| {"ollie_lemmagrep": {"description": "The Ollie dataset includes two configs for the data\nused to train the Ollie informatation extraction algorithm, for 18M\nsentences and 3M sentences respectively. \n\nThis data is for academic use only. From the authors:\n\nOllie is a program that automatically identifies and extracts binary\nrelationships from English sentences. Ollie is designed for Web-scale\ninformation extraction, where target relations are not specified in\nadvance.\n\nOllie is our second-generation information extraction system . Whereas\nReVerb operates on flat sequences of tokens, Ollie works with the\ntree-like (graph with only small cycles) representation using\nStanford's compression of the dependencies. This allows Ollie to\ncapture expression that ReVerb misses, such as long-range relations.\n\nOllie also captures context that modifies a binary relation. Presently\nOllie handles attribution (He said/she believes) and enabling\nconditions (if X then).\n\nMore information is available at the Ollie homepage:\nhttps://knowitall.github.io/ollie/\n", "citation": "@inproceedings{ollie-emnlp12,\n author = {Mausam and Michael Schmitz and Robert Bart and Stephen Soderland and Oren Etzioni},\n title = {Open Language Learning for Information Extraction},\n booktitle = {Proceedings of Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL)},\n year = {2012}\n}", "homepage": "https://knowitall.github.io/ollie/", "license": "The University of Washington acamdemic license:\nhttps://raw.githubusercontent.com/knowitall/ollie/master/LICENSE\n", "features": {"arg1": {"dtype": "string", "id": null, "_type": "Value"}, "arg2": {"dtype": "string", "id": null, "_type": "Value"}, "rel": {"dtype": "string", "id": null, "_type": "Value"}, "search_query": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "words": {"dtype": "string", "id": null, "_type": "Value"}, "pos": {"dtype": "string", "id": null, "_type": "Value"}, "chunk": {"dtype": "string", "id": null, "_type": "Value"}, "sentence_cnt": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ollie", "config_name": "ollie_lemmagrep", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 12324648919, "num_examples": 18674630, "dataset_name": "ollie"}}, "download_checksums": {"http://knowitall.cs.washington.edu/ollie/data/lemmagrep.txt.bz2": {"num_bytes": 1789363108, "checksum": "76ed3141fd95597c889eea1c05eb655914e76c72746893b856a00f2a422cbbab"}}, "download_size": 1789363108, "post_processing_size": null, "dataset_size": 12324648919, "size_in_bytes": 14114012027}, "ollie_patterned": {"description": "The Ollie dataset includes two configs for the data\nused to train the Ollie informatation extraction algorithm, for 18M\nsentences and 3M sentences respectively. \n\nThis data is for academic use only. From the authors:\n\nOllie is a program that automatically identifies and extracts binary\nrelationships from English sentences. Ollie is designed for Web-scale\ninformation extraction, where target relations are not specified in\nadvance.\n\nOllie is our second-generation information extraction system . Whereas\nReVerb operates on flat sequences of tokens, Ollie works with the\ntree-like (graph with only small cycles) representation using\nStanford's compression of the dependencies. This allows Ollie to\ncapture expression that ReVerb misses, such as long-range relations.\n\nOllie also captures context that modifies a binary relation. Presently\nOllie handles attribution (He said/she believes) and enabling\nconditions (if X then).\n\nMore information is available at the Ollie homepage:\nhttps://knowitall.github.io/ollie/\n", "citation": "@inproceedings{ollie-emnlp12,\n author = {Mausam and Michael Schmitz and Robert Bart and Stephen Soderland and Oren Etzioni},\n title = {Open Language Learning for Information Extraction},\n booktitle = {Proceedings of Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CONLL)},\n year = {2012}\n}", "homepage": "https://knowitall.github.io/ollie/", "license": "The University of Washington acamdemic license:\nhttps://raw.githubusercontent.com/knowitall/ollie/master/LICENSE\n", "features": {"rel": {"dtype": "string", "id": null, "_type": "Value"}, "arg1": {"dtype": "string", "id": null, "_type": "Value"}, "arg2": {"dtype": "string", "id": null, "_type": "Value"}, "slot0": {"dtype": "string", "id": null, "_type": "Value"}, "search_query": {"dtype": "string", "id": null, "_type": "Value"}, "pattern": {"dtype": "string", "id": null, "_type": "Value"}, "sentence": {"dtype": "string", "id": null, "_type": "Value"}, "parse": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "ollie", "config_name": "ollie_patterned", "version": "1.1.0", "splits": {"train": {"name": "train", "num_bytes": 2930309084, "num_examples": 3048961, "dataset_name": "ollie"}}, "download_checksums": {"http://knowitall.cs.washington.edu/ollie/data/patterned-all.txt.bz2": {"num_bytes": 387514061, "checksum": "a99e0907ff4c20f4a02a1a86453097affa73d6ab4160441c9b7203d756348f0d"}}, "download_size": 387514061, "post_processing_size": null, "dataset_size": 2930309084, "size_in_bytes": 3317823145}} |