Datasets:
conll2012_ontonotesv5

	# 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.
	"""CoNLL2012 shared task data based on OntoNotes 5.0"""

	import glob
	import os
	from collections import defaultdict
	from typing import DefaultDict, Iterator, List, Optional, Tuple

	import datasets


	_CITATION = """\
	@inproceedings{pradhan-etal-2013-towards,
	title = "Towards Robust Linguistic Analysis using {O}nto{N}otes",
	author = {Pradhan, Sameer and
	Moschitti, Alessandro and
	Xue, Nianwen and
	Ng, Hwee Tou and
	Bj{\"o}rkelund, Anders and
	Uryupina, Olga and
	Zhang, Yuchen and
	Zhong, Zhi},
	booktitle = "Proceedings of the Seventeenth Conference on Computational Natural Language Learning",
	month = aug,
	year = "2013",
	address = "Sofia, Bulgaria",
	publisher = "Association for Computational Linguistics",
	url = "https://aclanthology.org/W13-3516",
	pages = "143--152",
	}

	Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, \
	Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, \
	Mohammed El-Bachouti, Robert Belvin, Ann Houston. \
	OntoNotes Release 5.0 LDC2013T19. \
	Web Download. Philadelphia: Linguistic Data Consortium, 2013.
	"""

	_DESCRIPTION = """\
	OntoNotes v5.0 is the final version of OntoNotes corpus, and is a large-scale, multi-genre,
	multilingual corpus manually annotated with syntactic, semantic and discourse information.

	This dataset is the version of OntoNotes v5.0 extended and is used in the CoNLL-2012 shared task.
	It includes v4 train/dev and v9 test data for English/Chinese/Arabic and corrected version v12 train/dev/test data (English only).

	The source of data is the Mendeley Data repo [ontonotes-conll2012](https://data.mendeley.com/datasets/zmycy7t9h9), which seems to be as the same as the official data, but users should use this dataset on their own responsibility.

	See also summaries from paperwithcode, [OntoNotes 5.0](https://paperswithcode.com/dataset/ontonotes-5-0) and [CoNLL-2012](https://paperswithcode.com/dataset/conll-2012-1)

	For more detailed info of the dataset like annotation, tag set, etc., you can refer to the documents in the Mendeley repo mentioned above.
	"""

	_URL = "https://data.mendeley.com/public-files/datasets/zmycy7t9h9/files/b078e1c4-f7a4-4427-be7f-9389967831ef/file_downloaded"


	class Conll2012Ontonotesv5Config(datasets.BuilderConfig):
	"""BuilderConfig for the CoNLL formatted OntoNotes dataset."""

	def __init__(self, language=None, conll_version=None, **kwargs):
	"""BuilderConfig for the CoNLL formatted OntoNotes dataset.

	Args:
	language: string, one of the language {"english", "chinese", "arabic"} .
	conll_version: string, "v4" or "v12". Note there is only English v12.
	**kwargs: keyword arguments forwarded to super.
	"""
	assert language in ["english", "chinese", "arabic"]
	assert conll_version in ["v4", "v12"]
	if conll_version == "v12":
	assert language == "english"
	super(Conll2012Ontonotesv5Config, self).__init__(
	name=f"{language}_{conll_version}",
	description=f"{conll_version} of CoNLL formatted OntoNotes dataset for {language}.",
	version=datasets.Version("1.0.0"), # hf dataset script version
	**kwargs,
	)
	self.language = language
	self.conll_version = conll_version


	class Conll2012Ontonotesv5(datasets.GeneratorBasedBuilder):
	"""The CoNLL formatted OntoNotes dataset."""

	BUILDER_CONFIGS = [
	Conll2012Ontonotesv5Config(
	language=lang,
	conll_version="v4",
	)
	for lang in ["english", "chinese", "arabic"]
	] + [
	Conll2012Ontonotesv5Config(
	language="english",
	conll_version="v12",
	)
	]

