// Convert CAS data into structures usable by ClearNLP

      for (int i = 0; i < tokens.size(); i++) {

        BaseToken token = tokens.get(i);

        String lemma = useLemmatizer ? lemmatizer.getLemma(token.getCoveredText(), token.getPartOfSpeech()) : token.getNormalizedForm();

        tree.add(node);

      }

      // Run parser and convert output back to CAS friendly data types

      parser.process(tree);

          String form = casDepNode.getForm();

          String pos = casDepNode.getPostag();

          String lemma = casDepNode.getLemma();

         

          tree.add(node);

          idIter++;

        }

      }

      for (int i=1; i<tree.size(); i++)

      {

        String  label = deprels[i-1];

        node.setHead(head, label);

      }

     

      tree.initSHeads();

    Map<Predicate, List<SemanticArgument>> predicateArguments = new HashMap<Predicate, List<SemanticArgument>>();

    for (int i = 1; i < tree.size(); i++) {

        // Every ClearNLP parserNode will contain an srlInfo field.

        BaseToken token = tokens.get(i - 1);

        String rolesetId;

       

        {

          if (!headIdToPredicate.containsKey(parserNode)) {

            // We have not encountered this predicate yet, so create it

            Predicate pred = this.createPredicate(jCas, rolesetId, token);

            headIdToPredicate.put(parserNode, pred);

            pred.setRelations(new EmptyFSList(jCas));

          }

        }

    }

   

   

    // Start at node 1, since node 0 is considered the head of the sentence

    for (int i = 1; i < tree.size(); i++) {

      // Every ClearNLP parserNode will contain an srlInfo field.

      BaseToken token = tokens.get(i - 1);

    

          Predicate predicate = headIdToPredicate.get(head.getNode());

          // Append this argument to the predicate's list of arguments

          if (!predicateArguments.containsKey(predicate)) {

            predicateArguments.put(predicate, new ArrayList<SemanticArgument>());

  private void initRegexes() {

    // Recognizes phrases like "<disease1>, <disease2>, or FOCUS".  For appending for any FOCUS.

    DEPNode[] regnodes_NN_CONJ_NN = new DEPNode[1];

    regnodes_NN_CONJ_NN[0].setLabel(DependencyRegex.fromSet(rel_conjunctSet));

    //regnodes_NN_CONJ_NN[1]        = new DEPNode();

    //regnodes_NN_CONJ_NN[1].deprel = DependencyRegex.fromSet(rel_conjunctSet);

    // Recognizes phrases like "denies <symptom1>"

    DEPNode[] regnodes_VBNEG_OBJ = new DEPNode[2];

    regnodes_VBNEG_OBJ[1].setLabel(DEPLibEn.DEP_DOBJ);  /* was "OBJ" in clearparser */

    int cVBNEG_OBJ = 0;

   

    // Recognizes phrases like "was not FOCUS" -- in dependency order: not was

    DEPNode[] regnodes_PRT_VB_PRD = new DEPNode[3];

    regnodes_PRT_VB_PRD[0].setLabel(DEPLibEn.DEP_NEG); // was: ("ADV");

    regnodes_PRT_VB_PRD[2].setLabel(DEPLibEn.DEP_ACOMP); // was: ("PRD");

    regnodes_PRT_VB_PRD[2].pos      = DependencyRegex.ANY_ADJECTIVE;

    int cPRT_VB_PRD = 1;

   

    // Recognizes phrases like "did not find FOCUS"  (don't think the modal verb will be on the path?)

    DEPNode[] regnodes_PRT_MOD_VB_OBJ = new DEPNode[3];

    regnodes_PRT_MOD_VB_OBJ[0].setLabel(DEPLibEn.DEP_NEG); // was: ("ADV");

    regnodes_PRT_MOD_VB_OBJ[1].pos      = ANY_VERB;

    regnodes_PRT_MOD_VB_OBJ[2].setLabel(DEPLibEn.DEP_DOBJ); 

    // everything below here was replaced for this path:

    regnodes_PRT_MOD_VB_OBJ[1]          = new DEPNode(DEPLib.NULL_ID,fromSet(iv_modalVerbsSet));

//    regnodes_PRT_MOD_VB_OBJ[1].form     = DependencyRegex.fromSet(iv_modalVerbsSet);

    regnodes_PRT_MOD_VB_OBJ[2]          = new DEPNode(DEPLib.NULL_ID,fromSet(iv_regVerbsSet));

//    regnodes_PRT_MOD_VB_OBJ[2].form     = DependencyRegex.fromSet(iv_regVerbsSet);

