Add configs for entity linking, relation extraction and event extraction (sentence level)

mobie.py

@@ -16,6 +16,7 @@
 MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities. The dataset consists of 3,232 social media texts and traffic reports with 91K tokens, and contains 20.5K annotated entities, 13.1K of which are linked to a knowledge base. A subset of the dataset is human-annotated with seven mobility-related, n-ary relation types, while the remaining documents are annotated using a weakly-supervised labeling approach implemented with the Snorkel framework. The dataset combines annotations for NER, EL and RE, and thus can be used for joint and multi-task learning of these fundamental information extraction tasks."""
 
 import re
+import json
 from json import JSONDecodeError, JSONDecoder
 
 import datasets
@@ -51,6 +52,30 @@ _URLs = {
 }
 
 
+def simplify_dict(d, remove_attribute=True):
+    if isinstance(d, dict):
+        new_dict = {}
+        for k, v in d.items():
+            if remove_attribute and k == "attributes":
+                continue
+            if isinstance(v, dict) and len(v) == 1:
+                if "string" in v:
+                    new_dict[k] = v["string"]
+                elif "map" in v:
+                    new_dict[k] = v["map"]
+                elif "array" in v:
+                    new_dict[k] = simplify_dict(v["array"])
+                else:
+                    new_dict[k] = simplify_dict(v)
+            else:
+                new_dict[k] = simplify_dict(v)
+        return new_dict
+    elif isinstance(d, list):
+        return [simplify_dict(x) for x in d]
+    else:
+        return d
+
+
 class Mobie(datasets.GeneratorBasedBuilder):
     """MobIE is a German-language dataset which is human-annotated with 20 coarse- and fine-grained entity types and entity linking information for geographically linkable entities"""
 
@@ -68,63 +93,154 @@ class Mobie(datasets.GeneratorBasedBuilder):
     # data = datasets.load_dataset('my_dataset', 'first_domain')
     # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="mobie-v1_20210811", version=VERSION, description="MobIE V1"),
+        datasets.BuilderConfig(name="ner", version=VERSION, description="MobIE V1 NER"),
+        datasets.BuilderConfig(name="el", version=VERSION, description="MobIE V1 Entity Linking"),
+        datasets.BuilderConfig(name="re", version=VERSION, description="MobIE V1 Relation Extraction"),
+        datasets.BuilderConfig(name="ee", version=VERSION, description="MobIE V1 Event Extraction"),
     ]
+    DEFAULT_CONFIG_NAME = "ner"
 
     def _info(self):
-        features = datasets.Features(
+        labels = [
+            "date",
+            "disaster-type",
+            "distance",
+            "duration",
+            "event-cause",
+            "location",
+            "location-city",
+            "location-route",
+            "location-stop",
+            "location-street",
+            "money",
+            "number",
+            "organization",
+            "organization-company",
+            "org-position",
+            "percent",
+            "person",
+            "set",
+            "time",
+            "trigger",
+        ]
+        concept_mentions = [
             {
                 "id": datasets.Value("string"),
-                "tokens": datasets.Sequence(datasets.Value("string")),
-                "ner_tags": datasets.Sequence(
-                    datasets.features.ClassLabel(
-                        names=[
-                            "O",
-                            "B-date",
-                            "I-date",
-                            "B-disaster-type",
-                            "I-disaster-type",
-                            "B-distance",
-                            "I-distance",
-                            "B-duration",
-                            "I-duration",
-                            "B-event-cause",
-                            "I-event-cause",
-                            "B-location",
-                            "I-location",
-                            "B-location-city",
-                            "I-location-city",
-                            "B-location-route",
-                            "I-location-route",
-                            "B-location-stop",
-                            "I-location-stop",
-                            "B-location-street",
-                            "I-location-street",
-                            "B-money",
-                            "I-money",
-                            "B-number",
-                            "I-number",
-                            "B-organization",
-                            "I-organization",
-                            "B-organization-company",
-                            "I-organization-company",
-                            "B-org-position",
-                            "I-org-position",
-                            "B-percent",
-                            "I-percent",
-                            "B-person",
-                            "I-person",
-                            "B-set",
-                            "I-set",
-                            "B-time",
-                            "I-time",
-                            "B-trigger",
-                            "I-trigger",
-                        ]
-                    )
-                ),
+                "text": datasets.Value("string"),
+                "start": datasets.Value("int32"),
+                "end": datasets.Value("int32"),
+                "type": datasets.features.ClassLabel(names=labels),
+                "refids": [
+                    {
+                        "key": datasets.Value("string"),
+                        "value": datasets.Value("string")
+                    }
+                ]
             }
-        )
+        ]
+        if self.config.name == "ner":
+            prefixes = ["B", "I"]
+
+            names = ["O"] + [f"{prefix}-{label}" for prefix in prefixes for label in labels]
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=names
+                        )
+                    ),
+                }
+            )
+        elif self.config.name == "el":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "concept_mentions": concept_mentions
+                }
+            )
+        elif self.config.name == "re":
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "concept_mentions": concept_mentions,
+                    "relation_mentions": [
+                        {
+                            "id": datasets.Value("string"),
+                            "trigger": {
+                                "id": datasets.Value("string"),
+                                "text": datasets.Value("string"),
+                                "start": datasets.Value("int32"),
+                                "end": datasets.Value("int32")
+                            },
+                            "argument": {
+                                "id": datasets.Value("string"),
+                                "text": datasets.Value("string"),
+                                "start": datasets.Value("int32"),
+                                "end": datasets.Value("int32")
+                            },
+                            "type": datasets.features.ClassLabel(
+                                names=[
+                                    "no_arg", "location", "delay", "direction", "start_loc", "end_loc",
+                                    "start_date", "end_date", "cause", "jam_length", "route"
+                                ]
+                            ),
+                            "event_type": datasets.features.ClassLabel(
+                                names=[
+                                    "O", "Accident", "CanceledRoute", "CanceledStop", "Delay", "Obstruction",
+                                    "RailReplacementService", "TrafficJam"
+                                ]
+                            )
+                        }
+                    ]
+                }
+            )
+        elif self.config.name == "ee":
+            # Inspired by https://github.com/nlpcl-lab/ace2005-preprocessing?tab=readme-ov-file#format
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "entity_mentions": concept_mentions,
+                    "event_mentions": [
+                        {
+                            "id": datasets.Value("string"),
+                            "trigger": {
+                                "id": datasets.Value("string"),
+                                "text": datasets.Value("string"),
+                                "start": datasets.Value("int32"),
+                                "end": datasets.Value("int32"),
+                            },
+                            "arguments": [{
+                                "id": datasets.Value("string"),
+                                "text": datasets.Value("string"),
+                                "start": datasets.Value("int32"),
+                                "end": datasets.Value("int32"),
+                                "role": datasets.features.ClassLabel(
+                                    names=[
+                                        "no_arg", "location", "delay", "direction", "start_loc", "end_loc",
+                                        "start_date", "end_date", "cause", "jam_length", "route"
+                                    ]
+                                ),
+                                "type": datasets.features.ClassLabel(
+                                    names=labels
+                                )
+                            }],
+                            "event_type": datasets.features.ClassLabel(
+                                names=[
+                                    "O", "Accident", "CanceledRoute", "CanceledStop", "Delay", "Obstruction",
+                                    "RailReplacementService", "TrafficJam"
+                                ]
+                            ),
+                        }
+                    ]
+                }
+            )
+        else:
+            raise ValueError("Invalid configuration name")
 
