add reader

named_timexes.py

@@ -0,0 +1,148 @@
+# coding=utf-8
+# Copyright 2022 Leon Derczynski
+#
+# 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.
+
+# Lint as: python3
+"""Named Temporal Expressions corpus (English)"""
+
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@inproceedings{brucato-etal-2013-recognising,
+    title = "Recognising and Interpreting Named Temporal Expressions",
+    author = "Brucato, Matteo  and
+      Derczynski, Leon  and
+      Llorens, Hector  and
+      Bontcheva, Kalina  and
+      Jensen, Christian S.",
+    booktitle = "Proceedings of the International Conference Recent Advances in Natural Language Processing {RANLP} 2013",
+    month = sep,
+    year = "2013",
+    address = "Hissar, Bulgaria",
+    publisher = "INCOMA Ltd. Shoumen, BULGARIA",
+    url = "https://aclanthology.org/R13-1015",
+    pages = "113--121",
+}
+"""
+
+_DESCRIPTION = """\
+This is a dataset annotated for _named temporal expression_ chunks.
+
+The
+commonest temporal expressions typically
+contain date and time words, like April or
+hours. Research into recognising and interpreting these typical expressions is mature in many languages. However, there is
+a class of expressions that are less typical,
+very varied, and difficult to automatically
+interpret. These indicate dates and times,
+but are harder to detect because they often do not contain time words and are not
+used frequently enough to appear in conventional temporally-annotated corpora –
+for example *Michaelmas* or *Vasant Panchami*.
+
+For more details see [https://aclanthology.org/R13-1015.pdf](https://aclanthology.org/R13-1015.pdf)
+"""
+
+_URL = "http://www.derczynski.com/resources/named_timex.tar.bz2"
+_TRAIN_FILE = "ntimex-train.conll"
+_TEST_FILE = "ntimex-eval.conll"
+
+class NamedTimexesConfig(datasets.BuilderConfig):
+    """BuilderConfig for NamedTimexes"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for NamedTimexes.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(NamedTimexesConfig, self).__init__(**kwargs)
+
+
+class NamedTimexes(datasets.GeneratorBasedBuilder):
+    """NamedTimexes dataset."""
+
+    BUILDER_CONFIGS = [
+        NamedTimexesConfig(name="named-timexes", version=datasets.Version("1.0.0"), description="Named Temporal Expressions dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "tokens": datasets.Sequence(datasets.Value("string")),
+                    "ntimex_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "T",
+                            ]
+                        )
+                    ),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://aclanthology.org/R13-1015.pdf",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_file = dl_manager.download_and_extract(_URL)
+
+        data_files = {
+            "train": os.path.join(downloaded_file, _TRAIN_FILE),
+            "test": os.path.join(downloaded_file, _TEST_FILE),
+        }
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["test"]}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
+        ]
+
+    def _generate_examples(self, filepath):
+        guid = 0
+        with open(filepath, encoding="utf-8") as f:
+            logger.info("⏳ Generating examples from = %s", filepath)
+            tokens = []
+            ntimex_tags = []
+            for line in f:
+                if line.startswith("-DOCSTART-") or line.strip() == "" or line == "\n":
+                    if tokens:
+                        yield guid, {
+                            "id": str(guid),
+                            "tokens": tokens,
+                            "ntimex_tags": ntimex_tags,
+                        }
+                        guid += 1
+                        tokens = []
+                        ntimex_tags = []
+                else:
+                    # btc entries are tab separated
+                    fields = line.split("\t")
+                    tokens.append(fields[0])
+                    ntimex_tags.append(fields[1].rstrip())
+            # last example
+            yield guid, {
+                "id": str(guid),
+                "tokens": tokens,
+                "ntimex_tags": ntimex_tags,
+            }