nlp_architect.data.sequential_tagging.CONLL2000¶

class nlp_architect.data.sequential_tagging.CONLL2000(data_path, sentence_length=None, max_word_length=None, extract_chars=False, lowercase=True)[source]¶

CONLL 2000 POS/chunking task data set (numpy)

Parameters:

data_path (str) – directory containing CONLL2000 files
sentence_length (int, optional) – number of time steps to embed the data. None value will not truncate vectors
max_word_length (int, optional) – max word length in characters. None value will not truncate vectors
extract_chars (boolean, optional) – Yield Char RNN features.
lowercase (bool, optional) – lower case sentence words

__init__(data_path, sentence_length=None, max_word_length=None, extract_chars=False, lowercase=True)[source]¶: Initialize self. See help(type(self)) for accurate signature.

Methods

__init__(data_path[, sentence_length, …]) Initialize self.

Attributes

`char_vocab`	character Vocabulary
`chunk_vocab`	chunk label Vocabulary
`dataset_files`
`pos_vocab`	pos label Vocabulary
`test_set`	get the test set
`train_set`	get the train set
`word_vocab`	word Vocabulary

char_vocab¶: character Vocabulary

chunk_vocab¶: chunk label Vocabulary

dataset_files = {'test': 'test.txt', 'train': 'train.txt'}¶

pos_vocab¶: pos label Vocabulary

test_set¶: get the test set

train_set¶: get the train set

word_vocab¶: word Vocabulary