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
@@ -171,41 +287,173 @@ class Mobie(datasets.GeneratorBasedBuilder):
     def _generate_examples(self, filepath, split):
         """Yields examples."""
 
-        NOT_WHITESPACE = re.compile(r"[^\s]")
-
-        def decode_stacked(document, pos=0, decoder=JSONDecoder()):
-            while True:
-                match = NOT_WHITESPACE.search(document, pos)
-                if not match:
-                    return
-                pos = match.start()
-                try:
-                    obj, pos = decoder.raw_decode(document, pos)
-                except JSONDecodeError:
-                    raise
-                yield obj
-
-        with open(filepath, encoding="utf-8") as f:
-            raw = f.read()
-
-        for doc in decode_stacked(raw):
-            text = doc["text"]["string"]
-
-
-            entity_starts = []
-            for m in doc["conceptMentions"]["array"]:
-                entity_starts.append(m["span"]["start"])
-            for s in doc["sentences"]["array"]:
-                toks = []
-                lbls = []
-                sid = s["id"]
-                for x in s["tokens"]["array"]:
-                    toks.append(text[x["span"]["start"] : x["span"]["end"]])
-                    lbls.append(x["ner"]["string"])
-
-                yield sid, {
-                    "id": sid,
-                    "tokens": toks,
-                    "ner_tags": lbls,
-                }
+        if self.config.name == "ner":
+            NOT_WHITESPACE = re.compile(r"[^\s]")
+
+            def decode_stacked(document, pos=0, decoder=JSONDecoder()):
+                while True:
+                    match = NOT_WHITESPACE.search(document, pos)
+                    if not match:
+                        return
+                    pos = match.start()
+                    try:
+                        obj, pos = decoder.raw_decode(document, pos)
+                    except JSONDecodeError:
+                        raise
+                    yield obj
+
+            with open(filepath, encoding="utf-8") as f:
+                raw = f.read()
+
+            for doc in decode_stacked(raw):
+                text = doc["text"]["string"]
+
+                # entity_starts = []
+                # for m in doc["conceptMentions"]["array"]:
+                #     entity_starts.append(m["span"]["start"])
+                for s in doc["sentences"]["array"]:
+                    toks = []
+                    lbls = []
+                    sid = s["id"]
+                    for x in s["tokens"]["array"]:
+                        toks.append(text[x["span"]["start"]: x["span"]["end"]])
+                        lbls.append(x["ner"]["string"])
 