	def _info(self):
	lang = self.config.language
	conll_version = self.config.conll_version
	if lang == "arabic":
	pos_tag_feature = datasets.Value("string")
	else:
	tag_set = _POS_TAGS[f"{lang}_{conll_version}"]
	pos_tag_feature = datasets.ClassLabel(num_classes=len(tag_set), names=tag_set)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"document_id": datasets.Value("string"),
	"sentences": [
	{
	"part_id": datasets.Value("int32"),
	"words": datasets.Sequence(datasets.Value("string")),
	"pos_tags": datasets.Sequence(pos_tag_feature),
	"parse_tree": datasets.Value("string"),
	"predicate_lemmas": datasets.Sequence(datasets.Value("string")),
	"predicate_framenet_ids": datasets.Sequence(datasets.Value("string")),
	"word_senses": datasets.Sequence(datasets.Value("float32")),
	"speaker": datasets.Value("string"),
	"named_entities": datasets.Sequence(
	datasets.ClassLabel(num_classes=37, names=_NAMED_ENTITY_TAGS)
	),
	"srl_frames": [
	{
	"verb": datasets.Value("string"),
	"frames": datasets.Sequence(datasets.Value("string")),
	}
	],
	"coref_spans": datasets.Sequence(datasets.Sequence(datasets.Value("int32"), length=3)),
	}
	],
	}
	),
	homepage="https://conll.cemantix.org/2012/introduction.html",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	lang = self.config.language
	conll_version = self.config.conll_version
	dl_dir = dl_manager.download_and_extract(_URL)
	data_dir = os.path.join(dl_dir, f"conll-2012/{conll_version}/data")

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"conll_files_directory": os.path.join(data_dir, f"train/data/{lang}")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={"conll_files_directory": os.path.join(data_dir, f"development/data/{lang}")},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"conll_files_directory": os.path.join(data_dir, f"test/data/{lang}")},
	),
	]

	def _generate_examples(self, conll_files_directory):
	conll_files = sorted(glob.glob(os.path.join(conll_files_directory, "*/gold_conll"), recursive=True))
	for idx, conll_file in enumerate(conll_files):
	sentences = []
	for sent in Ontonotes().sentence_iterator(conll_file):
	document_id = sent.document_id
	sentences.append(
	{
	"part_id": sent.sentence_id, # should be part id, according to https://conll.cemantix.org/2012/data.html
	"words": sent.words,
	"pos_tags": sent.pos_tags,
	"parse_tree": sent.parse_tree,
	"predicate_lemmas": sent.predicate_lemmas,
	"predicate_framenet_ids": sent.predicate_framenet_ids,
	"word_senses": sent.word_senses,
	"speaker": sent.speakers[0],
	"named_entities": sent.named_entities,
	"srl_frames": [{"verb": f[0], "frames": f[1]} for f in sent.srl_frames],
	"coref_spans": [(c[0], *c[1]) for c in sent.coref_spans],
	}
	)
	yield idx, {"document_id": document_id, "sentences": sentences}


	# --------------------------------------------------------------------------------------------------------
	# Tag set
	_NAMED_ENTITY_TAGS = [
	"O", # out of named entity
	"B-PERSON",
	"I-PERSON",
	"B-NORP",
	"I-NORP",
	"B-FAC", # FACILITY
	"I-FAC",
	"B-ORG", # ORGANIZATION
	"I-ORG",
	"B-GPE",
	"I-GPE",
	"B-LOC",
	"I-LOC",
	"B-PRODUCT",
	"I-PRODUCT",
	"B-DATE",
	"I-DATE",
	"B-TIME",
	"I-TIME",
	"B-PERCENT",
	"I-PERCENT",
	"B-MONEY",
	"I-MONEY",
	"B-QUANTITY",
	"I-QUANTITY",
	"B-ORDINAL",
	"I-ORDINAL",
	"B-CARDINAL",
	"I-CARDINAL",
	"B-EVENT",
	"I-EVENT",
	"B-WORK_OF_ART",
	"I-WORK_OF_ART",
	"B-LAW",
	"I-LAW",
	"B-LANGUAGE",
	"I-LANGUAGE",
	]

