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README.md

@@ -1,84 +1,86 @@
 ---
 dataset_info:
   features:
-    - name: text
-      dtype: string
-    - name: tokens
-      sequence: string
-    - name: tokens_offsets
-      sequence:
-        sequence: int32
-    - name: clinical_entity_tags
-      sequence:
-        class_label:
-          names:
-            "0": O
-            "1": B-CLINENTITY
-            "2": I-CLINENTITY
-    - name: temporal_information_tags
-      sequence:
-        class_label:
-          names:
-            "0": O
-            "1": B-EVENT
-            "2": B-ACTOR
-            "3": B-BODYPART
-            "4": B-TIMEX3
-            "5": B-RML
-            "6": I-EVENT
-            "7": I-ACTOR
-            "8": I-BODYPART
-            "9": I-TIMEX3
-            "10": I-RML
+  - name: text
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: tokens_offsets
+    sequence:
+      sequence: int32
+  - name: clinical_entity_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-CLINENTITY
+          '2': I-CLINENTITY
+  - name: clinical_entity_cuid
+    sequence: string
+  - name: temporal_information_tags
+    sequence:
+      class_label:
+        names:
+          '0': O
+          '1': B-EVENT
+          '2': B-ACTOR
+          '3': B-BODYPART
+          '4': B-TIMEX3
+          '5': B-RML
+          '6': I-EVENT
+          '7': I-ACTOR
+          '8': I-BODYPART
+          '9': I-TIMEX3
+          '10': I-RML
   config_name: e3c
   splits:
-    - name: en.layer1
-      num_bytes: 1273610
-      num_examples: 1520
-    - name: en.layer2
-      num_bytes: 2550153
-      num_examples: 2873
-    - name: en.layer2.validation
-      num_bytes: 290108
-      num_examples: 334
-    - name: es.layer1
-      num_bytes: 1252571
-      num_examples: 1134
-    - name: es.layer2
-      num_bytes: 2498266
-      num_examples: 2347
-    - name: es.layer2.validation
-      num_bytes: 275770
-      num_examples: 261
-    - name: eu.layer1
-      num_bytes: 1519021
-      num_examples: 3126
-    - name: eu.layer2
-      num_bytes: 839955
-      num_examples: 1594
-    - name: eu.layer2.validation
-      num_bytes: 220097
-      num_examples: 468
-    - name: fr.layer1
-      num_bytes: 1258738
-      num_examples: 1109
-    - name: fr.layer2
-      num_bytes: 2628628
-      num_examples: 2389
-    - name: fr.layer2.validation
-      num_bytes: 282527
-      num_examples: 293
-    - name: it.layer1
-      num_bytes: 1276534
-      num_examples: 1146
-    - name: it.layer2
-      num_bytes: 2641257
-      num_examples: 2436
-    - name: it.layer2.validation
-      num_bytes: 286702
-      num_examples: 275
+  - name: en.layer1
+    num_bytes: 1632165
+    num_examples: 1520
+  - name: en.layer2
+    num_bytes: 3263885
+    num_examples: 2873
+  - name: en.layer2.validation
+    num_bytes: 371196
+    num_examples: 334
+  - name: es.layer1
+    num_bytes: 1599169
+    num_examples: 1134
+  - name: es.layer2
+    num_bytes: 3192361
+    num_examples: 2347
+  - name: es.layer2.validation
+    num_bytes: 352193
+    num_examples: 261
+  - name: eu.layer1
+    num_bytes: 1931109
+    num_examples: 3126
+  - name: eu.layer2
+    num_bytes: 1066405
+    num_examples: 1594
+  - name: eu.layer2.validation
+    num_bytes: 279306
+    num_examples: 468
+  - name: fr.layer1
+    num_bytes: 1610663
+    num_examples: 1109
+  - name: fr.layer2
+    num_bytes: 3358033
+    num_examples: 2389
+  - name: fr.layer2.validation
+    num_bytes: 361816
+    num_examples: 293
+  - name: it.layer1
+    num_bytes: 1633613
+    num_examples: 1146
+  - name: it.layer2
+    num_bytes: 3373977
+    num_examples: 2436
+  - name: it.layer2.validation
+    num_bytes: 366932
+    num_examples: 275
   download_size: 230213492
-  dataset_size: 19093937
+  dataset_size: 24392823
 ---
 