//    regnodes_PRT_MOD_VB_OBJ[2].setLabel(DEPLibEn.DEP_CCOMP); // was: ("VC"); really a shot in the dark

    regnodes_PRT_MOD_VB_OBJ[2].pos      = ANY_VERB;

    regnodes_PRT_MOD_VB_OBJ[3]          = new DEPNode(DEPLib.NULL_ID,ANY_TOKEN);

    regnodes_PRT_MOD_VB_OBJ[3].setLabel(DEPLibEn.DEP_DOBJ);  // was:("OBJ"); */

    int cPRT_MOD_VB_OBJ = 1;

   

    // Recognizes phrases like "did not find evidence of FOCUS"

    DEPNode[] regnodes_PRT_MOD_VB_OBJ_IN_PMOD = new DEPNode[6];

    regnodes_PRT_MOD_VB_OBJ_IN_PMOD[0].setLabel(DEPLibEn.DEP_NEG); // was:("ADV");

    regnodes_PRT_MOD_VB_OBJ_IN_PMOD[1].setLabel(DEPLibEn.DEP_CCOMP); // was: ("VC");

    regnodes_PRT_MOD_VB_OBJ_IN_PMOD[3].setLabel(DEPLibEn.DEP_DOBJ); //("OBJ");

    regnodes_PRT_MOD_VB_OBJ_IN_PMOD[4].pos    = "IN";

    regnodes_PRT_MOD_VB_OBJ_IN_PMOD[5].setLabel(DEPLibEn.DEP_POBJ); // was: ("PMOD");   

    int cPRT_MOD_VB_OBJ_IN_PMOD = 1;

   

    // Recognizes phrases like "excluding FOCUS"

    DEPNode[] regnodes_VBN_SBJ = new DEPNode[2];

    regnodes_VBN_SBJ[0].pos    = "VBG";  // was VBN

    regnodes_VBN_SBJ[1].setLabel(DEPLibEn.DEP_DOBJ); // was: ("SBJ");

    int cVBN_SBJ = 0;

   

    // Recognizes phrases like "rules out FOCUS"

    DEPNode[] regnodes_PRT_rule_OBJ = new DEPNode[3];

    regnodes_PRT_rule_OBJ[0].setLabel(DEPLibEn.DEP_PRT); // was: ("PRT");

    regnodes_PRT_rule_OBJ[2].setLabel(DEPLibEn.DEP_DOBJ); //("OBJ");

    int cPRT_rule_OBJ = 1;

   

    //// Noun-ish rules

    // Recognizes phrases like "no pain"

    DEPNode[] regnodes_DT_NMOD   = new DEPNode[2];

    regnodes_DT_NMOD[0].setLabel(DEP_DET); // was: ("NMOD");

    regnodes_DT_NMOD[1].pos      = DependencyRegex.ANY_NOUN;

    int cDT_NMOD = 1;

    // Recognizes phrases like "without pain"

    DEPNode[] regnodes_IN_PMOD   = new DEPNode[2];

    regnodes_IN_PMOD[0].setLabel(DEP_PMOD); // was: ("PMOD");

    regnodes_IN_PMOD[1].pos      = DependencyRegex.ANY_NOUN;

    int cIN_PMOD = 0;

   

    // Recognizes phrases like "no history of FOCUS"

    DEPNode[] regnodes_DT_NN_IN_PMOD = new DEPNode[4];

//    regnodes_DT_NN_IN_PMOD[0].setLabel(DEP_PMOD + "|" + DEP_NMOD); // was: ("[NP]MOD");  // no convincing evidence that this can be restricted among the set of words listed above. (no would be det i think)

    regnodes_DT_NN_IN_PMOD[2].pos    = "IN";

    regnodes_DT_NN_IN_PMOD[3].setLabel(DEP_POBJ); // was: ("PMOD");

    int cDT_NN_IN_PMOD = 1;

   

//    DEPNode[] regnodes_JJNEG_COP_IN_PMOD = new DEPNode[4];

//    regnodes_JJNEG_COP_IN_PMOD[0]        = new DEPNode();

//    regnodes_JJNEG_COP_IN_PMOD[0].form   = DependencyRegex.fromSet(iv_negAdjectivesSet);

//    regnodes_JJNEG_COP_IN_PMOD[1]        = new DEPNode();

//    regnodes_JJNEG_COP_IN_PMOD[1].pos    = DependencyRegex.ANY_VERB; // banking that people don't use double negatives

//    regnodes_JJNEG_COP_IN_PMOD[2]        = new DEPNode();

//    regnodes_JJNEG_COP_IN_PMOD[2].form   = DependencyRegex.fromSet(iv_regPrepositionsSet);

