Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
machine-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
word-segmentation
License:
File size: 3,641 Bytes
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"""STAN small dataset by Bansal et al.."""
import datasets
import pandas as pd
_CITATION = """
@misc{bansal2015deep,
title={Towards Deep Semantic Analysis Of Hashtags},
author={Piyush Bansal and Romil Bansal and Vasudeva Varma},
year={2015},
eprint={1501.03210},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
"""
_DESCRIPTION = """
Manually Annotated Stanford Sentiment Analysis Dataset by Bansal et al..
"""
_URLS = {
"test": "https://github.com/prashantkodali/HashSet/raw/master/datasets/stan-small-bansal_et_al.pkl"
}
class StanSmall(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"index": datasets.Value("int32"),
"hashtag": datasets.Value("string"),
"segmentation": datasets.Value("string"),
"alternatives": datasets.Sequence(
{
"segmentation": datasets.Value("string")
}
)
}
),
supervised_keys=None,
homepage="https://github.com/mounicam/hashtag_master",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
downloaded_files = dl_manager.download(_URLS)
return [
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"] }),
]
def _generate_examples(self, filepath):
def get_segmentation(row):
needle = row["hashtags"]
haystack = row["goldtruths"][0].strip()
output = ""
iterator = iter(haystack)
for char in needle:
output += char
while True:
try:
next_char = next(iterator)
if next_char.lower() == char.lower():
break
elif next_char.isspace():
output = output[0:-1] + next_char + output[-1]
except StopIteration:
break
return output
def get_alternatives(row, segmentation):
alts = list(set([x.strip() for x in row["goldtruths"]]))
alts = [x for x in alts if x != segmentation]
alts = [{"segmentation": x} for x in alts]
return alts
with open(filepath, 'rb') as f:
try:
import pickle
records = pickle.load(f)
except ValueError:
try:
import pickle5 as pickle
records = pickle.load(f)
except ModuleNotFoundError:
raise ImportError(
"""To be able to use stan_small, you need to install the following dependencies['pickle5']
using 'pip install pickle5' for instance"""
)
records = records.to_dict("records")
for idx, row in enumerate(records):
segmentation = get_segmentation(row)
alternatives = get_alternatives(row, segmentation)
yield idx, {
"index": idx,
"hashtag": row["hashtags"],
"segmentation": segmentation,
"alternatives": alternatives
}
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