Datasets:
Sub-tasks:
dialogue-modeling
Languages:
Yue Chinese
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
# | |
# 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. | |
"""Hong Kong Cantonese Corpus (HKCanCor).""" | |
import os | |
import xml.etree.ElementTree as ET | |
import datasets | |
_CITATION = """\ | |
@article{luke2015hong, | |
author={Luke, Kang-Kwong and Wong, May LY}, | |
title={The Hong Kong Cantonese corpus: design and uses}, | |
journal={Journal of Chinese Linguistics}, | |
year={2015}, | |
pages={309-330}, | |
month={12} | |
} | |
@misc{lee2020, | |
author = {Lee, Jackson}, | |
title = {PyCantonese: Cantonese Linguistics and NLP in Python}, | |
year = {2020}, | |
publisher = {GitHub}, | |
journal = {GitHub repository}, | |
howpublished = {https://github.com/jacksonllee/pycantonese}, | |
commit = {1d58f44e1cb097faa69de6b617e1d28903b84b98} | |
} | |
""" | |
_DESCRIPTION = """\ | |
The Hong Kong Cantonese Corpus (HKCanCor) comprise transcribed conversations | |
recorded between March 1997 and August 1998. It contains recordings of | |
spontaneous speech (51 texts) and radio programmes (42 texts), | |
which involve 2 to 4 speakers, with 1 text of monologue. | |
In total, the corpus contains around 230,000 Chinese words. | |
The text is word-segmented, annotated with part-of-speech (POS) tags and | |
romanised Cantonese pronunciation. | |
Romanisation scheme - Linguistic Society of Hong Kong (LSHK) | |
POS scheme - Peita-Fujitsu-Renmin Ribao (PRF) corpus (Duan et al., 2000), | |
with extended tags for Cantonese-specific phenomena added by | |
Luke and Wang (see original paper for details). | |
""" | |
_HOMEPAGE = "http://compling.hss.ntu.edu.sg/hkcancor/" | |
_LICENSE = "CC BY 4.0" | |
# Original URL gives connection error | |
# _URL = "http://compling.hss.ntu.edu.sg/hkcancor/data/hkcancor-utf8.zip" | |
_URL = "https://github.com/fcbond/hkcancor/raw/master/data/hkcancor-utf8.zip" | |
class Hkcancor(datasets.GeneratorBasedBuilder): | |
"""Hong Kong Cantonese Corpus (HKCanCor).""" | |
VERSION = datasets.Version("1.0.0") | |
# Original tagset has 110 + tags and includes fine-grained annotations, | |
# e.g., distinguish morphemes vs non-moprhemes. For practical purposes | |
# (usability, comparing across datasets), Lee 2020 mapped HKCanCor tags | |
# to the Universal Dependencies 2.0 scheme. The following is adapted from: | |
# https://github.com/jacksonllee/pycantonese/blob/master/pycantonese/pos_tagging/hkcancor_to_ud.py | |
pos_map = { | |
"!": "PUNCT", | |
'"': "PUNCT", | |
"#": "X", | |
"'": "PUNCT", | |
",": "PUNCT", | |
"-": "PUNCT", | |
".": "PUNCT", | |
"...": "PUNCT", | |
"?": "PUNCT", | |
"A": "ADJ", # HKCanCor: Adjective | |
"AD": "ADV", # HKCanCor: Adjective as Adverbial | |
"AG": "ADJ", # HKCanCor: Adjective Morpheme | |
"AIRWAYS0": "PROPN", | |
"AN": "NOUN", # HKCanCor: Adjective with Nominal Function | |
"AND": "PROPN", # In one instance of "Chilli and Pepper" | |
"B": "ADJ", # HKCanCor: Non-predicate Adjective | |
"BG": "ADJ", # HKCanCor: Non-predicate Adjective Morpheme | |
"BEAN0": "PROPN", # In one instance of "Mr Bean" | |
"C": "CCONJ", # HKCanCor: Conjunction | |
"CENTRE0": "NOUN", # In one instance of "career centre" | |
"CG": "CCONJ", | |
"D": "ADV", # HKCanCor: Adverb | |