	_POS_TAGS = {
	"english_v4": [
	"XX", # missing
	"``",
	"$",
	"''",
	",",
	"-LRB-", # (
	"-RRB-", # )
	".",
	":",
	"ADD",
	"AFX",
	"CC",
	"CD",
	"DT",
	"EX",
	"FW",
	"HYPH",
	"IN",
	"JJ",
	"JJR",
	"JJS",
	"LS",
	"MD",
	"NFP",
	"NN",
	"NNP",
	"NNPS",
	"NNS",
	"PDT",
	"POS",
	"PRP",
	"PRP$",
	"RB",
	"RBR",
	"RBS",
	"RP",
	"SYM",
	"TO",
	"UH",
	"VB",
	"VBD",
	"VBG",
	"VBN",
	"VBP",
	"VBZ",
	"WDT",
	"WP",
	"WP$",
	"WRB",
	], # 49
	"english_v12": [
	"XX", # misssing
	"``",
	"$",
	"''",
	"*",
	",",
	"-LRB-", # (
	"-RRB-", # )
	".",
	":",
	"ADD",
	"AFX",
	"CC",
	"CD",
	"DT",
	"EX",
	"FW",
	"HYPH",
	"IN",
	"JJ",
	"JJR",
	"JJS",
	"LS",
	"MD",
	"NFP",
	"NN",
	"NNP",
	"NNPS",
	"NNS",
	"PDT",
	"POS",
	"PRP",
	"PRP$",
	"RB",
	"RBR",
	"RBS",
	"RP",
	"SYM",
	"TO",
	"UH",
	"VB",
	"VBD",
	"VBG",
	"VBN",
	"VBP",
	"VBZ",
	"VERB",
	"WDT",
	"WP",
	"WP$",
	"WRB",
	], # 51
	"chinese_v4": [
	"X", # missing
	"AD",
	"AS",
	"BA",
	"CC",
	"CD",
	"CS",
	"DEC",
	"DEG",
	"DER",
	"DEV",
	"DT",
	"ETC",
	"FW",
	"IJ",
	"INF",
	"JJ",
	"LB",
	"LC",
	"M",
	"MSP",
	"NN",
	"NR",
	"NT",
	"OD",
	"ON",
	"P",
	"PN",
	"PU",
	"SB",
	"SP",
	"URL",
	"VA",
	"VC",
	"VE",
	"VV",
	], # 36
	}

	# --------------------------------------------------------------------------------------------------------
	# The CoNLL(2012) file reader
	# Modified the original code to get rid of extra package dependency.
	# Original code: https://github.com/allenai/allennlp-models/blob/main/allennlp_models/common/ontonotes.py


	class OntonotesSentence:
	"""
	A class representing the annotations available for a single CONLL formatted sentence.
	# Parameters
	document_id : `str`
	This is a variation on the document filename
	sentence_id : `int`
	The integer ID of the sentence within a document.
	words : `List[str]`
	This is the tokens as segmented/tokenized in the bank.
	pos_tags : `List[str]`
	This is the Penn-Treebank-style part of speech. When parse information is missing,
	all parts of speech except the one for which there is some sense or proposition
	annotation are marked with a XX tag. The verb is marked with just a VERB tag.
	parse_tree : `nltk.Tree`
	An nltk Tree representing the parse. It includes POS tags as pre-terminal nodes.
	When the parse information is missing, the parse will be `None`.
	predicate_lemmas : `List[Optional[str]]`
	The predicate lemma of the words for which we have semantic role
	information or word sense information. All other indices are `None`.
	predicate_framenet_ids : `List[Optional[int]]`
	The PropBank frameset ID of the lemmas in `predicate_lemmas`, or `None`.
	word_senses : `List[Optional[float]]`
	The word senses for the words in the sentence, or `None`. These are floats
	because the word sense can have values after the decimal, like `1.1`.
	speakers : `List[Optional[str]]`
	The speaker information for the words in the sentence, if present, or `None`
	This is the speaker or author name where available. Mostly in Broadcast Conversation
	and Web Log data. When not available the rows are marked with an "-".
	named_entities : `List[str]`
	The BIO tags for named entities in the sentence.
	srl_frames : `List[Tuple[str, List[str]]]`
	A dictionary keyed by the verb in the sentence for the given
	Propbank frame labels, in a BIO format.
	coref_spans : `Set[TypedSpan]`
	The spans for entity mentions involved in coreference resolution within the sentence.
	Each element is a tuple composed of (cluster_id, (start_index, end_index)). Indices
	are `inclusive`.
	"""

	def __init__(
	self,
	document_id: str,
	sentence_id: int,
	words: List[str],
	pos_tags: List[str],
	parse_tree: Optional[str],
	predicate_lemmas: List[Optional[str]],
	predicate_framenet_ids: List[Optional[str]],
	word_senses: List[Optional[float]],
	speakers: List[Optional[str]],
	named_entities: List[str],
	srl_frames: List[Tuple[str, List[str]]],
	coref_spans,
	) -> None:

	self.document_id = document_id
	self.sentence_id = sentence_id
	self.words = words
	self.pos_tags = pos_tags
	self.parse_tree = parse_tree
	self.predicate_lemmas = predicate_lemmas
	self.predicate_framenet_ids = predicate_framenet_ids
	self.word_senses = word_senses
	self.speakers = speakers
	self.named_entities = named_entities
	self.srl_frames = srl_frames
	self.coref_spans = coref_spans


