Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
machine-generated
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
word-segmentation
License:
"""STAN large dataset""" | |
from multiprocessing.sharedctypes import Value | |
import datasets | |
import pandas as pd | |
import ast | |
_CITATION = """ | |
@inproceedings{maddela-etal-2019-multi, | |
title = "Multi-task Pairwise Neural Ranking for Hashtag Segmentation", | |
author = "Maddela, Mounica and | |
Xu, Wei and | |
Preo{\c{t}}iuc-Pietro, Daniel", | |
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics", | |
month = jul, | |
year = "2019", | |
address = "Florence, Italy", | |
publisher = "Association for Computational Linguistics", | |
url = "https://aclanthology.org/P19-1242", | |
doi = "10.18653/v1/P19-1242", | |
pages = "2538--2549", | |
abstract = "Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics. However, the semantic content of hashtags is not straightforward to infer as these represent ad-hoc conventions which frequently include multiple words joined together and can include abbreviations and unorthodox spellings. We build a dataset of 12,594 hashtags split into individual segments and propose a set of approaches for hashtag segmentation by framing it as a pairwise ranking problem between candidate segmentations. Our novel neural approaches demonstrate 24.6{\%} error reduction in hashtag segmentation accuracy compared to the current state-of-the-art method. Finally, we demonstrate that a deeper understanding of hashtag semantics obtained through segmentation is useful for downstream applications such as sentiment analysis, for which we achieved a 2.6{\%} increase in average recall on the SemEval 2017 sentiment analysis dataset.", | |
} | |
""" | |
_DESCRIPTION = """ | |
The description below was taken from the paper "Multi-task Pairwise Neural Ranking for Hashtag Segmentation" | |
by Maddela et al.. | |
"STAN large, our new expert curated dataset, which includes all 12,594 unique English hashtags and their | |
associated tweets from the same Stanford dataset. | |
STAN small is the most commonly used dataset in previous work. However, after reexamination, we found annotation | |
errors in 6.8% of the hashtags in this dataset, which is significant given that the error rate of the state-of-the art | |
models is only around 10%. Most of the errors were related to named entities. For example, #lionhead, | |
which refers to the “Lionhead” video game company, was labeled as “lion head”. | |
We therefore constructed the STAN large dataset of 12,594 hashtags with additional quality control for human annotations." | |
""" | |
_URLS = { | |
"train": "https://raw.githubusercontent.com/ruanchaves/hashformers/master/datasets/stan_large_train.csv", | |
"dev": "https://raw.githubusercontent.com/ruanchaves/hashformers/master/datasets/stan_large_dev.csv", | |
"test": "https://raw.githubusercontent.com/ruanchaves/hashformers/master/datasets/stan_large_test.csv" | |
} | |
class StanLarge(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.TRAIN, gen_kwargs={"filepath": downloaded_files["train"] }), | |
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"] }), | |
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 | |
records = pd.read_csv(filepath).to_dict("records") | |
records = [{"hashtags": row["hashtags"], "goldtruths": ast.literal_eval(row["goldtruths"])} for row in 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 | |
} | |