"D1": "ADV", # Most instances are gwai2 "ghost". | |
"DG": "ADV", # HKCanCor: Adverb Morpheme | |
"E": "INTJ", # HKCanCor: Interjection | |
"ECHO0": "PROPN", # In one instance of "Big Echo" | |
"F": "ADV", # HKCanCor: Directional Locality | |
"G": "X", # HKCanCor: Morpheme | |
"G1": "V", # The first A in the "A-not-AB" pattern, where AB is a verb. | |
"G2": "ADJ", # The first A in "A-not-AB", where AB is an adjective. | |
"H": "PROPN", # HKCanCor: Prefix (aa3 阿 followed by a person name) | |
"HILL0": "PROPN", # In "Benny Hill" | |
"I": "X", # HKCanCor: Idiom | |
"IG": "X", | |
"J": "NOUN", # HKCanCor: Abbreviation | |
"JB": "ADJ", | |
"JM": "NOUN", | |
"JN": "NOUN", | |
"JNS": "PROPN", | |
"JNT": "PROPN", | |
"JNZ": "PROPN", | |
"K": "X", # HKCanCor: Suffix (sing3 性 for nouns; dei6 地 for adverbs) | |
"KONG": "PROPN", # In "Hong Kong" | |
"L": "X", # Fixed Expression | |
"L1": "X", | |
"LG": "X", | |
"M": "NUM", # HKCanCor: Numeral | |
"MG": "X", | |
"MONTY0": "PROPN", # In "Full Monty" | |
"MOUNTAIN0": "PROPN", # In "Blue Mountain" | |
"N": "NOUN", # Common Noun | |
"N1": "DET", # HKCanCor: only used for ne1 呢; determiner | |
"NG": "NOUN", | |
"NR": "PROPN", # HKCanCor: Personal Name | |
"NS": "PROPN", # HKCanCor: Place Name | |
"NSG": "PROPN", | |
"NT": "PROPN", # HKCanCor: Organization Name | |
"NX": "NOUN", # HKCanCor: Nominal Character String | |
"NZ": "PROPN", # HKCanCor: Other Proper Noun | |
"O": "X", # HKCanCor: Onomatopoeia | |
"P": "ADP", # HKCanCor: Preposition | |
"PEPPER0": "PROPN", # In "Chilli and Pepper" | |
"Q": "NOUN", # HKCanCor: Classifier | |
"QG": "NOUN", # HKCanCor: Classifier Morpheme | |
"R": "PRON", # HKCanCor: Pronoun | |
"RG": "PRON", # HKCanCor: Pronoun Morpheme | |
"S": "NOUN", # HKCanCor: Space Word | |
"SOUND0": "PROPN", # In "Manchester's Sound" | |
"T": "ADV", # HKCanCor: Time Word | |
"TELECOM0": "PROPN", # In "Hong Kong Telecom" | |
"TG": "ADV", # HKCanCor: Time Word Morpheme | |
"TOUCH0": "PROPN", # In "Don't Touch" (a magazine) | |
"U": "PART", # HKCanCor: Auxiliary (e.g., ge3 嘅 after an attributive adj) | |
"UG": "PART", # HKCanCor: Auxiliary Morpheme | |
"U0": "PROPN", # U as in "Hong Kong U" (= The University of Hong Kong) | |
"V": "VERB", # HKCanCor: Verb | |
"V1": "VERB", | |
"VD": "ADV", # HKCanCor: Verb as Adverbial | |
"VG": "VERB", | |
"VK": "VERB", | |
"VN": "NOUN", # HKCanCor: Verb with Nominal Function | |
"VU": "AUX", | |
"VUG": "AUX", | |
"W": "PUNCT", # HKCanCor: Punctuation | |
"X": "X", # HKCanCor: Unclassified Item | |
"XA": "ADJ", | |
"XB": "ADJ", | |
"XC": "CCONJ", | |
"XD": "ADV", | |
"XE": "INTJ", | |
"XJ": "X", | |
"XJB": "PROPN", | |
"XJN": "NOUN", | |
"XJNT": "PROPN", | |
"XJNZ": "PROPN", | |
"XJV": "VERB", | |
"XJA": "X", | |
"XL1": "INTJ", | |
"XM": "NUM", | |
"XN": "NOUN", | |
"XNG": "NOUN", | |
"XNR": "PROPN", | |
"XNS": "PROPN", | |
"XNT": "PROPN", | |
"XNX": "NOUN", | |
"XNZ": "PROPN", | |
"XO": "X", | |
"XP": "ADP", | |
"XQ": "NOUN", | |
"XR": "PRON", | |
"XS": "PROPN", | |
"XT": "NOUN", | |
"XV": "VERB", | |
"XVG": "VERB", | |
"XVN": "NOUN", | |
"XX": "X", | |
"Y": "PART", # HKCanCor: Modal Particle | |
"YG": "PART", # HKCanCor: Modal Particle Morpheme | |
"Y1": "PART", | |
"Z": "ADJ", # HKCanCor: Descriptive | |
} | |
def _info(self): | |