	class Ontonotes:
	"""
	This `DatasetReader` is designed to read in the English OntoNotes v5.0 data
	in the format used by the CoNLL 2011/2012 shared tasks. In order to use this
	Reader, you must follow the instructions provided [here (v12 release):]
	(https://cemantix.org/data/ontonotes.html), which will allow you to download
	the CoNLL style annotations for the OntoNotes v5.0 release -- LDC2013T19.tgz
	obtained from LDC.
	Once you have run the scripts on the extracted data, you will have a folder
	structured as follows:
	```
	conll-formatted-ontonotes-5.0/
	── data
	├── development
	└── data
	└── english
	└── annotations
	├── bc
	├── bn
	├── mz
	├── nw
	├── pt
	├── tc
	└── wb
	├── test
	└── data
	└── english
	└── annotations
	├── bc
	├── bn
	├── mz
	├── nw
	├── pt
	├── tc
	└── wb
	└── train
	└── data
	└── english
	└── annotations
	├── bc
	├── bn
	├── mz
	├── nw
	├── pt
	├── tc
	└── wb
	```
	The file path provided to this class can then be any of the train, test or development
	directories(or the top level data directory, if you are not utilizing the splits).
	The data has the following format, ordered by column.
	1. Document ID : `str`
	This is a variation on the document filename
	2. Part number : `int`
	Some files are divided into multiple parts numbered as 000, 001, 002, ... etc.
	3. Word number : `int`
	This is the word index of the word in that sentence.
	4. Word : `str`
	This is the token as segmented/tokenized in the Treebank. Initially the `*_skel` file
	contain the placeholder [WORD] which gets replaced by the actual token from the
	Treebank which is part of the OntoNotes release.
	5. POS Tag : `str`
	This is the Penn Treebank style part of speech. When parse information is missing,
	all part of speeches except the one for which there is some sense or proposition
	annotation are marked with a XX tag. The verb is marked with just a VERB tag.
	6. Parse bit : `str`
	This is the bracketed structure broken before the first open parenthesis in the parse,
	and the word/part-of-speech leaf replaced with a `*`. When the parse information is
	missing, the first word of a sentence is tagged as `(TOP*` and the last word is tagged
	as `)` and all intermediate words are tagged with a ``.
	7. Predicate lemma : `str`
	The predicate lemma is mentioned for the rows for which we have semantic role
	information or word sense information. All other rows are marked with a "-".
	8. Predicate Frameset ID : `int`
	The PropBank frameset ID of the predicate in Column 7.
	9. Word sense : `float`
	This is the word sense of the word in Column 3.
	10. Speaker/Author : `str`
	This is the speaker or author name where available. Mostly in Broadcast Conversation
	and Web Log data. When not available the rows are marked with an "-".
	11. Named Entities : `str`
	These columns identifies the spans representing various named entities. For documents
	which do not have named entity annotation, each line is represented with an `*`.
	12. Predicate Arguments : `str`
	There is one column each of predicate argument structure information for the predicate
	mentioned in Column 7. If there are no predicates tagged in a sentence this is a
	single column with all rows marked with an `*`.
	-1. Co-reference : `str`
	Co-reference chain information encoded in a parenthesis structure. For documents that do
	not have co-reference annotations, each line is represented with a "-".
	"""

	def dataset_iterator(self, file_path: str) -> Iterator[OntonotesSentence]:
	"""
	An iterator over the entire dataset, yielding all sentences processed.
	"""
	for conll_file in self.dataset_path_iterator(file_path):
	yield from self.sentence_iterator(conll_file)

	@staticmethod
	def dataset_path_iterator(file_path: str) -> Iterator[str]:
	"""
	An iterator returning file_paths in a directory
	containing CONLL-formatted files.
	"""
	for root, _, files in list(os.walk(file_path)):
	for data_file in sorted(files):
	# These are a relic of the dataset pre-processing. Every
	# file will be duplicated - one file called filename.gold_skel
	# and one generated from the preprocessing called filename.gold_conll.
	if not data_file.endswith("gold_conll"):
	continue

	yield os.path.join(root, data_file)

