private String standardCombination(
      CQAinstance cqainstance, List<List<Double>> features, List<JCas> allCommentsCas) {

    StringBuffer sb = new StringBuffer();

    List<CQAcomment> comments = cqainstance.getComments();

    for (int i = 0; i < comments.size() - 1; i++) {
      CQAcomment comment1 = comments.get(i);

      for (int j = i + 1; j < comments.size(); j++) {
        CQAcomment comment2 = comments.get(j);

        sb.append(comment1.getId()).append("-").append(comment2.getId()).append(",");
        sb.append(getClassLabel(comment1.getGold(), comment2.getGold()));
        sb.append(",");

        if (COMBINATION_CONCAT) {
          sb.append(Joiner.on(",").join(concatVectors(features.get(i), features.get(j))));
        } else {
          sb.append(Joiner.on(",").join(absoluteDifference(features.get(i), features.get(j))));
        }

        // Simimarities
        if (INCLUDE_SIMILARITIES) {
          AugmentableFeatureVector fv;
          fv =
              (AugmentableFeatureVector)
                  pfEnglish.getPairFeatures(
                      allCommentsCas.get(i), allCommentsCas.get(j), PARAMETER_LIST);

          //	          System.out.println(
          //	              ids.get(i) + ","+
          //	              labels.get(i) + ","+
          //	              ids.get(j)+","+
          //	              labels.get(j) + ","+
          //	              Joiner.on(",").join(this.serializeFv(fv))
          //	          );

          sb.append(",").append(Joiner.on(",").join(this.serializeFv(fv)));
        }

        // out.writeLn(sb.toString());
        sb.append("\n");
      }
    }
    return sb.toString();
  }
  // TODO the method should be private
  public void processEnglishFile(Document doc, String dataFile, String suffix)
      throws ResourceInitializationException, UIMAException, IOException,
          AnalysisEngineProcessException, SimilarityException {

    String plainTextOutputPath = dataFile + "plain.txt";
    String goodVSbadOutputPath = dataFile + ".csv";
    String pairwiseOutputPath = dataFile + getPairwiseSuffix();
    String kelpFilePath = dataFile + ".klp";

    /** Marker which adds relational information to a pair of trees */
    MarkTreesOnRepresentation marker = new MarkTreesOnRepresentation(new MarkTwoAncestors());

    /** Load stopwords for english */
    marker.useStopwords(Stopwords.STOPWORD_EN);

    /** Tree serializer for converting tree structures to string */
    TreeSerializer ts = new TreeSerializer().enableRelationalTags().useRoundBrackets();

    /// ** Instantiate CASes */ assigned in the for loop
    // JCas questionCas = JCasFactory.createJCas();

    // WriteFile out = new WriteFile(dataFile + ".csv");
    // TODO ABC, Sep 10th 2015. Do we really need this? It seems like a bad patch
    doc.select("QURAN").remove();
    doc.select("HADEETH").remove();

    boolean firstRow = true;

    /** Consume data */
    Elements questions = doc.getElementsByTag("Question");
    int numberOfQuestions = questions.size();
    int qNumber = 1;

    for (Element question : questions) {
      System.out.println("[INFO]: Processing " + qNumber++ + " out of " + numberOfQuestions);

      CQAinstance cqainstance = qElementToObject(question);

      getFeaturesFromThread(cqainstance);
      // TODO MOVE FROM HERE TO getFeaturesFromThread.
      // FOR THAT the printing operations have to be moved out and
      // question and comment must have a method to extract header+body.
      // Move them from SubjectBodyAggregator
      // AQUI VOY
      /** Setup question CAS */

      // questionCas.reset();
      JCas questionCas = cqaElementToCas(cqainstance.getQuestion());

      fm.writeLn(
          plainTextOutputPath,
          "---------------------------- QID: "
              + cqainstance.getQuestion().getId()
              + " USER:"******"q-" + qid, qsubject + ". " + qbody));

      /*Comment-level features to be combined*/
      List<List<Double>> listFeatures = new ArrayList<List<Double>>();
      List<Map<String, Double>> albertoSimoneFeatures;
      if (GENERATE_ALBERTO_AND_SIMONE_FEATURES) { // TODO RENAME THIS PLEASE
        albertoSimoneFeatures = FeatureExtractor.extractFeatures(cqainstance);
      }

      int commentIndex = 0;
      List<JCas> allCommentsCas = new ArrayList<JCas>();
      for (CQAcomment c : cqainstance.getComments()) {
        /** Setup comment CAS */
        JCas commentCas = cqaElementToCas(c);

