Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Source Datasets:
original
ArXiv:
License:
# coding=utf-8 | |
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
# | |
# 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 | |
"""TODO(scicite): Add a description here.""" | |
import json | |
import datasets | |
_CITATION = """ | |
@InProceedings{Cohan2019Structural, | |
author={Arman Cohan and Waleed Ammar and Madeleine Van Zuylen and Field Cady}, | |
title={Structural Scaffolds for Citation Intent Classification in Scientific Publications}, | |
booktitle={NAACL}, | |
year={2019} | |
} | |
""" | |
_DESCRIPTION = """ | |
This is a dataset for classifying citation intents in academic papers. | |
The main citation intent label for each Json object is specified with the label | |
key while the citation context is specified in with a context key. Example: | |
{ | |
'string': 'In chacma baboons, male-infant relationships can be linked to both | |
formation of friendships and paternity success [30,31].' | |
'sectionName': 'Introduction', | |
'label': 'background', | |
'citingPaperId': '7a6b2d4b405439', | |
'citedPaperId': '9d1abadc55b5e0', | |
... | |
} | |
You may obtain the full information about the paper using the provided paper ids | |
with the Semantic Scholar API (https://api.semanticscholar.org/). | |
The labels are: | |
Method, Background, Result | |
""" | |
_SOURCE_NAMES = ["properNoun", "andPhrase", "acronym", "etAlPhrase", "explicit", "acronymParen", "nan"] | |
class Scicite(datasets.GeneratorBasedBuilder): | |
"""This is a dataset for classifying citation intents in academic papers.""" | |
VERSION = datasets.Version("1.0.0") | |
def _info(self): | |
return datasets.DatasetInfo( | |
# This is the description that will appear on the datasets page. | |
description=_DESCRIPTION, | |
# datasets.features.FeatureConnectors | |
features=datasets.Features( | |
{ | |
"string": datasets.Value("string"), | |
"sectionName": datasets.Value("string"), | |
"label": datasets.features.ClassLabel(names=["method", "background", "result"]), | |
"citingPaperId": datasets.Value("string"), | |
"citedPaperId": datasets.Value("string"), | |
"excerpt_index": datasets.Value("int32"), | |
"isKeyCitation": datasets.Value("bool"), | |
"label2": datasets.features.ClassLabel( | |
names=["supportive", "not_supportive", "cant_determine", "none"] | |
), | |
"citeEnd": datasets.Value("int64"), | |
"citeStart": datasets.Value("int64"), | |
"source": datasets.features.ClassLabel(names=_SOURCE_NAMES), | |
"label_confidence": datasets.Value("float32"), | |
"label2_confidence": datasets.Value("float32"), | |
"id": datasets.Value("string"), | |
} | |
), | |
# 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="https://github.com/allenai/scicite", | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
archive = dl_manager.download("https://s3-us-west-2.amazonaws.com/ai2-s2-research/scicite/scicite.tar.gz") | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": "/".join(["scicite", "train.jsonl"]), | |
"files": dl_manager.iter_archive(archive), | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={"filepath": "/".join(["scicite", "dev.jsonl"]), "files": dl_manager.iter_archive(archive)}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepath": "/".join(["scicite", "test.jsonl"]), | |
"files": dl_manager.iter_archive(archive), | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, files): | |
"""Yields examples.""" | |
for path, f in files: | |
if path == filepath: | |
unique_ids = {} | |
for line in f: | |
d = json.loads(line.decode("utf-8")) | |
unique_id = str(d["unique_id"]) | |
if unique_id in unique_ids: | |
continue | |
unique_ids[unique_id] = True | |
yield unique_id, { | |
"string": d["string"], | |
"label": str(d["label"]), | |
"sectionName": str(d["sectionName"]), | |
"citingPaperId": str(d["citingPaperId"]), | |
"citedPaperId": str(d["citedPaperId"]), | |
"excerpt_index": int(d["excerpt_index"]), | |
"isKeyCitation": bool(d["isKeyCitation"]), | |
"label2": str(d.get("label2", "none")), | |
"citeEnd": _safe_int(d["citeEnd"]), | |
"citeStart": _safe_int(d["citeStart"]), | |
"source": str(d["source"]), | |
"label_confidence": float(d.get("label_confidence", 0.0)), | |
"label2_confidence": float(d.get("label2_confidence", 0.0)), | |
"id": str(d["id"]), | |
} | |
break | |
def _safe_int(a): | |
try: | |
# skip NaNs | |
return int(a) | |
except ValueError: | |
return -1 | |