code> Read one line at a time from stdin. <br>
- * <code>-output</code> pennTrees: Output trees with scores at each binarized node.  vectors: Number tree nodes and print out the vectors.  Defaults to printing just the root. <br>
+ * <code>-output</code> pennTrees: Output trees with scores at each binarized node.  vectors: Number tree nodes and print out the vectors.  probabilities: Output the scores for different labels for each node. Defaults to printing just the root. <br>
+ * <code>-filterUnknown</code> remove unknown trees from the input.  Only applies to TREES input, in which case the trees must be binarized with sentiment labels <br>
+ * <code>-help</code> Print out help <br>
  *
  * @author John Bauer
  */
diff --git a/src/edu/stanford/nlp/tagger/util/CountTagSequences.java b/src/edu/stanford/nlp/tagger/util/CountTagSequences.java
deleted file mode 100644
index 3a41b293d0..0000000000
--- a/src/edu/stanford/nlp/tagger/util/CountTagSequences.java
+++ /dev/null
@@ -1,135 +0,0 @@
-package edu.stanford.nlp.tagger.util;
-
-import edu.stanford.nlp.ling.Label;
-import edu.stanford.nlp.ling.Tag;
-import edu.stanford.nlp.trees.*;
-import edu.stanford.nlp.util.HashIndex;
-import edu.stanford.nlp.util.Index;
-import edu.stanford.nlp.util.StringUtils;
-
-import java.io.Reader;
-import java.text.NumberFormat;
-import java.util.ArrayList;
-import java.util.Iterator;
-import java.util.List;
-
-/**
- * This class makes a table of tag bigram counts and percentages in data.
- * It counts the bigrams (including sentence ends) prints out both a count
- * matrix
- * and percentages.  The only clever part was learning how to format floating
- * point numbers in Java.
- *
- * @author <a href="mailto:manning@cs.stanford.edu">Christopher Manning</a>
- * @version 1.0
- */
-public final class CountTagSequences {
-
-  private static final int MAXTAGS = 50;
-  private static final int FIELDLENG = 7;
-
-  private static Index<Label> numb = new HashIndex<Label>();
-
-  private static int[][] counts = new int[MAXTAGS][MAXTAGS];
-  private static int[] rowcounts = new int[MAXTAGS];
-
-
-  /**
-   * Not meant to be instantiated
-   */
-  private CountTagSequences() {
-  }
-
-  private static void addBigrams(List ts) {
-    Iterator it = ts.iterator();
-    if (!it.hasNext()) {
-      System.err.println("Bung!");
-      System.exit(0);
-    }
-    Label t1 = (Label) it.next();
-    while (it.hasNext()) {
-      Label t2 = (Label) it.next();
-      int i = numb.indexOf(t1, true);
-      int j = numb.indexOf(t2, true);
-      counts[i][j]++;
-      rowcounts[i]++;
-      t1 = t2;
-    }
-  }
-
-
-  private static void printBigrams() {
-    int max = numb.size();
-
-    System.out.println("Tag bigram counts (t1\\t2)");
-    System.out.println("==========================");
-
-    System.out.print(StringUtils.pad("", FIELDLENG));
-    for (int j = 0; j < max; j++) {
-      System.out.print(StringUtils.padLeft(numb.get(j), FIELDLENG));
-    }
-    System.out.println(StringUtils.padLeft("Total", FIELDLENG));
-    for (int i = 0; i < max; i++) {
-      System.out.print(StringUtils.pad(numb.get(i), FIELDLENG));
-      for (int j = 0; j < max; j++) {
-        System.out.print(StringUtils.padLeft(Integer.toString(counts[i][j]), FIELDLENG));
-      }
-      System.out.println(StringUtils.padLeft(Integer.toString(rowcounts[i]), FIELDLENG));
-    }
-
-    NumberFormat nf = NumberFormat.getNumberInstance();
-    nf.setMaximumFractionDigits(1);
-    nf.setMinimumFractionDigits(1);
-    System.out.println();
-    System.out.println("Tag bigram percentages (stochastic matrix)");
-    System.out.println("==========================================");
-
-    System.out.print(StringUtils.pad("", FIELDLENG));
-    for (int j = 0; j < max; j++) {
-      System.out.print(StringUtils.padLeft(numb.get(j), FIELDLENG));
-    }
-    System.out.println();
-    for (int i = 0; i < max; i++) {
-      System.out.print(StringUtils.pad(numb.get(i), FIELDLENG));
-      for (int j = 0; j < max; j++) {
-        if (rowcounts[i] == 0) {
-          System.out.print(StringUtils.padLeft("-", FIELDLENG));
-        } else {
-          double percent = (counts[i][j] * 100.0) / rowcounts[i];
-          System.out.print(StringUtils.padLeft(nf.format(percent), FIELDLENG));
-        }
-      }
-      System.out.println();
-    }
-
-
-  }
-
-  /**
-   * Describe <code>main</code> method here.
-   *
-   * @param args a <code>String[]</code> value
-   */
-  public static void main(String[] args) {
-    Label boundary = new Tag("<s>");
-    // delete empties from the tree!
-    Treebank treebank = new DiskTreebank(new TreeReaderFactory() {
-      @Override
-      public TreeReader newTreeReader(Reader in) {
-        return new PennTreeReader(in, new LabeledScoredTreeFactory(), new BobChrisTreeNormalizer());
-      }
-    });
-    treebank.loadPath(args[0]);
-    for (Tree t : treebank) {
-      // System.out.println(t);
-      List<Label> ts = new ArrayList<Label>();
-      ts.add(boundary);
-      t.preTerminalYield(ts);
-      ts.add(boundary);
-      // System.out.println(ts);
-      addBigrams(ts);
-    }
-    printBigrams();
-  }
-
-}