+                    yield sid, {
+                        "id": sid,
+                        "tokens": toks,
+                        "ner_tags": lbls,
+                    }
+        else:
+            example_idx = 0
+            with open(filepath, encoding="utf-8") as f:
+                for line in f:
+                    doc = json.loads(line)
+                    doc = simplify_dict(doc)
+                    text = doc["text"]
+                    for sentence in doc["sentences"]:
+                        sentence_id = sentence["id"]
+                        sentence_start = sentence["span"]["start"]
+                        mobie_cms = sentence["conceptMentions"]
+                        concept_mentions = []
+                        for cm in mobie_cms:
+                            cm_start = cm["span"]["start"]
+                            cm_end = cm["span"]["end"]
+                            cm_text = text[cm_start:cm_end]
+                            concept_mentions.append({
+                                "id": cm["id"],
+                                "text": cm_text,
+                                "start": cm_start - sentence_start,
+                                "end": cm_end - sentence_start,
+                                "type": cm["type"],
+                                "refids": [
+                                    {
+                                        "key": refid["key"], 
+                                        "value": refid["value"]
+                                    } for refid in cm["refids"]
+                                ] if "refids" in cm and cm["refids"] else []
+                            })
+                        if self.config.name == "el":
+                            yield sentence_id, {
+                                "id": sentence_id,
+                                "text": text,
+                                "concept_mentions": concept_mentions
+                            }
+                        elif self.config.name == "re":
+                            mobie_rms = sentence["relationMentions"]
+                            if not mobie_rms:
+                                continue
+                            relation_mentions = []
+                            for rm in mobie_rms:
+                                # Find trigger in rm["args"]
+                                trigger = None
+                                for arg in rm["args"]:
+                                    if arg["role"] == "trigger":
+                                        trigger = arg
+                                        break
+                                if trigger is None:
+                                    continue
+                                trigger_start = trigger["conceptMention"]["span"]["start"]
+                                trigger_end = trigger["conceptMention"]["span"]["end"]
+                                trigger_text = text[trigger_start:trigger_end]
+                                for arg in rm["args"]:
+                                    if arg["role"] == "trigger":
+                                        continue
+                                    argument_start = arg["conceptMention"]["span"]["start"]
+                                    argument_end = arg["conceptMention"]["span"]["end"]
+                                    argument_text = text[argument_start:argument_end]
+                                    relation_mentions.append({
+                                        "id": f"{sentence_id}-{example_idx}",
+                                        "trigger": {
+                                            "id": trigger["conceptMention"]["id"],
+                                            "text": trigger_text,
+                                            "start": trigger_start - sentence_start,
+                                            "end": trigger_end - sentence_start
+                                        },
+                                        "argument": {
+                                            "id": arg["conceptMention"]["id"],
+                                            "text": argument_text,
+                                            "start": argument_start - sentence_start,
+                                            "end": argument_end - sentence_start
+                                        },
+                                        "type": arg["role"],
+                                        "event_type": rm["name"]
+                                    })
+                                    yield f"{sentence_id}_{example_idx}", {
+                                        "id": f"{sentence_id}_{example_idx}",
+                                        "text": text,
+                                        "concept_mentions": concept_mentions,
+                                        "relation_mentions": relation_mentions
+                                    }
+                                    example_idx += 1
+                        elif self.config.name == "ee":
+                            mobie_rms = sentence["relationMentions"]
+                            if not mobie_rms:
+                                continue
+                            event_mentions = []
+                            for rm in mobie_rms:
+                                # Find trigger in rm["args"]
+                                trigger = None
+                                for arg in rm["args"]:
+                                    if arg["role"] == "trigger":
+                                        trigger = arg
+                                        break
+                                if trigger is None:
+                                    continue
+                                trigger_start = trigger["conceptMention"]["span"]["start"]
+                                trigger_end = trigger["conceptMention"]["span"]["end"]
+                                trigger_text = text[trigger_start:trigger_end]
+                                args = []
+                                for arg in rm["args"]:
+                                    if arg["role"] == "trigger":
+                                        continue
+                                    arg_start = arg["conceptMention"]["span"]["start"]
+                                    arg_end = arg["conceptMention"]["span"]["end"]
+                                    arg_text = text[arg_start:arg_end]
+                                    args.append({
+                                        "id": arg["conceptMention"]["id"],
+                                        "text": arg_text,
+                                        "start": arg_start - sentence_start,
+                                        "end": arg_end - sentence_start,
+                                        "role": arg["role"],
+                                        "type": arg["conceptMention"]["type"]
+                                    })
+                                event_mentions.append({
+                                    "id": rm["id"],
+                                    "trigger": {
+                                        "id": trigger["conceptMention"]["id"],
+                                        "text": trigger_text,
+                                        "start": trigger_start - sentence_start,
+                                        "end": trigger_end - sentence_start
+                                    },
+                                    "arguments": args,
+                                    "event_type": rm["name"]
+                                })
+                            yield sentence_id, {
+                                "id": sentence_id,
+                                "text": text,
+                                "entity_mentions": concept_mentions,
+                                "event_mentions": event_mentions
+                            }
+                        else:
+                            raise ValueError("Invalid configuration name")