initial commit

README.md

@@ -0,0 +1,182 @@
+---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+languages:
+- de
+licenses:
+- cc-by-4.0
+multilinguality:
+- monolingual
+paperswithcode_id: mobie
+pretty_name: MobIE
+size_categories:
+- 10K<n<100K
+source_datasets:
+- original
+task_categories:
+- structure-prediction
+task_ids:
+- named-entity-recognition
+---
+
+# Dataset Card for "MobIE"
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [https://github.com/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie)
+- **Repository:** [https://github.com/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie)
+- **Paper:** [https://aclanthology.org/2021.konvens-1.22/](https://aclanthology.org/2021.konvens-1.22/)
+- **Point of Contact:** See [https://github.com/dfki-nlp/mobie](https://github.com/dfki-nlp/mobie) 
+
+### Dataset Summary
+
+This script is for loading the MobIE dataset from https://github.com/dfki-nlp/mobie. 
+
+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.
+
+This version of the dataset loader provides NER tags only. NER tags use the `BIO` tagging scheme. 
+
+For more details see https://github.com/dfki-nlp/mobie and https://aclanthology.org/2021.konvens-1.22/.
+
+### Supported Tasks and Leaderboards
+
+- **Tasks:** Named Entity Recognition
+- **Leaderboards:**
+
+### Languages
+
+German
+
+## Dataset Structure
+
+### Data Instances
+
+- **Size of downloaded dataset files:** 7.8 MB
+- **Size of the generated dataset:** 1.7 MB
+- **Total amount of disk used:** 9.5 MB
+
+An example of 'train' looks as follows.
+
+```json
+{ 
+  'id': 'http://www.ndr.de/nachrichten/verkehr/index.html#2@2016-05-04T21:02:14.000+02:00',
+  'tokens': ['Vorsicht', 'bitte', 'auf', 'der', 'A28', 'Leer', 'Richtung', 'Oldenburg', 'zwischen', 'Zwischenahner', 'Meer', 'und', 'Neuenkruge', 'liegen', 'Gegenstände', '!'], 
+  'ner_tags': [0, 0, 0, 0, 19, 13, 0, 13, 0, 11, 12, 0, 11, 0, 0, 0]
+}
+```
+
+
+### Data Fields
+
+The data fields are the same among all splits.
+
+- `id`: a `string` feature.
+- `tokens`: a `list` of `string` features.
+- `ner_tags`: a `list` of classification labels, with possible values including `O` (0), `B-date` (1), `I-date` (2), `B-disaster-type` (3), `I-disaster-type` (4), ...
+
+### Data Splits
+
+|           | Train  |  Dev  | Test |
+| -----     | ------ | ----- | ---- |
+| MobIE     | 4785 | 1082 | 1210 |
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the source language producers?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Annotations
+
+#### Annotation process
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+#### Who are the annotators?
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Personal and Sensitive Information
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Discussion of Biases
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Other Known Limitations
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+
+### Licensing Information
+
+[CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/)
+
+### Citation Information
+
+```
+@inproceedings{hennig-etal-2021-mobie,
+    title = "{M}ob{IE}: A {G}erman Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain",
+    author = "Hennig, Leonhard  and
+      Truong, Phuc Tran  and
+      Gabryszak, Aleksandra",
+    booktitle = "Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)",
+    month = "6--9 " # sep,
+    year = "2021",
+    address = {D{\"u}sseldorf, Germany},
+    publisher = "KONVENS 2021 Organizers",
+    url = "https://aclanthology.org/2021.konvens-1.22",
+    pages = "223--227",
+}
+```
+
+
+### Contributions

mobie.py

@@ -0,0 +1,215 @@
+# coding=utf-8
+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""\
+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
+from json import JSONDecodeError, JSONDecoder
+
+import datasets
+
+
+_CITATION = """\
+        @inproceedings{hennig-etal-2021-mobie,
+    title = "{M}ob{IE}: A {G}erman Dataset for Named Entity Recognition, Entity Linking and Relation Extraction in the Mobility Domain",
+    author = "Hennig, Leonhard  and
+      Truong, Phuc Tran  and
+      Gabryszak, Aleksandra",
+    booktitle = "Proceedings of the 17th Conference on Natural Language Processing (KONVENS 2021)",
+    month = "6--9 " # sep,
+    year = "2021",
+    address = {D{\"u}sseldorf, Germany},
+    publisher = "KONVENS 2021 Organizers",
+    url = "https://aclanthology.org/2021.konvens-1.22",
+    pages = "223--227",
+}
+"""
+
+_DESCRIPTION = """\
+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."""
+
+_HOMEPAGE = "https://github.com/dfki-nlp/mobie"
+
+_LICENSE = "CC-BY 4.0"
+
+_URLs = {
+    "train": "https://github.com/DFKI-NLP/MobIE/raw/master/v1_20210811/train.jsonl.gz",
+    "dev": "https://github.com/DFKI-NLP/MobIE/raw/master/v1_20210811/dev.jsonl.gz",
+    "test": "https://github.com/DFKI-NLP/MobIE/raw/master/v1_20210811/test.jsonl.gz",
+}
+
+
+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"""
+
+    VERSION = datasets.Version("1.0.0")
+
+    # This is an example of a dataset with multiple configurations.
+    # If you don't want/need to define several sub-sets in your dataset,
+    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
+
+    # If you need to make complex sub-parts in the datasets with configurable options
+    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
+    # BUILDER_CONFIG_CLASS = MyBuilderConfig
+
+    # You will be able to load one or the other configurations in the following list with
+    # 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"),
+    ]
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "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",
+                        ]
+                    )
+                ),
+            }
+        )
+
+        return datasets.DatasetInfo(
+            # This is the description that will appear on the datasets page.
+            description=_DESCRIPTION,
+            # This defines the different columns of the dataset and their types
+            features=features,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage=_HOMEPAGE,
+            # License for the dataset if available
+            license=_LICENSE,
+            # Citation for the dataset
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+
+        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
+        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
+        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
+        data_dir = dl_manager.download_and_extract(_URLs)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": data_dir["train"], "split": "train"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": data_dir["test"], "split": "test"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                # These kwargs will be passed to _generate_examples
+                gen_kwargs={"filepath": data_dir["dev"], "split": "dev"},
+            ),
+        ]
+
+    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"]])
+                    # convert to BIO
+                    if x["ner"]["string"] != 'O':
+                        lbls.append("B-" + x["ner"]["string"] if x["span"]["start"] in entity_starts else "I-" + x["ner"]["string"])
+                    else:
+                        lbls.append(x["ner"]["string"])
+
+                yield sid, {
+                    "id": sid,
+                    "tokens": toks,
+                    "ner_tags": lbls,
+                }
+