pos_tags_prf = datasets.Sequence(datasets.features.ClassLabel(names=[tag for tag in self.pos_map.keys()])) | |
pos_tags_ud = datasets.Sequence( | |
datasets.features.ClassLabel(names=[tag for tag in set(self.pos_map.values())]) | |
) | |
features = datasets.Features( | |
{ | |
"conversation_id": datasets.Value("string"), | |
"speaker": datasets.Value("string"), | |
"turn_number": datasets.Value("int16"), | |
"tokens": datasets.Sequence(datasets.Value("string")), | |
"transcriptions": datasets.Sequence(datasets.Value("string")), | |
"pos_tags_prf": pos_tags_prf, | |
"pos_tags_ud": pos_tags_ud, | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
supervised_keys=None, | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
data_dir = os.path.join(dl_manager.download_and_extract(_URL), "utf8") | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"data_dir": data_dir, | |
"split": "train", | |
}, | |
) | |
] | |
def _generate_examples(self, data_dir, split): | |
"""Yields examples.""" | |
key = 0 | |
downloaded_files = [os.path.join(data_dir, fn) for fn in sorted(os.listdir(data_dir))] | |
for filepath in downloaded_files: | |
# Each file in the corpus contains one conversation | |
with open(filepath, encoding="utf-8") as f: | |
xml = f.read() | |
# Add dummy root node to form valid tree | |
xml = "<root>" + xml + "</root>" | |
tree = ET.fromstring(xml) | |
# Extract dialogue metadata | |
info = [line.strip() for line in tree.find("info").text.split("\n") if line and not line.endswith("END")] | |
tape_number = "".join(info[0].split("-")[1:]) | |
date_recorded = "".join(info[1].split("-")[1:]) | |
turn_number = -1 | |
for sent in tree.findall("sent"): | |
for child in sent.iter(): | |
if child.tag == "sent_head": | |
current_speaker = child.text.strip()[:-1] | |
turn_number += 1 | |
elif child.tag == "sent_tag": | |
tokens = [] | |
pos_prf = [] | |
pos_ud = [] | |
transcriptions = [] | |
current_sentence = [w.strip() for w in child.text.split("\n") if w and not w.isspace()] | |
for w in current_sentence: | |
token_data = w.split("/") | |
tokens.append(token_data[0]) | |
transcriptions.append(token_data[2]) | |
prf_tag = token_data[1].upper() | |
ud_tag = self.pos_map.get(prf_tag, "X") | |
pos_prf.append(prf_tag) | |
pos_ud.append(ud_tag) | |
num_tokens = len(tokens) | |
num_pos_tags = len(pos_prf) | |
num_transcriptions = len(transcriptions) | |
assert len(tokens) == len( | |
pos_prf | |
), f"Sizes do not match: {num_tokens} vs {num_pos_tags} for tokens vs pos-tags in {filepath}" | |
assert len(pos_prf) == len( | |
transcriptions | |
), f"Sizes do not match: {num_pos_tags} vs {num_transcriptions} for tokens vs pos-tags in {filepath}" | |
# Corpus doesn't come with conversation-level ids, and | |
# multiple texts can correspond to the same tape number, | |
# date, and speakers. | |
# The following workaround prepends metadata with the | |
# first few transcriptions in the conversation | |
# to create an identifier. | |
id_from_transcriptions = "".join(transcriptions[:5])[:5].upper() | |
id_ = f"{tape_number}-{date_recorded}-{id_from_transcriptions}" | |
yield key, { | |
"conversation_id": id_, | |
"speaker": current_speaker, | |
"turn_number": turn_number, | |
"tokens": tokens, | |
"transcriptions": transcriptions, | |
"pos_tags_prf": pos_prf, | |
"pos_tags_ud": pos_ud, | |
} | |
key += 1 | |