Datasets:
amttl

Tasks: parsing
Task Categories: token-classification
Languages: Chinese
Multilinguality: monolingual
Size Categories: 1K<n<10K
Language Creators: found
Annotations Creators: crowdsourced
Source Datasets: original
Licenses: mit
File size: 5,328 Bytes
0a0f653
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f2287c3
 
 
0a0f653
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
f2287c3
0a0f653
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
# coding=utf-8
# Copyright 2020 HuggingFace Datasets Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3
"""Introduction to AMTTL CWS Dataset"""

import datasets


logger = datasets.logging.get_logger(__name__)


_CITATION = """\
@inproceedings{xing2018adaptive,
  title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
  author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
  booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
  pages={3619--3630},
  year={2018}
}
"""

_DESCRIPTION = """\
Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop
when dealing with domain text, especially for a domain with lots of special terms and diverse
writing styles, such as the biomedical domain. However, building domain-specific CWS requires
extremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant
knowledge from high resource to low resource domains. Extensive experiments show that our mode
achieves consistently higher accuracy than the single-task CWS and other transfer learning
baselines, especially when there is a large disparity between source and target domains.

This dataset is the accompanied medical Chinese word segmentation (CWS) dataset.
The tags are in BIES scheme.

For more details see https://www.aclweb.org/anthology/C18-1307/
"""

_URL = "https://raw.githubusercontent.com/adapt-sjtu/AMTTL/master/medical_data/"
_TRAINING_FILE = "forum_train.txt"
_DEV_FILE = "forum_dev.txt"
_TEST_FILE = "forum_test.txt"


class AmttlConfig(datasets.BuilderConfig):
    """BuilderConfig for AMTTL"""

    def __init__(self, **kwargs):
        """BuilderConfig for AMTTL.

        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(AmttlConfig, self).__init__(**kwargs)


class Amttl(datasets.GeneratorBasedBuilder):
    """AMTTL Chinese Word Segmentation dataset."""

    BUILDER_CONFIGS = [
        AmttlConfig(
            name="amttl",
            version=datasets.Version("1.0.0"),
            description="AMTTL medical Chinese word segmentation dataset",
        ),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "tokens": datasets.Sequence(datasets.Value("string")),
                    "tags": datasets.Sequence(
                        datasets.features.ClassLabel(
                            names=[
                                "B",
                                "I",
                                "E",
                                "S",
                            ]
                        )
                    ),
                }
            ),
            supervised_keys=None,
            homepage="https://www.aclweb.org/anthology/C18-1307/",
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        urls_to_download = {
            "train": f"{_URL}{_TRAINING_FILE}",
            "dev": f"{_URL}{_DEV_FILE}",
            "test": f"{_URL}{_TEST_FILE}",
        }
        downloaded_files = dl_manager.download_and_extract(urls_to_download)

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}),
        ]

    def _generate_examples(self, filepath):
        logger.info("⏳ Generating examples from = %s", filepath)
        with open(filepath, encoding="utf-8") as f:
            guid = 0
            tokens = []
            tags = []
            for line in f:
                line_stripped = line.strip()
                if line_stripped == "":
                    if tokens:
                        yield guid, {
                            "id": str(guid),
                            "tokens": tokens,
                            "tags": tags,
                        }
                        guid += 1
                        tokens = []
                        tags = []
                else:
                    splits = line_stripped.split("\t")
                    if len(splits) == 1:
                        splits.append("O")
                    tokens.append(splits[0])
                    tags.append(splits[1])
            # last example
            yield guid, {
                "id": str(guid),
                "tokens": tokens,
                "tags": tags,
            }