diff --git a/response_selectors/convers_evaluation_based_selector/tag_based_selection.py b/response_selectors/convers_evaluation_based_selector/tag_based_selection.py
index eea5effde2..ac8ad94330 100644
--- a/response_selectors/convers_evaluation_based_selector/tag_based_selection.py
+++ b/response_selectors/convers_evaluation_based_selector/tag_based_selection.py
@@ -177,10 +177,8 @@ def choose_best_with_scores(curr_cands_ids, curr_single_scores, candidates, bot_
 def get_main_info_annotations(annotated_utterance):
     intents = get_intents(annotated_utterance, which="all")
     topics = get_topics(annotated_utterance, which="all")
-    named_entities = [ent[0]["text"] for ent in annotated_utterance.get("annotations", {}).get("ner", []) if ent]
-    nounphrases = [
-        nounph for nounph in annotated_utterance.get("annotations", {}).get("spacy_nounphrases", []) if nounph
-    ]
+    named_entities = get_entities(annotated_utterance, only_named=True, with_labels=False)
+    nounphrases = get_entities(annotated_utterance, only_named=False, with_labels=False)
     return intents, topics, named_entities, nounphrases
 
 
@@ -627,7 +625,7 @@ def tag_based_response_selection(dialog, candidates, scores, confidences, bot_ut
             # -------------------- SUPER CONFIDENCE CASE HERE! --------------------
             categorized_hyps = add_to_top1_category(cand_id, categorized_hyps, _is_require_action_intent)
 
-        if cand_uttr["skill_name"] == "dff_grounding_skill" and "acknowledgement" in cand_uttr.get(
+        if cand_uttr["skill_name"] == "dff_grounding_skill" and ["acknowledgement"] == cand_uttr.get(
             "response_parts", []
         ):
             acknowledgement_hypothesis = deepcopy(cand_uttr)