Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
unknown
Language Creators:
machine-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
Tags:
word-segmentation
License:
unknown
| """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 | |
| } | |