Upload stan_large.py

stan_large.py

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+"""STAN large dataset as corrected by Maddela et al.."""
+
+import datasets
+import pandas as pd
+import pickle
+
+_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://github.com/prashantkodali/HashSet/raw/master/datasets/stan-large-maddela_et_al_train.pkl",
+    "dev": "https://github.com/prashantkodali/HashSet/raw/master/datasets/stan-large-maddela_et_al_dev.pkl",
+    "test": "https://github.com/prashantkodali/HashSet/raw/master/datasets/stan-large-maddela_et_al_test.pkl"
+}
+
+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):
+            return row["goldtruths"][0]
+        
+        def get_alternatives(row):
+            segmentations = [{
+                "segmentation": x
+            } for x in row["goldtruths"]]
+
+            return segmentations[1:]
+
+        with open(filepath, 'rb') as f:
+            records = pickle.load(f)
+        records = records.to_dict("records")
+        for idx, row in enumerate(records):
+            yield idx, {
+                "index": idx,
+                "hashtag": row["hashtags"],
+                "segmentation": get_segmentation(row),
+                "alternatives": get_alternatives(row)
+            }