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# 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.
"""NFH: Numeric Fused-Heads."""
import csv
import json
import datasets
_CITATION = """\
@article{elazar_head,
author = {Elazar, Yanai and Goldberg, Yoav},
title = {Where’s My Head? Definition, Data Set, and Models for Numeric Fused-Head Identification and Resolution},
journal = {Transactions of the Association for Computational Linguistics},
volume = {7},
number = {},
pages = {519-535},
year = {2019},
doi = {10.1162/tacl\\_a\\_00280},
URL = {https://doi.org/10.1162/tacl_a_00280},
}
"""
_DESCRIPTION = """\
Fused Head constructions are noun phrases in which the head noun is \
missing and is said to be "fused" with its dependent modifier. This \
missing information is implicit and is important for sentence understanding.\
The missing heads are easily filled in by humans, but pose a challenge for \
computational models.
For example, in the sentence: "I bought 5 apples but got only 4.", 4 is a \
Fused-Head, and the missing head is apples, which appear earlier in the sentence.
This is a crowd-sourced dataset of 10k numerical fused head examples (1M tokens).
"""
_HOMEPAGE = "https://nlp.biu.ac.il/~lazary/fh/"
_LICENSE = "MIT"
_URLs = {
"identification": {
"train": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/identification/processed/train.tsv",
"test": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/identification/processed/test.tsv",
"dev": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/identification/processed/dev.tsv",
},
"resolution": {
"train": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/resolution/processed/nfh_train.jsonl",
"test": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/resolution/processed/nfh_test.jsonl",
"dev": "https://raw.githubusercontent.com/yanaiela/num_fh/master/data/resolution/processed/nfh_dev.jsonl",
},
}
class NumericFusedHead(datasets.GeneratorBasedBuilder):
"""NFH: Numeric Fused-Heads"""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="identification", description="Identify NFH anchors in a sentence"),
datasets.BuilderConfig(name="resolution", description="Identify the head for the numeric anchor"),
]
def _info(self):
if self.config.name == "identification":
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"start_index": datasets.Value("int32"),
"end_index": datasets.Value("int32"),
"label": datasets.features.ClassLabel(names=["neg", "pos"]),
}
)
else:
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"line_indices": datasets.Sequence(datasets.Value("int32")),
"head": datasets.Sequence(datasets.Value("string")),
"speakers": datasets.Sequence(datasets.Value("string")),
"anchors_indices": datasets.Sequence(datasets.Value("int32")),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
data_files = dl_manager.download_and_extract(_URLs[self.config.name])
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_files["train"]}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_files["test"]}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_files["dev"]}),
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
if self.config.name == "identification":
r = csv.DictReader(f, delimiter="\t")
for id_, row in enumerate(r):
data = {
"tokens": row["text"].split("_SEP_"),
"start_index": row["ind_s"],
"end_index": row["ind_e"],
"label": "neg" if row["y"] == "0" else "pos",
}
yield id_, data
else:
for id_, row in enumerate(f):
data = json.loads(row)
yield id_, {
"tokens": data["tokens"],
"line_indices": data["line_indices"],
"head": [str(s) for s in data["head"]],
"speakers": [str(s) for s in data["speakers"]],
"anchors_indices": data["anchors_indices"],
}
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