SzegedNER Dataset

README.md

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+---
+annotations_creators:
+- expert-generated
+language:
+- hu
+language_creators:
+- other
+license: []
+multilinguality:
+- monolingual
+paperswithcode_id: null
+pretty_name: SzegedNER
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- hungarian
+- szeged
+- ner
+task_categories:
+- token-classification
+task_ids:
+- named-entity-recognition
+---
+
+# Introduction
+
+The recognition and classification of proper nouns and names in plain text is of key importance in Natural Language Processing (NLP) as it has a beneficial effect on the performance of various types of applications, including Information Extraction, Machine Translation, Syntactic Parsing/Chunking, etc.
+
+## Corpus of Business Newswire Texts (business)
+
+The Named Entity Corpus for Hungarian is a subcorpus of the Szeged Treebank, which contains full syntactic annotations done manually by linguist experts. A significant part of these texts has been annotated with Named Entity class labels in line with the annotation standards used on the CoNLL-2003 shared task.
+
+Statistical data on Named Entities occurring in the corpus:
+
+```
+       | tokens | phrases
+------ | ------ | -------
+non NE | 200067 | 	 
+PER    | 1921   | 982
+ORG    | 20433  | 10533
+LOC    | 1501   | 1294
+MISC   | 2041   | 1662
+```
+
+### Reference
+
+> György Szarvas, Richárd Farkas, László Felföldi, András Kocsor, János Csirik: Highly accurate Named Entity corpus for Hungarian. International Conference on Language Resources and Evaluation 2006, Genova (Italy)
+
+## Criminal NE corpus (criminal)
+
+The Hungarian National Corpus and its Heti Világgazdaság (HVG) subcorpus provided the basis for corpus text selection: articles related to the topic of financially liable offences were selected and annotated for the categories person, organization, location and miscellaneous.
+There are two annotated versions of the corpus. When preparing the tag-for-meaning annotation, our linguists took into consideration the context in which the Named Entity under investigation occurred, thus, it was not the primary sense of the Named Entity that determined the tag (e.g. Manchester=LOC) but its contextual reference (e.g. Manchester won the Premier League=ORG). As for tag-for-tag annotation, these cases were not differentiated: tags were always given on the basis of the primary sense.
+
+Statistical data on Named Entities occurring in the corpus:
+
+```
+       | tag-for-meaning | tag-for-tag
+------ | --------------- | -----------
+non NE | 200067          | 	 
+PER    | 8101            | 8121
+ORG    | 8782            | 9480
+LOC    | 5049            | 5391
+MISC   | 1917            | 854
+```
+
+## Metadata
+
+dataset_info:
+- config_name: business
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          0: O
+          1: B-PER
+          2: I-PER
+          3: B-ORG
+          4: I-ORG
+          5: B-LOC
+          6: I-LOC
+          7: B-MISC
+          8: I-MISC
+  - name: document_id
+    dtype: string
+  - name: sentence_id
+    dtype: string
+  splits:
+  - name: original
+    num_bytes: 4452207
+    num_examples: 9573
+  - name: test
+    num_bytes: 856798
+    num_examples: 1915
+  - name: train
+    num_bytes: 3171931
+    num_examples: 6701
+  - name: validation
+    num_bytes: 423478
+    num_examples: 957
+  download_size: 0
+  dataset_size: 8904414
+- config_name: criminal
+  features:
+  - name: id
+    dtype: string
+  - name: tokens
+    sequence: string
+  - name: ner_tags
+    sequence:
+      class_label:
+        names:
+          0: O
+          1: B-PER
+          2: I-PER
+          3: B-ORG
+          4: I-ORG
+          5: B-LOC
+          6: I-LOC
+          7: B-MISC
+          8: I-MISC
+  - name: document_id
+    dtype: string
+  - name: sentence_id
+    dtype: string
+  splits:
+  - name: original
+    num_bytes: 2807970
+    num_examples: 5375
+  - name: test
+    num_bytes: 520959
+    num_examples: 1089
+  - name: train
+    num_bytes: 1989662
+    num_examples: 3760
+  - name: validation
+    num_bytes: 297349
+    num_examples: 526
+  download_size: 0
+  dataset_size: 5615940
+