	def dataset_document_iterator(self, file_path: str) -> Iterator[List[OntonotesSentence]]:
	"""
	An iterator over CONLL formatted files which yields documents, regardless
	of the number of document annotations in a particular file. This is useful
	for conll data which has been preprocessed, such as the preprocessing which
	takes place for the 2012 CONLL Coreference Resolution task.
	"""
	with open(file_path, "r", encoding="utf8") as open_file:
	conll_rows = []
	document: List[OntonotesSentence] = []
	for line in open_file:
	line = line.strip()
	if line != "" and not line.startswith("#"):
	# Non-empty line. Collect the annotation.
	conll_rows.append(line)
	else:
	if conll_rows:
	document.append(self._conll_rows_to_sentence(conll_rows))
	conll_rows = []
	if line.startswith("#end document"):
	yield document
	document = []
	if document:
	# Collect any stragglers or files which might not
	# have the '#end document' format for the end of the file.
	yield document

	def sentence_iterator(self, file_path: str) -> Iterator[OntonotesSentence]:
	"""
	An iterator over the sentences in an individual CONLL formatted file.
	"""
	for document in self.dataset_document_iterator(file_path):
	for sentence in document:
	yield sentence

	def _conll_rows_to_sentence(self, conll_rows: List[str]) -> OntonotesSentence:
	document_id: str = None
	sentence_id: int = None
	# The words in the sentence.
	sentence: List[str] = []
	# The pos tags of the words in the sentence.
	pos_tags: List[str] = []
	# the pieces of the parse tree.
	parse_pieces: List[str] = []
	# The lemmatised form of the words in the sentence which
	# have SRL or word sense information.
	predicate_lemmas: List[str] = []
	# The FrameNet ID of the predicate.
	predicate_framenet_ids: List[str] = []
	# The sense of the word, if available.
	word_senses: List[float] = []
	# The current speaker, if available.
	speakers: List[str] = []

	verbal_predicates: List[str] = []
	span_labels: List[List[str]] = []
	current_span_labels: List[str] = []

	# Cluster id -> List of (start_index, end_index) spans.
	clusters: DefaultDict[int, List[Tuple[int, int]]] = defaultdict(list)
	# Cluster id -> List of start_indices which are open for this id.
	coref_stacks: DefaultDict[int, List[int]] = defaultdict(list)

	for index, row in enumerate(conll_rows):
	conll_components = row.split()

	document_id = conll_components[0]
	sentence_id = int(conll_components[1])
	word = conll_components[3]
	pos_tag = conll_components[4]
	parse_piece = conll_components[5]

	# Replace brackets in text and pos tags
	# with a different token for parse trees.
	if pos_tag != "XX" and word != "XX":
	if word == "(":
	parse_word = "-LRB-"
	elif word == ")":
	parse_word = "-RRB-"
	else:
	parse_word = word
	if pos_tag == "(":
	pos_tag = "-LRB-"
	if pos_tag == ")":
	pos_tag = "-RRB-"
	(left_brackets, right_hand_side) = parse_piece.split("*")
	# only keep ')' if there are nested brackets with nothing in them.
	right_brackets = right_hand_side.count(")") * ")"
	parse_piece = f"{left_brackets} ({pos_tag} {parse_word}) {right_brackets}"
	else:
	# There are some bad annotations in the CONLL data.
	# They contain no information, so to make this explicit,
	# we just set the parse piece to be None which will result
	# in the overall parse tree being None.
	parse_piece = None

	lemmatised_word = conll_components[6]
	framenet_id = conll_components[7]
	word_sense = conll_components[8]
	speaker = conll_components[9]

	if not span_labels:
	# If this is the first word in the sentence, create
	# empty lists to collect the NER and SRL BIO labels.
	# We can't do this upfront, because we don't know how many
	# components we are collecting, as a sentence can have
	# variable numbers of SRL frames.
	span_labels = [[] for _ in conll_components[10:-1]]
	# Create variables representing the current label for each label
	# sequence we are collecting.
	current_span_labels = [None for _ in conll_components[10:-1]]

	self._process_span_annotations_for_word(conll_components[10:-1], span_labels, current_span_labels)

