komet.py

"""Metaphor corpus KOMET 1.0"""

import os
import re
import xml.etree.ElementTree as ET
from typing import List, Tuple

import datasets

_CITATION = """\
@InProceedings{antloga2020komet,
title = {Korpus metafor KOMET 1.0},
author={Antloga, \v{S}pela},
booktitle={Proceedings of the Conference on Language Technologies and Digital Humanities (Student abstracts)},
year={2020},
pages={167-170}
}
"""


_DESCRIPTION = """\
KOMET 1.0 is a hand-annotated corpus for metaphorical expressions which contains about 200,000 words from 
Slovene journalistic, fiction and on-line texts. 

To annotate metaphors in the corpus an adapted and modified procedure of the MIPVU protocol 
(Steen et al., 2010: A method for linguistic metaphor identification: From MIP to MIPVU, https://www.benjamins.com/catalog/celcr.14) 
was used. The lexical units (words) whose contextual meanings are opposed to their basic meanings are considered 
metaphor-related words. The basic and contextual meaning for each word in the corpus was identified using the 
Dictionary of the standard Slovene Language. The corpus was annotated for the metaphoric following relations: 
indirect metaphor (MRWi), direct metaphor (MRWd), borderline case (WIDLI) and metaphor signal (MFlag). 
In addition, the corpus introduces a new 'frame' tag, which gives information about the concept to which it refers.
"""

_HOMEPAGE = "http://hdl.handle.net/11356/1293"

_LICENSE = "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"

_URLS = {
    "komet": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1293/komet.tei.zip"
}


XML_NAMESPACE = "{http://www.w3.org/XML/1998/namespace}"
EL_LEAF, EL_TYPE, EL_FRAME = range(3)


def namespace(element):
    # https://stackoverflow.com/a/12946675
    m = re.match(r'\{.*\}', element.tag)
    return m.group(0) if m else ''


def word_info(sent_el):
    def _resolve_recursively(element) -> List:
        """ Knowingly ignored tags: name (anonymized, without IDs), gap, vocal, pause, del,
        linkGrp (syntactic dependencies) """
        # Leaf node: word or punctuation character
        if element.tag.endswith(("w", "pc")):
            id_curr = element.attrib[f"{XML_NAMESPACE}id"]
            return [(id_curr, element.text)]

        # Annotated word or word group - not interested in the annotations in this function
        elif element.tag.endswith("seg"):
            parsed_data = []
            for child in element:
                if child.tag.endswith("c"):  # empty space betw. words
                    continue

                res = _resolve_recursively(child)
                if isinstance(res, list):
                    parsed_data.extend(res)
                else:
                    parsed_data.append(res)

            return parsed_data

    id_words, words = [], []
    for child_el in sent_el:
        curr_annotations = _resolve_recursively(child_el)
        if curr_annotations is not None:  # None = unrecognized ("unimportant") element
            for ann in curr_annotations:
                id_words.append(ann[0])
                words.append(ann[1])

    return id_words, words


def seg_info(sent_el):
    def _resolve_recursively(element) -> Tuple:
        """ Returns (type[, subtype], deeper_elements, latest_element)"""
        # Leaf node: word or punctuation character
        if element.tag.endswith(("w", "pc")):
            id_curr = element.attrib[f"{XML_NAMESPACE}id"]
            return EL_LEAF, [], [id_curr]

        # Annotated word or word group
        elif element.tag.endswith("seg"):
            if element.attrib["subtype"] == "frame":
                ann_type, subtype = EL_FRAME, element.attrib["ana"]
                if subtype.startswith("#met."):  # for consistency with G-Komet, remove "#met." prefix from frames
                    subtype = subtype[5:]
            elif element.attrib["type"] == "metaphor":
                ann_type = EL_TYPE
                subtype = element.attrib["subtype"]
            else:
                raise ValueError(f"Unrecognized seg type: {element.attrib['type']}")

            deeper_elements = []
            latest_element = []
            for child in element:
                if child.tag.endswith("c"):  # empty space betw. words
                    continue