        /** Run the UIMA pipeline */
        SimplePipeline.runPipeline(commentCas, this.analysisEngineList);
        // this.analyzer.analyze(commentCas, new SimpleContent("c-" + cid, csubject + ". " +
        // cbody));

        AugmentableFeatureVector fv;
        if (GENERATE_MASSIMO_FEATURES) {
          fv =
              (AugmentableFeatureVector)
                  pfEnglish.getPairFeatures(questionCas, commentCas, PARAMETER_LIST);
        } else {
          fv = new AugmentableFeatureVector(this.alphabet);
        }

        if (GENERATE_ALBERTO_AND_SIMONE_FEATURES) {
          Map<String, Double> featureVector = albertoSimoneFeatures.get(commentIndex);
          for (String featureName : FeatureExtractor.getAllFeatureNames()) {
            Double value = featureVector.get(featureName);
            double featureValue = 0;
            if (value != null) {
              featureValue = value;
            }
            fv.add(featureName, featureValue);
          }
        }
        commentIndex++;

        /**
         * ************************************* * * * PLUG YOUR FEATURES HERE * * * *
         * *************************************
         */

        /**
         * fv is actually an AugmentableFeatureVector from the Mallet library
         *
         * <p>Internally the features are named so you must specify an unique identifier.
         *
         * <p>An example:
         *
         * <p>((AugmentableFeatureVector) fv).add("your_super_feature_id", 42);
         *
         * <p>or:
         *
         * <p>AugmentableFeatureVector afv = (AugmentableFeatureVector) fv;
         * afv.add("your_super_feature_id", 42);
         */

        // ((AugmentableFeatureVector) fv).add("quseridEqCuserid", quseridEqCuserid);

        /**
         * ************************************* * * * THANKS! * * * *
         * *************************************
         */

        /** Produce outputs */
        writeToPlainTextOutput(plainTextOutputPath, c, commentCas);

        //				String goodVSbadOutputPath = dataFile + ".csv";
        //				String pairwiseOutputPath

        // FIXME Once we fix that issue with the features, we can know this info
        // in advance and fix the output, probably out of the method
        if (firstRow) {
          // header for Good vs Bad
          this.fm.write(goodVSbadOutputPath, "qid,cgold,cgold_yn");
          for (int i = 0; i < fv.numLocations(); i++) {
            int featureIndex = i + 1;
            this.fm.write(goodVSbadOutputPath, ",f" + featureIndex);
          }
          this.fm.writeLn(goodVSbadOutputPath, "");

          // header for pairwise
          this.fm.write(pairwiseOutputPath, "qid,cgold");
          int numFeatures = fv.numLocations();
          if (COMBINATION_CONCAT) {
            numFeatures *= 2;
          }
          if (INCLUDE_SIMILARITIES) {
            numFeatures += PairFeatureFactoryEnglish.NUM_SIM_FEATURES;
          }

          for (int i = 0; i < numFeatures; i++) {
            int featureIndex = i + 1;
            this.fm.write(pairwiseOutputPath, ",f" + featureIndex);
          }
          this.fm.writeLn(pairwiseOutputPath, "");

          firstRow = false;
        }

        List<Double> features = this.serializeFv(fv);
        listFeatures.add(features);

        this.fm.writeLn(
            goodVSbadOutputPath,
            c.getId()
                + ","
                + c.getGold()
                + ","
                + c.getGold_yn()
                + ","
                + Joiner.on(",").join(features));

        /** Produce also the file needed to train structural models */
        if (PRODUCE_SVMLIGHTTK_DATA) {
          produceSVMLightTKExample(
              questionCas,
              commentCas,
              suffix,
              ts,
              cqainstance.getQuestion().getId(),
              c.getId(),
              c.getGold(),
              c.getGold_yn(),
              features);
        }
        if (PRODUCE_KELP_DATA) {
          produceKelpExample(
              questionCas,
              commentCas,
              kelpFilePath,
              ts,
              cqainstance.getQuestion().getId(),
              c.getId(),
              c.getGold(),
              c.getGold_yn(),
              features);
        }
        allCommentsCas.add(commentCas);
      }
      // TODO MOVE UP TO HERE

      this.fm.write(
          pairwiseOutputPath, computePairwiseFeatures(cqainstance, listFeatures, allCommentsCas));
      // out.writeLn(computePairwiseFeatures(q, listFeatures);
    }

    //		Iterator<String> iterator = questionCategories.iterator();
    //		while(iterator.hasNext()){
    //			System.out.println("CATEGORY_" + iterator.next());
    //		}

    this.fm.closeFiles();
  }