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| """Ollie""" |
|
|
|
|
| import bz2 |
|
|
| import datasets |
|
|
|
|
| _CITATION = """\ |
| @inproceedings{ollie-emnlp12, |
| author = {Mausam and Michael Schmitz and Robert Bart and Stephen Soderland and Oren Etzioni}, |
| title = {Open Language Learning for Information Extraction}, |
| booktitle = {Proceedings of Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL)}, |
| year = {2012} |
| }""" |
|
|
|
|
| _DESCRIPTION = """The Ollie dataset includes two configs for the data |
| used to train the Ollie informatation extraction algorithm, for 18M |
| sentences and 3M sentences respectively. |
| |
| This data is for academic use only. From the authors: |
| |
| Ollie is a program that automatically identifies and extracts binary |
| relationships from English sentences. Ollie is designed for Web-scale |
| information extraction, where target relations are not specified in |
| advance. |
| |
| Ollie is our second-generation information extraction system . Whereas |
| ReVerb operates on flat sequences of tokens, Ollie works with the |
| tree-like (graph with only small cycles) representation using |
| Stanford's compression of the dependencies. This allows Ollie to |
| capture expression that ReVerb misses, such as long-range relations. |
| |
| Ollie also captures context that modifies a binary relation. Presently |
| Ollie handles attribution (He said/she believes) and enabling |
| conditions (if X then). |
| |
| More information is available at the Ollie homepage: |
| https://knowitall.github.io/ollie/ |
| """ |
|
|
|
|
| _LICENSE = """The University of Washington acamdemic license: |
| https://raw.githubusercontent.com/knowitall/ollie/master/LICENSE |
| """ |
|
|
| _URLs = { |
| "ollie_lemmagrep": "http://knowitall.cs.washington.edu/ollie/data/lemmagrep.txt.bz2", |
| "ollie_patterned": "http://knowitall.cs.washington.edu/ollie/data/patterned-all.txt.bz2", |
| } |
|
|
|
|
| class Ollie(datasets.GeneratorBasedBuilder): |
| """Ollie dataset for knowledge bases and knowledge graphs and underlying sentences.""" |
|
|
| VERSION = datasets.Version("0.1.0") |
|
|
| BUILDER_CONFIGS = [ |
| datasets.BuilderConfig(name="ollie_lemmagrep", description="The Ollie training data", version="1.1.0"), |
| datasets.BuilderConfig( |
| name="ollie_patterned", description="The Ollie data used in the Ollie paper.", version="1.1.0" |
| ), |
| ] |
|
|
| DEFAULT_CONFIG_NAME = "ollie_lemmagrep" |
|
|
| def _info(self): |
| if self.config.name == "ollie_lemmagrep": |
| features = datasets.Features( |
| { |
| "arg1": datasets.Value("string"), |
| "arg2": datasets.Value("string"), |
| "rel": datasets.Value("string"), |
| "search_query": datasets.Value("string"), |
| "sentence": datasets.Value("string"), |
| "words": datasets.Value("string"), |
| "pos": datasets.Value("string"), |
| "chunk": datasets.Value("string"), |
| "sentence_cnt": datasets.Value("string"), |
| } |
| ) |
| else: |
| features = datasets.Features( |
| { |
| "rel": datasets.Value("string"), |
| "arg1": datasets.Value("string"), |
| "arg2": datasets.Value("string"), |
| "slot0": datasets.Value("string"), |
| "search_query": datasets.Value("string"), |
| "pattern": datasets.Value("string"), |
| "sentence": datasets.Value("string"), |
| "parse": datasets.Value("string"), |
| } |
| ) |
| return datasets.DatasetInfo( |
| description=_DESCRIPTION, |
| features=features, |
| supervised_keys=None, |
| homepage="https://knowitall.github.io/ollie/", |
| license=_LICENSE, |
| citation=_CITATION, |
| ) |
|
|
| def _split_generators(self, dl_manager): |
| """Returns SplitGenerators.""" |
| my_urls = _URLs[self.config.name] |
| data_dir = dl_manager.download_and_extract(my_urls) |
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "filepath": data_dir, |
| "split": "train", |
| }, |
| ), |
| ] |
|
|
| def _generate_examples(self, filepath, split): |
| """Yields examples from the Ollie predicates and sentences.""" |
|
|
| with bz2.open(filepath, "rt") as f: |
| id_ = -1 |
| if self.config.name == "ollie_lemmagrep": |
| for row in f: |
| row = row.strip().split("\t") |
| id_ += 1 |
| if len(row) == 8: |
| yield id_, { |
| "arg1": row[0].strip(), |
| "arg2": row[1].strip(), |
| "rel": "", |
| "search_query": row[2].strip(), |
| "sentence": row[3].strip(), |
| "words": row[4].strip(), |
| "pos": row[5].strip(), |
| "chunk": row[6].strip(), |
| "sentence_cnt": row[7].strip(), |
| } |
| else: |
| yield id_, { |
| "arg1": row[1].strip(), |
| "arg2": row[2].strip(), |
| "rel": row[0].strip(), |
| "search_query": row[3].strip(), |
| "sentence": row[4].strip(), |
| "words": row[5].strip(), |
| "pos": row[6].strip(), |
| "chunk": row[7].strip(), |
| "sentence_cnt": row[8].strip(), |
| } |
| else: |
| for row in f: |
| row = row.strip().split("\t") |
| id_ += 1 |
| if len(row) == 7: |
| yield id_, { |
| "rel": row[0].strip(), |
| "arg1": row[1].strip(), |
| "arg2": row[2].strip(), |
| "slot0": "", |
| "search_query": row[3].strip(), |
| "pattern": row[4].strip(), |
| "sentence": row[5].strip(), |
| "parse": row[6].strip(), |
| } |
| else: |
| yield id_, { |
| "rel": row[0].strip(), |
| "arg1": row[1].strip(), |
| "arg2": row[2].strip(), |
| "slot0": row[7].strip(), |
| "search_query": row[3].strip(), |
| "pattern": row[4].strip(), |
| "sentence": row[5].strip(), |
| "parse": row[6].strip(), |
| } |
|
|