                res = _resolve_recursively(child)
                if res[0] == EL_LEAF:
                    latest_element.extend(res[2])
                else:
                    deeper_elements.extend(res[2])
                    deeper_elements.append((res[0], res[1], res[3]))
                    latest_element.extend(res[3])

            return ann_type, subtype, deeper_elements, latest_element

    annotations = []
    for child_el in sent_el:
        if not child_el.tag.endswith("seg"):
            continue

        ann_type, subtype, deeper_elements, latest_element = _resolve_recursively(child_el)
        annotations.extend(deeper_elements)
        annotations.append((ann_type, subtype, latest_element))

    return annotations


class Komet(datasets.GeneratorBasedBuilder):
    """KOMET is a hand-annotated Slovenian corpus of metaphorical expressions."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "document_name": datasets.Value("string"),
                "idx": datasets.Value("uint32"),  # index inside current document
                "idx_paragraph": datasets.Value("uint32"),
                "idx_sentence": datasets.Value("uint32"),  # index inside current paragraph
                "sentence_words": datasets.Sequence(datasets.Value("string")),
                "met_type": [{
                    "type": datasets.Value("string"),
                    "word_indices": datasets.Sequence(datasets.Value("uint32"))
                }],
                "met_frame": [{
                    "type": datasets.Value("string"),
                    "word_indices": datasets.Sequence(datasets.Value("uint32"))
                }]
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        data_dir = dl_manager.download_and_extract(_URLS["komet"])
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"data_dir": os.path.join(data_dir, "komet.tei")},
            )
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, data_dir):
        data_files = []
        for fname in os.listdir(data_dir):
            curr_path = os.path.join(data_dir, fname)
            if os.path.isfile(curr_path) and fname.endswith(".xml") and fname != "komet.xml":  # komet.xml = meta-file
                data_files.append(fname)
        data_files = sorted(data_files)

        idx_example = 0
        for fname in data_files:
            fpath = os.path.join(data_dir, fname)

            curr_doc = ET.parse(fpath)
            root = curr_doc.getroot()
            NAMESPACE = namespace(root)

            idx_sent_glob = 0
            for idx_par, curr_par in enumerate(root.iterfind(f".//{NAMESPACE}p")):
                id2position = {}  # {<idx_sent> -> {<id_word>: <position> foreach word} foreach sent}
                all_words = []

                # Pass#1: extract word information
                for idx_sent, curr_sent in enumerate(curr_par.iterfind(f"{NAMESPACE}s")):
                    id_words, words = word_info(curr_sent)

                    id2position[idx_sent] = dict(zip(id_words, range(len(words))))
                    all_words.append(words)

                all_types, all_frames = [], []

                # Pass#2: extract annotations from <seg>ments
                for idx_sent, curr_sent in enumerate(curr_par.iterfind(f"{NAMESPACE}s")):
                    annotated_segs = seg_info(curr_sent)
                    all_types.append([])
                    all_frames.append([])

                    for curr_ann in annotated_segs:
                        ann_type, ann_subtype, words_involved = curr_ann
                        if ann_type == EL_TYPE:
                            all_types[idx_sent].append({
                                "type": ann_subtype,
                                "word_indices": [id2position[idx_sent][_id_word] for _id_word in words_involved
                                                 if _id_word in id2position[idx_sent]]
                            })
                        elif ann_type == EL_FRAME:
                            all_frames[idx_sent].append({
                                "type": ann_subtype,
                                "word_indices": [id2position[idx_sent][_id_word] for _id_word in words_involved
                                                 if _id_word in id2position[idx_sent]]
                            })

                idx_sent = 0
                for curr_words, curr_types, curr_frames in zip(all_words, all_types, all_frames):
                    if len(curr_words) == 0:
                        continue

                    yield idx_example, {
                        "document_name": fname,
                        "idx": idx_sent_glob,
                        "idx_paragraph": idx_par,
                        "idx_sentence": idx_sent,
                        "sentence_words": curr_words,
                        "met_type": curr_types,
                        "met_frame": curr_frames
                    }
                    idx_example += 1
                    idx_sent += 1
                    idx_sent_glob += 1