//    regnodes_JJNEG_COP_IN_PMOD[2].pos    = "IN";

//    regnodes_JJNEG_COP_IN_PMOD[3]        = new DEPNode();

//    regnodes_JJNEG_COP_IN_PMOD[3].deprel = "PMOD";

//    int cJJNEG_COP_IN_PMOD = 1;

    // Recognizes negative-adjective phrases like "negative for carcinoma"

    DEPNode[] regnodes_JJNEG_AMOD_PMOD = new DEPNode[3];

    regnodes_JJNEG_AMOD_PMOD[1].pos    = "IN";

    regnodes_JJNEG_AMOD_PMOD[2].setLabel(DEP_POBJ); // was: ("PMOD");

    int cJJNEG_AMOD_PMOD = 0;

   

    // Recognizes negative-adjective phrases like "unlikely to have carcinoma"

    // FIXME - not sure this one works correctly -- this example parses weirdly in CVD.

    DEPNode[] regnodes_JJNEG_AMOD_IM_OBJ = new DEPNode[4];

    regnodes_JJNEG_AMOD_IM_OBJ[1].setLabel(DEP_AMOD); // was: ("AMOD");

    regnodes_JJNEG_AMOD_IM_OBJ[2].setLabel(DEP_INFMOD); // was: ("IM");

    regnodes_JJNEG_AMOD_IM_OBJ[3].setLabel(DEP_DOBJ); // was: ("OBJ");

    int cJJNEG_AMOD_IM_OBJ = 0;

   

    // Recognizes negative-adjective phrases like "unlikely carcinoma"

    DEPNode[] regnodes_JJNEG_NN = new DEPNode[2];

    regnodes_JJNEG_NN[1].pos    = DependencyRegex.ANY_NOUN;

    int cJJNEG_NN = 1;

   

   

    // Add the verb-ish rules to the set of regexes to search

  private void initRegexes() {

    // Recognizes phrases like "<disease1>, <disease2>, or FOCUS".  For appending for any FOCUS.

    DEPNode[] regnodes_NN_CONJ_NN = new DEPNode[1];

    regnodes_NN_CONJ_NN[0].setLabel(DependencyRegex.fromSet(rel_conjunctSet));

    //regnodes_NN_CONJ_NN[1]        = new DEPNode();

    //regnodes_NN_CONJ_NN[1].deprel = DependencyRegex.fromSet(rel_conjunctSet);

    // Recognizes phrases like "denies <symptom1>"

    DEPNode[] regnodes_VBNEG_OBJ = new DEPNode[2];

    regnodes_VBNEG_OBJ[1].setLabel(DEPLibEn.DEP_DOBJ);  /* was "OBJ" in clearparser */

    int cVBNEG_OBJ = 0;

   

    // Recognizes phrases like "was not FOCUS" -- in dependency order: not was

    DEPNode[] regnodes_PRT_VB_PRD = new DEPNode[3];

    regnodes_PRT_VB_PRD[0].setLabel(DEPLibEn.DEP_NEG); // was: ("ADV");

    regnodes_PRT_VB_PRD[2].setLabel(DEPLibEn.DEP_ACOMP); // was: ("PRD");

    regnodes_PRT_VB_PRD[2].pos      = DependencyRegex.ANY_ADJECTIVE;

    int cPRT_VB_PRD = 1;

   

    // Recognizes phrases like "did not find FOCUS"  (don't think the modal verb will be on the path?)

    DEPNode[] regnodes_PRT_MOD_VB_OBJ = new DEPNode[3];

    regnodes_PRT_MOD_VB_OBJ[0].setLabel(DEPLibEn.DEP_NEG); // was: ("ADV");

    regnodes_PRT_MOD_VB_OBJ[1].pos      = ANY_VERB;

    regnodes_PRT_MOD_VB_OBJ[2].setLabel(DEPLibEn.DEP_DOBJ); 

    // everything below here was replaced for this path:

    regnodes_PRT_MOD_VB_OBJ[1]          = new DEPNode(DEPLib.NULL_ID,fromSet(iv_modalVerbsSet));

//    regnodes_PRT_MOD_VB_OBJ[1].form     = DependencyRegex.fromSet(iv_modalVerbsSet);

    regnodes_PRT_MOD_VB_OBJ[2]          = new DEPNode(DEPLib.NULL_ID,fromSet(iv_regVerbsSet));

//    regnodes_PRT_MOD_VB_OBJ[2].form     = DependencyRegex.fromSet(iv_regVerbsSet);

