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"""TODO: Add a description here.""" |
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import csv |
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import os |
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import datasets |
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_CITATION = """\ |
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@inproceedings{socher-etal-2013-recursive, |
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title = "Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank", |
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author = "Socher, Richard and Perelygin, Alex and Wu, Jean and |
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Chuang, Jason and Manning, Christopher D. and Ng, Andrew and Potts, Christopher", |
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booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing", |
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month = oct, |
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year = "2013", |
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address = "Seattle, Washington, USA", |
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publisher = "Association for Computational Linguistics", |
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url = "https://www.aclweb.org/anthology/D13-1170", |
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pages = "1631--1642", |
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} |
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""" |
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_DESCRIPTION = """\ |
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The Stanford Sentiment Treebank, the first corpus with fully labeled parse trees that allows for a |
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complete analysis of the compositional effects of sentiment in language. |
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""" |
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_HOMEPAGE = "https://nlp.stanford.edu/sentiment/" |
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_LICENSE = "" |
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_DEFAULT_URL = "https://nlp.stanford.edu/~socherr/stanfordSentimentTreebank.zip" |
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_PTB_URL = "https://nlp.stanford.edu/sentiment/trainDevTestTrees_PTB.zip" |
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class Sst(datasets.GeneratorBasedBuilder): |
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"""The Stanford Sentiment Treebank""" |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="default", |
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version=VERSION, |
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description="Sentences and relative parse trees annotated with sentiment labels.", |
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), |
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datasets.BuilderConfig( |
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name="dictionary", |
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version=VERSION, |
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description="List of all possible sub-sentences (phrases) with their sentiment label.", |
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), |
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datasets.BuilderConfig( |
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name="ptb", version=VERSION, description="Penn Treebank-formatted trees with labelled sub-sentences." |
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), |
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] |
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DEFAULT_CONFIG_NAME = "default" |
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def _info(self): |
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if self.config.name == "default": |
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features = datasets.Features( |
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{ |
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"sentence": datasets.Value("string"), |
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"label": datasets.Value("float"), |
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"tokens": datasets.Value("string"), |
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"tree": datasets.Value("string"), |
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} |
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) |
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elif self.config.name == "dictionary": |
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features = datasets.Features({"phrase": datasets.Value("string"), "label": datasets.Value("float")}) |
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else: |
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features = datasets.Features( |
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{ |
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"ptb_tree": datasets.Value("string"), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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default_dir = dl_manager.download_and_extract(_DEFAULT_URL) |
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ptb_dir = dl_manager.download_and_extract(_PTB_URL) |
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file_paths = {} |
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for split_index in range(0, 4): |
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file_paths[split_index] = { |
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"phrases_path": os.path.join(default_dir, "stanfordSentimentTreebank/dictionary.txt"), |
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"labels_path": os.path.join(default_dir, "stanfordSentimentTreebank/sentiment_labels.txt"), |
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"tokens_path": os.path.join(default_dir, "stanfordSentimentTreebank/SOStr.txt"), |
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"trees_path": os.path.join(default_dir, "stanfordSentimentTreebank/STree.txt"), |
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"splits_path": os.path.join(default_dir, "stanfordSentimentTreebank/datasetSplit.txt"), |
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"sentences_path": os.path.join(default_dir, "stanfordSentimentTreebank/datasetSentences.txt"), |
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"ptb_filepath": None, |
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"split_id": str(split_index), |
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} |
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ptb_file_paths = {} |
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for ptb_split in ["train", "dev", "test"]: |
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ptb_file_paths[ptb_split] = { |
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"phrases_path": None, |
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"labels_path": None, |
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"tokens_path": None, |
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"trees_path": None, |
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"splits_path": None, |
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"sentences_path": None, |
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"ptb_filepath": os.path.join(ptb_dir, "trees/" + ptb_split + ".txt"), |
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"split_id": None, |
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} |
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if self.config.name == "default": |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=file_paths[1]), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=file_paths[3]), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=file_paths[2]), |
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] |
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elif self.config.name == "dictionary": |
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return [datasets.SplitGenerator(name="dictionary", gen_kwargs=file_paths[0])] |
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else: |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=ptb_file_paths["train"]), |
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datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=ptb_file_paths["dev"]), |
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datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=ptb_file_paths["test"]), |
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] |
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def _generate_examples( |
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self, phrases_path, labels_path, tokens_path, trees_path, splits_path, sentences_path, split_id, ptb_filepath |
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): |
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if self.config.name == "ptb": |
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with open(ptb_filepath, encoding="utf-8") as fp: |
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ptb_reader = csv.reader(fp, delimiter="\t", quoting=csv.QUOTE_NONE) |
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for id_, row in enumerate(ptb_reader): |
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yield id_, {"ptb_tree": row[0]} |
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else: |
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labels = {} |
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phrases = {} |
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with open(labels_path, encoding="utf-8") as g, open(phrases_path, encoding="utf-8") as f: |
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label_reader = csv.DictReader(g, delimiter="|", quoting=csv.QUOTE_NONE) |
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for row in label_reader: |
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labels[row["phrase ids"]] = float(row["sentiment values"]) |
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phrase_reader = csv.reader(f, delimiter="|", quoting=csv.QUOTE_NONE) |
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if self.config.name == "dictionary": |
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for id_, row in enumerate(phrase_reader): |
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yield id_, {"phrase": row[0], "label": labels[row[1]]} |
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else: |
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for row in phrase_reader: |
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phrases[row[0]] = labels[row[1]] |
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trees = {} |
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with open(tokens_path, encoding="utf-8") as tok, open(trees_path, encoding="utf-8") as tr: |
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tok_reader = csv.reader(tok, delimiter="\t", quoting=csv.QUOTE_NONE) |
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tree_reader = csv.reader(tr, delimiter="\t", quoting=csv.QUOTE_NONE) |
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for i, row in enumerate(tok_reader, start=1): |
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trees[i] = {} |
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trees[i]["tokens"] = row[0] |
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for i, row in enumerate(tree_reader, start=1): |
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trees[i]["tree"] = row[0] |
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with open(splits_path, encoding="utf-8") as spl, open(sentences_path, encoding="utf-8") as snt: |
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splits_reader = csv.DictReader(spl, delimiter=",", quoting=csv.QUOTE_NONE) |
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splits = {row["sentence_index"]: row["splitset_label"] for row in splits_reader} |
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sentence_reader = csv.DictReader(snt, delimiter="\t", quoting=csv.QUOTE_NONE) |
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for id_, row in enumerate(sentence_reader): |
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row["sentence"] = ( |
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row["sentence"] |
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.encode("utf-8") |
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.replace(b"\xc3\x83\xc2", b"\xc3") |
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.replace(b"\xc3\x82\xc2", b"\xc2") |
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.decode("utf-8") |
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) |
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row["sentence"] = row["sentence"].replace("-LRB-", "(").replace("-RRB-", ")") |
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if splits[row["sentence_index"]] == split_id: |
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tokens = trees[int(row["sentence_index"])]["tokens"] |
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parse_tree = trees[int(row["sentence_index"])]["tree"] |
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yield id_, { |
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"sentence": row["sentence"], |
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"label": phrases[row["sentence"]], |
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"tokens": tokens, |
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"tree": parse_tree, |
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} |
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