 # Dataset Card for E3C
@@ -107,4 +109,4 @@ information about clinical entities based on medical taxonomies, to be used for
     url = {https://uts.nlm.nih.gov/uts/umls/home},
     year = {2021},
 }
-```
+```

e3c.py

@@ -70,6 +70,9 @@ class E3C(datasets.GeneratorBasedBuilder):
                         ],
                     ),
                 ),
+                "clinical_entity_cuid": datasets.Sequence(
+                    datasets.Value("string"),
+                ),
                 "temporal_information_tags": datasets.Sequence(
                     datasets.features.ClassLabel(
                         names=[
@@ -285,6 +288,25 @@ class E3C(datasets.GeneratorBasedBuilder):
     def get_annotations(entities: ResultSet, text: str) -> list:
         """Extract the offset, the text and the type of the entity.
 
+        Args:
+            entities: The entities to extract.
+            text: The text of the document.
+        Returns:
+            A list of list containing the offset, the text and the type of the entity.
+        """
+        return [
+
+            [
+                int(entity.get("begin")),
+                int(entity.get("end")),
+                text[int(entity.get("begin")) : int(entity.get("end"))],
+            ]
+            for entity in entities
+        ]
+
+    def get_clinical_annotations(self, entities: ResultSet, text: str) -> list:
+        """Extract the offset, the text and the type of the entity.
+
         Args:
             entities: The entities to extract.
             text: The text of the document.
@@ -296,6 +318,7 @@ class E3C(datasets.GeneratorBasedBuilder):
                 int(entity.get("begin")),
                 int(entity.get("end")),
                 text[int(entity.get("begin")) : int(entity.get("end"))],
+                entity.get("entityID"),
             ]
             for entity in entities
         ]
@@ -320,7 +343,7 @@ class E3C(datasets.GeneratorBasedBuilder):
                     soup = BeautifulSoup(soup_file, "xml")
                     text = soup.find("cas:Sofa").get("sofaString")
                     yield {
-                        "CLINENTITY": self.get_annotations(
+                        "CLINENTITY": self.get_clinical_annotations(
                             soup.find_all("custom:CLINENTITY"), text
                         ),
                         "EVENT": self.get_annotations(soup.find_all("custom:EVENT"), text),
@@ -362,6 +385,7 @@ class E3C(datasets.GeneratorBasedBuilder):
                     [token[0] - sentence[0], token[1] - sentence[0]] for token in filtered_tokens
                 ]
                 clinical_labels = ["O"] * len(filtered_tokens)
+                clinical_cuid = ["CUI_LESS"] * len(filtered_tokens)
                 temporal_information_labels = ["O"] * len(filtered_tokens)
                 for entity_type in [
                     "CLINENTITY",
@@ -386,6 +410,7 @@ class E3C(datasets.GeneratorBasedBuilder):
                                         clinical_labels[idx_token] = f"B-{entity_type}"
                                     else:
                                         clinical_labels[idx_token] = f"I-{entity_type}"
+                                    clinical_cuid[idx_token] = entities[-1]
                                 else:
                                     if idx_token == annotated_tokens[0]:
                                         temporal_information_labels[idx_token] = f"B-{entity_type}"
@@ -395,7 +420,12 @@ class E3C(datasets.GeneratorBasedBuilder):
                     "text": sentence[-1],
                     "tokens": list(map(lambda token: token[2], filtered_tokens)),
                     "clinical_entity_tags": clinical_labels,
+                    "clinical_entity_cuid": clinical_cuid,
                     "temporal_information_tags": temporal_information_labels,
                     "tokens_offsets": tokens_offsets,
                 }
                 guid += 1
+
+if __name__ == "__main__":
+    builder = E3C()
+    builder.download_and_prepare()