//    regnodes_PRT_MOD_VB_OBJ[2].setLabel(DEPLibEn.DEP_CCOMP); // was: ("VC"); really a shot in the dark

    regnodes_PRT_MOD_VB_OBJ[2].pos      = ANY_VERB;

    regnodes_PRT_MOD_VB_OBJ[3]          = new DEPNode(DEPLib.NULL_ID,ANY_TOKEN);

    regnodes_PRT_MOD_VB_OBJ[3].setLabel(DEPLibEn.DEP_DOBJ);  // was:("OBJ"); */

    int cPRT_MOD_VB_OBJ = 1;

   

    // Recognizes phrases like "did not find evidence of FOCUS"

    DEPNode[] regnodes_PRT_MOD_VB_OBJ_IN_PMOD = new DEPNode[6];

    regnodes_PRT_MOD_VB_OBJ_IN_PMOD[0].setLabel(DEPLibEn.DEP_NEG); // was:("ADV");

    regnodes_PRT_MOD_VB_OBJ_IN_PMOD[1].setLabel(DEPLibEn.DEP_CCOMP); // was: ("VC");

    regnodes_PRT_MOD_VB_OBJ_IN_PMOD[3].setLabel(DEPLibEn.DEP_DOBJ); //("OBJ");

    regnodes_PRT_MOD_VB_OBJ_IN_PMOD[4].pos    = "IN";

    regnodes_PRT_MOD_VB_OBJ_IN_PMOD[5].setLabel(DEPLibEn.DEP_POBJ); // was: ("PMOD");   

    int cPRT_MOD_VB_OBJ_IN_PMOD = 1;

   

    // Recognizes phrases like "excluding FOCUS"

    DEPNode[] regnodes_VBN_SBJ = new DEPNode[2];

    regnodes_VBN_SBJ[0].pos    = "VBG";  // was VBN

    regnodes_VBN_SBJ[1].setLabel(DEPLibEn.DEP_DOBJ); // was: ("SBJ");

    int cVBN_SBJ = 0;

   

    // Recognizes phrases like "rules out FOCUS"

    DEPNode[] regnodes_PRT_rule_OBJ = new DEPNode[3];

    regnodes_PRT_rule_OBJ[0].setLabel(DEPLibEn.DEP_PRT); // was: ("PRT");

    regnodes_PRT_rule_OBJ[2].setLabel(DEPLibEn.DEP_DOBJ); //("OBJ");

    int cPRT_rule_OBJ = 1;

   

    //// Noun-ish rules

    // Recognizes phrases like "no pain"

    DEPNode[] regnodes_DT_NMOD   = new DEPNode[2];

    regnodes_DT_NMOD[0].setLabel(DEP_DET); // was: ("NMOD");

    regnodes_DT_NMOD[1].pos      = DependencyRegex.ANY_NOUN;

    int cDT_NMOD = 1;

    // Recognizes phrases like "without pain"

    DEPNode[] regnodes_IN_PMOD   = new DEPNode[2];

    regnodes_IN_PMOD[0].setLabel(DEP_PMOD); // was: ("PMOD");

    regnodes_IN_PMOD[1].pos      = DependencyRegex.ANY_NOUN;

    int cIN_PMOD = 0;

   

    // Recognizes phrases like "no history of FOCUS"

    DEPNode[] regnodes_DT_NN_IN_PMOD = new DEPNode[4];

//    regnodes_DT_NN_IN_PMOD[0].setLabel(DEP_PMOD + "|" + DEP_NMOD); // was: ("[NP]MOD");  // no convincing evidence that this can be restricted among the set of words listed above. (no would be det i think)

    regnodes_DT_NN_IN_PMOD[2].pos    = "IN";

    regnodes_DT_NN_IN_PMOD[3].setLabel(DEP_POBJ); // was: ("PMOD");

    int cDT_NN_IN_PMOD = 1;

   

//    DEPNode[] regnodes_JJNEG_COP_IN_PMOD = new DEPNode[4];

//    regnodes_JJNEG_COP_IN_PMOD[0]        = new DEPNode();

//    regnodes_JJNEG_COP_IN_PMOD[0].form   = DependencyRegex.fromSet(iv_negAdjectivesSet);

//    regnodes_JJNEG_COP_IN_PMOD[1]        = new DEPNode();

//    regnodes_JJNEG_COP_IN_PMOD[1].pos    = DependencyRegex.ANY_VERB; // banking that people don't use double negatives

//    regnodes_JJNEG_COP_IN_PMOD[2]        = new DEPNode();

//    regnodes_JJNEG_COP_IN_PMOD[2].form   = DependencyRegex.fromSet(iv_regPrepositionsSet);

//    regnodes_JJNEG_COP_IN_PMOD[2].pos    = "IN";