SzegedNER.py

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+from datasets import BuilderConfig, Version, GeneratorBasedBuilder, DatasetInfo, Features, Value, \
+    Sequence, ClassLabel, DownloadManager, SplitGenerator, Split
+import datasets
+import os
+import textwrap
+import csv
+from ast import literal_eval
+
+
+_DESCRIPTION = """
+The recognition and classification of proper nouns and names in plain text is of key importance in Natural Language 
+Processing (NLP) as it has a beneficial effect on the performance of various types of applications, including 
+Information Extraction, Machine Translation, Syntactic Parsing/Chunking, etc."""
+_CITATION = """"""
+_FEATURES = Features(
+    {
+        "id": Value("int32"),
+        "tokens": Sequence(Value("string")),
+        "ner": Sequence(
+            ClassLabel(
+                names=[
+                    "O",
+                    "B-PER",
+                    "I-PER",
+                    "B-ORG",
+                    "I-ORG",
+                    "B-LOC",
+                    "I-LOC",
+                    "B-MISC",
+                    "I-MISC",
+                ]
+            )
+        ),
+        "document_id": Value("int32"),
+        "sentence_id": Value("int32")
+    }
+)
+
+
+class SzegedNERConfig(BuilderConfig):
+    """BuilderConfig for SzegedNER."""
+
+    def __init__(
+        self,
+        features,
+        label_column,
+        data_dir,
+        citation,
+        url,
+        process_label=lambda x: x,
+        **kwargs,
+    ):
+        super(SzegedNERConfig, self).__init__(version=Version("1.0.0", ""), **kwargs)
+        self.features = features
+        self.label_column = label_column
+        self.data_dir = data_dir
+        self.citation = citation
+        self.url = url
+        self.process_label = process_label
+
+
+class SzegedNER(GeneratorBasedBuilder):
+    """SzegedNER datasets."""
+
+    BUILDER_CONFIGS = [
+        SzegedNERConfig(
+            name="business",
+            description=textwrap.dedent(
+                """\
+            The Named Entity Corpus for Hungarian is a subcorpus of the Szeged Treebank, which contains full syntactic 
+            annotations done manually by linguist experts. A significant part of these texts has been annotated with 
+            Named Entity class labels in line with the annotation standards used on the CoNLL-2003 shared task."""
+            ),
+            features=_FEATURES,
+            label_column="ner_tags",
+            data_dir="data/business/",
+            citation=textwrap.dedent(_CITATION),
+            url="https://rgai.inf.u-szeged.hu/node/130"
+        ),
+        SzegedNERConfig(
+            name="criminal",
+            description=textwrap.dedent(
+                """\
+            The Hungarian National Corpus and its Heti Világgazdaság (HVG) subcorpus provided the basis for corpus text 
+            selection: articles related to the topic of financially liable offences were selected and annotated for the 
+            categories person, organization, location and miscellaneous. There are two annotated versions of the corpus. 
+            When preparing the tag-for-meaning annotation, our linguists took into consideration the context in which 
+            the Named Entity under investigation occurred, thus, it was not the primary sense of the Named Entity that 
+            determined the tag (e.g. Manchester=LOC) but its contextual reference (e.g. Manchester won the Premier 
+            League=ORG). As for tag-for-tag annotation, these cases were not differentiated: tags were always given on 
+            the basis of the primary sense."""
+            ),
+            features=_FEATURES,
+            label_column="ner_tags",
+            data_dir="data/criminal/",
+            citation=textwrap.dedent(_CITATION),
+            url="https://rgai.inf.u-szeged.hu/node/130"
+        )
+    ]
+
+    def _info(self):
+        return DatasetInfo(
+            description=self.config.description,
+            features=self.config.features,
+            homepage=self.config.url,
+            citation=self.config.citation,
+        )
+
+    def _split_generators(self, dl_manager: DownloadManager):
+        url = f"{self.base_path}{self.config.data_dir}"
+
+        path = dl_manager.download({key: f"{url}{key}.csv" for key in ["train", "validation", "test", "all"]})
+        return [
+            SplitGenerator(
+                name=Split.TRAIN,
+                gen_kwargs={"split_key": "train", "data_file": path['train']},
+            ),
+            SplitGenerator(
+                name=Split.VALIDATION,
+                gen_kwargs={"split_key": "validation", "data_file": path['validation']},
+            ),
+            SplitGenerator(
+                name=Split.TEST,
+                gen_kwargs={"split_key": "test", "data_file": path['test']},
+            )
+        ]
+
+    def _generate_examples(self, data_file, split_key, **kwargs):
+        with open(data_file, encoding="utf8") as f:
+            reader = csv.DictReader(f, delimiter=",", quoting=csv.QUOTE_MINIMAL)
+            for n, row in enumerate(reader):
+                labels = literal_eval(row['ner'])
+                tokens = literal_eval(row['tokens'])
+                if len(labels) != len(tokens):
+                    raise ValueError("Number of tokens and labels does not match")
+                yield n, {
+                    "id": int(row['id']),
+                    "tokens": tokens,
+                    "ner": labels,
+                    "document_id": int(row['document_id']),
+                    "sentence_id": int(row['sentence_id'])
+                }

data/business/test.csv

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+oid sha256:925a23ac30f7fb533490aae29b271b49e63b98e51feffb537b9584edd1b3546a
+size 366898

data/business/train.csv

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+oid sha256:6e2fe5d597f812254bf144c9e0f60585a104bec30129ca268b54f62241cf5d11
+size 3228046

data/business/validation.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:45fcb36de69c98b51ad58bfce099c58218bef06cff112beb2d46ba77b26b8b70
+size 187647

data/criminal/test.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:dccc2c08e2a7f111b16863ce023d333aad532fac5873cebdcb3a1e63ff38437c
+size 387511

data/criminal/train.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3b1d50794cf3e18bec248ab934860e665db4c1be4070555172064d5736554208
+size 1748329

data/criminal/validation.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d3aea95adb9689c814e2706db52acf11fe61f0eca6a061b3a60db3b35755ac38
+size 221219