	# If any annotation marks this word as a verb predicate,
	# we need to record its index. This also has the side effect
	# of ordering the verbal predicates by their location in the
	# sentence, automatically aligning them with the annotations.
	word_is_verbal_predicate = any("(V" in x for x in conll_components[11:-1])
	if word_is_verbal_predicate:
	verbal_predicates.append(word)

	self._process_coref_span_annotations_for_word(conll_components[-1], index, clusters, coref_stacks)

	sentence.append(word)
	pos_tags.append(pos_tag)
	parse_pieces.append(parse_piece)
	predicate_lemmas.append(lemmatised_word if lemmatised_word != "-" else None)
	predicate_framenet_ids.append(framenet_id if framenet_id != "-" else None)
	word_senses.append(float(word_sense) if word_sense != "-" else None)
	speakers.append(speaker if speaker != "-" else None)

	named_entities = span_labels[0]
	srl_frames = [(predicate, labels) for predicate, labels in zip(verbal_predicates, span_labels[1:])]

	if all(parse_pieces):
	parse_tree = "".join(parse_pieces)
	else:
	parse_tree = None
	coref_span_tuples = {(cluster_id, span) for cluster_id, span_list in clusters.items() for span in span_list}
	return OntonotesSentence(
	document_id,
	sentence_id,
	sentence,
	pos_tags,
	parse_tree,
	predicate_lemmas,
	predicate_framenet_ids,
	word_senses,
	speakers,
	named_entities,
	srl_frames,
	coref_span_tuples,
	)

	@staticmethod
	def _process_coref_span_annotations_for_word(
	label: str,
	word_index: int,
	clusters: DefaultDict[int, List[Tuple[int, int]]],
	coref_stacks: DefaultDict[int, List[int]],
	) -> None:
	"""
	For a given coref label, add it to a currently open span(s), complete a span(s) or
	ignore it, if it is outside of all spans. This method mutates the clusters and coref_stacks
	dictionaries.
	# Parameters
	label : `str`
	The coref label for this word.
	word_index : `int`
	The word index into the sentence.
	clusters : `DefaultDict[int, List[Tuple[int, int]]]`
	A dictionary mapping cluster ids to lists of inclusive spans into the
	sentence.
	coref_stacks : `DefaultDict[int, List[int]]`
	Stacks for each cluster id to hold the start indices of active spans (spans
	which we are inside of when processing a given word). Spans with the same id
	can be nested, which is why we collect these opening spans on a stack, e.g:
	[Greg, the baker who referred to [himself]_ID1 as 'the bread man']_ID1
	"""
	if label != "-":
	for segment in label.split("\|"):
	# The conll representation of coref spans allows spans to
	# overlap. If spans end or begin at the same word, they are
	# separated by a "\|".
	if segment[0] == "(":
	# The span begins at this word.
	if segment[-1] == ")":
	# The span begins and ends at this word (single word span).
	cluster_id = int(segment[1:-1])
	clusters[cluster_id].append((word_index, word_index))
	else:
	# The span is starting, so we record the index of the word.
	cluster_id = int(segment[1:])
	coref_stacks[cluster_id].append(word_index)
	else:
	# The span for this id is ending, but didn't start at this word.
	# Retrieve the start index from the document state and
	# add the span to the clusters for this id.
	cluster_id = int(segment[:-1])
	start = coref_stacks[cluster_id].pop()
	clusters[cluster_id].append((start, word_index))

	@staticmethod
	def _process_span_annotations_for_word(
	annotations: List[str],
	span_labels: List[List[str]],
	current_span_labels: List[Optional[str]],
	) -> None:
	"""
	Given a sequence of different label types for a single word and the current
	span label we are inside, compute the BIO tag for each label and append to a list.
	# Parameters
	annotations : `List[str]`
	A list of labels to compute BIO tags for.
	span_labels : `List[List[str]]`
	A list of lists, one for each annotation, to incrementally collect
	the BIO tags for a sequence.
	current_span_labels : `List[Optional[str]]`
	The currently open span per annotation type, or `None` if there is no open span.
	"""
	for annotation_index, annotation in enumerate(annotations):
	# strip all bracketing information to
	# get the actual propbank label.
	label = annotation.strip("()*")

	if "(" in annotation:
	# Entering into a span for a particular semantic role label.
	# We append the label and set the current span for this annotation.
	bio_label = "B-" + label
	span_labels[annotation_index].append(bio_label)
	current_span_labels[annotation_index] = label
	elif current_span_labels[annotation_index] is not None:
	# If there's no '(' token, but the current_span_label is not None,
	# then we are inside a span.
	bio_label = "I-" + current_span_labels[annotation_index]
	span_labels[annotation_index].append(bio_label)
	else:
	# We're outside a span.
	span_labels[annotation_index].append("O")
	# Exiting a span, so we reset the current span label for this annotation.
	if ")" in annotation:
	current_span_labels[annotation_index] = None