//    regnodes_JJNEG_COP_IN_PMOD[3]        = new DEPNode();

//    regnodes_JJNEG_COP_IN_PMOD[3].deprel = "PMOD";

//    int cJJNEG_COP_IN_PMOD = 1;

    // Recognizes negative-adjective phrases like "negative for carcinoma"

    DEPNode[] regnodes_JJNEG_AMOD_PMOD = new DEPNode[3];

    regnodes_JJNEG_AMOD_PMOD[1].pos    = "IN";

    regnodes_JJNEG_AMOD_PMOD[2].setLabel(DEP_POBJ); // was: ("PMOD");

    int cJJNEG_AMOD_PMOD = 0;

   

    // Recognizes negative-adjective phrases like "unlikely to have carcinoma"

    // FIXME - not sure this one works correctly -- this example parses weirdly in CVD.

    DEPNode[] regnodes_JJNEG_AMOD_IM_OBJ = new DEPNode[4];

    regnodes_JJNEG_AMOD_IM_OBJ[1].setLabel(DEP_AMOD); // was: ("AMOD");

    regnodes_JJNEG_AMOD_IM_OBJ[2].setLabel(DEP_INFMOD); // was: ("IM");

    regnodes_JJNEG_AMOD_IM_OBJ[3].setLabel(DEP_DOBJ); // was: ("OBJ");

    int cJJNEG_AMOD_IM_OBJ = 0;

   

    // Recognizes negative-adjective phrases like "unlikely carcinoma"

    DEPNode[] regnodes_JJNEG_NN = new DEPNode[2];

    regnodes_JJNEG_NN[1].pos    = DependencyRegex.ANY_NOUN;

    int cJJNEG_NN = 1;

   

   

    // Add the verb-ish rules to the set of regexes to search

      // Convert CAS data into structures usable by ClearNLP

      for (int i = 0; i < tokens.size(); i++) {

        BaseToken token = tokens.get(i);

        tree.add(node);

      }

      // Run parser and convert output back to CAS friendly data types

      postagger.process(tree);

     

      for (int i = 0; i < tokens.size(); i++) {

        BaseToken token = tokens.get(i);

        token.setPartOfSpeech(node.pos);

      }

     

    }

   

        // Set values in all the other nodes

        for (int i=1; i<clearTree.size(); i++) {

           

            ConllDependencyNode uimaNode = uimaNodes.get(i);

            uimaNode.setId(clearNode.id);

            uimaNode.setForm(clearNode.form);

            uimaNode.setLemma(clearNode.lemma);

            uimaNode.setCpostag(clearNode.pos);

            uimaNode.setPostag(clearNode.pos);

            uimaNode.setFeats("_");

            uimaNode.setPhead(null);

            uimaNode.setPdeprel("_");

            

       }

      // Convert CAS data into structures usable by ClearNLP

      for (int i = 0; i < tokens.size(); i++) {

        BaseToken token = tokens.get(i);

        String lemma = useLemmatizer ? lemmatizer.getLemma(token.getCoveredText(), token.getPartOfSpeech()) : token.getNormalizedForm();

        tree.add(node);

      }

      // Run parser and convert output back to CAS friendly data types

      parser.process(tree);

        String form = casDepNode.getForm();

        String pos = casDepNode.getPostag();

        String lemma = casDepNode.getLemma();

       

        tree.add(node);

      }

      for (int i=1; i<tree.size(); i++)

      {

        String  label = deprels[i-1];

        node.setHead(head, label);

      }

     

      tree.initSHeads();

    Map<Predicate, List<SemanticArgument>> predicateArguments = new HashMap<Predicate, List<SemanticArgument>>();

    for (int i = 1; i < tree.size(); i++) {

        // Every ClearNLP parserNode will contain an srlInfo field.

        BaseToken token = tokens.get(i - 1);

        String rolesetId;

       

        {

          if (!headIdToPredicate.containsKey(parserNode)) {

            // We have not encountered this predicate yet, so create it

            Predicate pred = this.createPredicate(jCas, rolesetId, token);

            headIdToPredicate.put(parserNode, pred);

            pred.setRelations(new EmptyFSList(jCas));

          }

        }

    }

   

   

    // Start at node 1, since node 0 is considered the head of the sentence

    for (int i = 1; i < tree.size(); i++) {

      // Every ClearNLP parserNode will contain an srlInfo field.

      BaseToken token = tokens.get(i - 1);

    

          Predicate predicate = headIdToPredicate.get(head.getNode());

          // Append this argument to the predicate's list of arguments

          if (!predicateArguments.containsKey(predicate)) {

            predicateArguments.put(predicate, new ArrayList<SemanticArgument>());