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import xml.etree.cElementTree as ET
from typing import NamedTuple, Optional, List, Callable, Tuple, Iterable
from xml.dom import minidom
from src.utils.commons import execute_bash_command
pos_map = {
# U-POS
"NOUN": "n",
"VERB": "v",
"ADJ": "a",
"ADV": "r",
"PROPN": "n",
# PEN
"AFX": "a",
"JJ": "a",
"JJR": "a",
"JJS": "a",
"MD": "v",
"NN": "n",
"NNP": "n",
"NNPS": "n",
"NNS": "n",
"RB": "r",
"RP": "r",
"RBR": "r",
"RBS": "r",
"VB": "v",
"VBD": "v",
"VBG": "v",
"VBN": "v",
"VBP": "v",
"VBZ": "v",
"WRB": "r",
}
class AnnotatedToken(NamedTuple):
idx: int
text: str
pos: Optional[str] = None
lemma: Optional[str] = None
class WSDInstance(NamedTuple):
annotated_token: AnnotatedToken
labels: Optional[List[str]]
instance_id: Optional[str]
def read_from_raganato(
xml_path: str,
key_path: Optional[str] = None,
instance_transform: Optional[Callable[[WSDInstance], WSDInstance]] = None,
) -> Iterable[Tuple[str, str, List[WSDInstance]]]:
def read_by_text_iter(xml_path: str):
it = ET.iterparse(xml_path, events=("start", "end"))
_, root = next(it)
for event, elem in it:
if event == "end" and elem.tag == "text":
document_id = elem.attrib["id"]
for sentence in elem:
sentence_id = sentence.attrib["id"]
for word in sentence:
yield document_id, sentence_id, word
root.clear()
mapping = {}
if key_path is not None:
with open(key_path) as f:
for line in f:
line = line.strip()
wsd_instance, *labels = line.split(" ")
mapping[wsd_instance] = labels
last_seen_document_id = None
last_seen_sentence_id = None
for document_id, sentence_id, element in read_by_text_iter(xml_path):
if last_seen_sentence_id != sentence_id:
if last_seen_sentence_id is not None:
yield last_seen_document_id, last_seen_sentence_id, sentence
sentence = []
last_seen_document_id = document_id
last_seen_sentence_id = sentence_id
annotated_token = AnnotatedToken(
idx=len(sentence),
text=element.text,
pos=element.attrib.get("pos", None),
lemma=element.attrib.get("lemma", None),
)
wsd_instance = WSDInstance(
annotated_token=annotated_token,
labels=None
if element.tag == "wf" or element.attrib["id"] not in mapping
else mapping[element.attrib["id"]],
instance_id=None if element.tag == "wf" else element.attrib["id"],
)
if instance_transform is not None:
wsd_instance = instance_transform(wsd_instance)
sentence.append(wsd_instance)
yield last_seen_document_id, last_seen_sentence_id, sentence
def expand_raganato_path(path: str) -> Tuple[str, str]:
return f"{path}.data.xml", f"{path}.gold.key.txt"
class RaganatoBuilder:
def __init__(self, lang: Optional[str] = None, source: Optional[str] = None):
self.corpus = ET.Element("corpus")
self.current_text_section = None
self.current_sentence_section = None
self.gold_senses = []
if lang is not None:
self.corpus.set("lang", lang)
if source is not None:
self.corpus.set("source", source)
def open_text_section(self, text_id: str, text_source: str = None):
text_section = ET.SubElement(self.corpus, "text")
text_section.set("id", text_id)
if text_source is not None:
text_section.set("source", text_source)
self.current_text_section = text_section
def open_sentence_section(self, sentence_id: str, update_id: bool = True):
sentence_section = ET.SubElement(self.current_text_section, "sentence")
if update_id:
sentence_id = self.compute_id([self.current_text_section.attrib["id"], sentence_id])
sentence_section.set("id", sentence_id)
self.current_sentence_section = sentence_section
def add_annotated_token(
self,
token: str,
lemma: str,
pos: str,
instance_id: Optional[str] = None,
labels: Optional[List[str]] = None,
update_id: bool = False,
):
if instance_id is not None:
token_element = ET.SubElement(self.current_sentence_section, "instance")
if update_id:
instance_id = self.compute_id([self.current_sentence_section.attrib["id"], instance_id])
token_element.set("id", instance_id)
if labels is not None:
self.gold_senses.append((instance_id, " ".join(labels)))
else:
token_element = ET.SubElement(self.current_sentence_section, "wf")
token_element.set("lemma", lemma)
token_element.set("pos", pos)
token_element.text = token
@staticmethod
def compute_id(chain_ids: List[str]) -> str:
return ".".join(chain_ids)
def store(self, data_output_path: str, labels_output_path: str):
self.__store_xml(data_output_path)
self.__store_labels(labels_output_path)
def __store_xml(self, output_path: str):
corpus_writer = ET.ElementTree(self.corpus)
with tempfile.TemporaryDirectory() as tmp_dir:
with open(f"{tmp_dir}/tmp.xml", "wb") as f_xml:
corpus_writer.write(f_xml, encoding="UTF-8", xml_declaration=True)
execute_bash_command(f" xmllint --format {tmp_dir}/tmp.xml > {output_path}")
def __store_labels(self, output_path: str):
with open(output_path, "w") as f_labels:
for gold_sense in self.gold_senses:
f_labels.write(" ".join(gold_sense))
f_labels.write("\n")
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