nyth-dataset 0.0.2

NYT-H dataset package allows the processing of the NYT-H dataset proposed by Zhu et al., 2020. NYT-H is based on the New York Times 2010 ( NYT2010) dataset proposed by Riedel et al., 2010. The advantage of NYT-H dataset is the manual annotation of the test partition.

NYT-H dataset

NYT-H dataset package allows the processing of the NYT-H dataset proposed by Zhu et al., 2020 [pdf]. NYT-H is based on the New York Times 2010 ( NYT2010) dataset proposed by Riedel et al., 2010 [pdf]. The advantage of NYT-H dataset is the manual annotation of the test partition.

Codes to process and read the NYT-H dataset used in the COLING2020 paper: Towards Accurate and Consistent Evaluation: A Dataset for Distantly-Supervised Relation Extraction [pdf]

Dependencies

• python == 3.7
  • pandas

Dataset

Download

Download Link (from NYT-H project):

• Google Drive: download

Download the data from the link. Then extract data files from the tarball file.

$ tar jxvf nyt-h.tar.bz2

Data Example (from NYT-H project)

{
    "instance_id": "NONNADEV#193662",
    "bag_id": "NONNADEV#91512",
    "relation": "/people/person/place_lived",
    "bag_label": "unk",  # `unk` means the bag is not annotated, otherwise `yes` or `no`.
    "sentence": "Instead , he 's the kind of writer who can stare at the wall of his house in New Rochelle -LRB- as he did with '' Ragtime '' -RRB- , think about the year the house was built (1906) , follow his thoughts to the local tracks that once brought trolleys from New Rochelle to New York City and wind up with a book featuring Theodore Roosevelt , Scott Joplin , Emma Goldman , Stanford White and Harry Houdini . ''",
    "head": {
        "guid": "/guid/9202a8c04000641f8000000000176dc3",
        "word": "Stanford White",
        "type": "/influence/influence_node,/people/deceased_person"
        # type for entities, split by comma if one entity has many types
    },
    "tail": {
        "guid": "/guid/9202a8c04000641f80000000002f8906",
        "word": "New York City",
        "type": "/architecture/architectural_structure_owner,/location/citytown"
    }
}

File Structure and Data Preparation (from NYT-H project)

data
├── bag_label2id.json : bag annotation labels to numeric identifiers. `unk` label means the bag is not annotated, otherwise `yes` or `no`
├── rel2id.json : relation labels to numeric identifiers
├── na_train.json : NA instances for training to reproduce results in our paper
├── na_rest.json : rest of the NA instances
├── train_nonna.json : Non-NA instances for training (NO ANNOTATIONS)
├── dev.json : Non-NA instances for model selection during training (NO ANNOTATIONS)
└── test.json : Non-NA instances for final evaluation, including `bag_label` annotations

To get the full NA set:

$ cd data && cat na_rest.json na_train.json > na.json

To reproduce the results in our paper, combine the sampled NA instances(na_train.json) and train_nonna.json to get the train set:

$ cd data && cat train_nonna.json na_train.json > train.json

How to use the package

from nyth_dataset import NYTHDataset

# Create the object with the data path
dataset = NYTHDataset(data_dir='./data')
# Load the data
dataset.load_data()
# Get the data
train, dev, test = dataset.get_data()

Additionally, you can define the parameters:

• include_na_relation in the constructor (default True). If it is True the NA (Not a Relation) instances are loaded, otherwise not.
• reload in the method load_data (default False). If it is True the method will read the original files; otherwise, it will load the *.pkl saved on the first run

from nyth_dataset import NYTHDataset

# Create the object with the data path
dataset = NYTHDataset(data_dir='./data', include_na_relation=False)
# Load the data
dataset.load_data(reload=True)
# Get the data
train, dev, test = dataset.get_data()

Format of the loaded data

The train, dev and test files are dataframes with the following columns:

data
├── instance_id : identifier of the instance
├── bag_id : identifier of the bag
├── sentence : full sentence
├── e1_name : name of the head entity
├── e2_name : name of the tail entity
├── e1_type : type of the head entity
├── e2_type : type of the tail entity
├── text_between_entities_including_them : text between the entities including them
├── text_between_entities : text between the entities without them
├── relation : relation
├── bag_label :  2 (unk) means the bag is not annotated, otherwise 1 (yes) or 0 (no).
└── noisy : 2 (unk) means the instance is not annotated, otherwise 1 (noisy) or 0 (not noisy)

License

Apache Software License