Add first version of G-KOMET 1.0

dataset_infos.json

@@ -0,0 +1 @@
+{"default": {"description": "G-KOMET 1.0 (a corpus of metaphorical expressions in spoken Slovene language) is a corpus of speech transcriptions and \nconversations that covers 50,000 lexical units. The corpus contains samples from the Gos corpus of spoken Slovene \nand includes a balanced set of transcriptions of informative, educational, entertaining, private, and public discourse.\n\nThe annotation scheme was based on the MIPVU metaphor identification process. \nThis protocol was modified and adapted to the specifics of the Slovene language and the specifics of the spoken \nlanguage. Corpus was annotated for the following relations to metaphor: indirect metaphor, direct metaphor, borderline \ncases and metaphor signals. In addition, the corpus introduces a new \u2018frame\u2019 tag, which gives information about the \nconcept to which it refers. \n", "citation": "@InProceedings{antloga2022gkomet,\ntitle = {Korpusni pristopi za identifikacijo metafore in metonimije: primer metonimije v korpusu gKOMET},\nauthor={Antloga, \u000b{S}pela},\nbooktitle={Proceedings of the Conference on Language Technologies and Digital Humanities (Student papers)},\nyear={2022},\npages={271-277}\n}\n", "homepage": "http://hdl.handle.net/11356/1490", "license": "Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)", "features": {"document_name": {"dtype": "string", "id": null, "_type": "Value"}, "idx": {"dtype": "uint32", "id": null, "_type": "Value"}, "idx_paragraph": {"dtype": "uint32", "id": null, "_type": "Value"}, "idx_sentence": {"dtype": "uint32", "id": null, "_type": "Value"}, "sentence_words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "met_type": [{"type": {"dtype": "string", "id": null, "_type": "Value"}, "word_indices": {"feature": {"dtype": "uint32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}], "met_frame": [{"type": {"dtype": "string", "id": null, "_type": "Value"}, "word_indices": {"feature": {"dtype": "uint32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "g_komet", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 733608, "num_examples": 5695, "dataset_name": "g_komet"}}, "download_checksums": {"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1490/G-Komet.zip": {"num_bytes": 1005891, "checksum": "29fffdaf085b889926eedcd9673f2129513b4578f41ba9bf7c632fe50cd45a8f"}}, "download_size": 1005891, "post_processing_size": null, "dataset_size": 733608, "size_in_bytes": 1739499}}

gkomet.py

@@ -0,0 +1,287 @@
+"""Metaphor corpus G-KOMET 1.0"""
+import logging
+import os
+import re
+import xml.etree.ElementTree as ET
+from typing import List, Tuple
+
+import datasets
+
+_CITATION = """\
+@InProceedings{antloga2022gkomet,
+title = {Korpusni pristopi za identifikacijo metafore in metonimije: primer metonimije v korpusu gKOMET},
+author={Antloga, \v{S}pela},
+booktitle={Proceedings of the Conference on Language Technologies and Digital Humanities (Student papers)},
+year={2022},
+pages={271-277}
+}
+"""
+
+
+_DESCRIPTION = """\
+G-KOMET 1.0 (a corpus of metaphorical expressions in spoken Slovene language) is a corpus of speech transcriptions and 
+conversations that covers 50,000 lexical units. The corpus contains samples from the Gos corpus of spoken Slovene 
+and includes a balanced set of transcriptions of informative, educational, entertaining, private, and public discourse.
+
+The annotation scheme was based on the MIPVU metaphor identification process. 
+This protocol was modified and adapted to the specifics of the Slovene language and the specifics of the spoken 
+language. Corpus was annotated for the following relations to metaphor: indirect metaphor, direct metaphor, borderline 
+cases and metaphor signals. In addition, the corpus introduces a new ‘frame’ tag, which gives information about the 
+concept to which it refers. 
+"""
+
+_HOMEPAGE = "http://hdl.handle.net/11356/1490"
+
+_LICENSE = "Creative Commons - Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)"
+
+_URLS = {
+    "gkomet": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1490/G-Komet.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 (handled separately in linkgroup_info()) """
+        # 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", "vocal", "pause")):  # empty space betw. words or "special" word
+                    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"):
+            subtype = element.attrib["subtype"]
+            if element.attrib["type"] == "frame":
+                ann_type = EL_FRAME
+            elif element.attrib["type"] == "metaphor":
+                ann_type = EL_TYPE
+            elif element.attrib["type"] == "idiom":
+                ann_type = EL_TYPE
+            else:
+                raise ValueError(f"Unrecognized seg type: {element.attrib['type']}")
+
+            deeper_elements = []
+            latest_element = []
+            for child in element:
+                if child.tag.endswith(("c", "vocal", "pause")):  # empty space betw. words or "special" word
+                    continue
+
+                res = _resolve_recursively(child)
+                if res[0] == EL_LEAF:
+                    latest_element.extend(res[2])
+                else:
+                    deeper_elements.append(res)
+                    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(list(map(lambda _tup: (_tup[0], _tup[1], _tup[3]), deeper_elements)))
+        annotations.append((ann_type, subtype, latest_element))
+
+    return annotations
+
+
+def linkgroup_info(sent_el):
+    annotations = []
+    for child_el in sent_el:
+        if not child_el.tag.endswith("linkGrp"):
+            continue
+
+        for curr_link in child_el:
+            ann_type = EL_TYPE
+            if child_el.attrib["type"] not in {"metonymy", "frame", "metaphor", "idiom"}:
+                logging.warning(f"Uncovered linkGrp element type, skipping: {child_el.attrib['type']}")
+                continue
+
+            if child_el.attrib["type"] == "metonymy":
+                subtype = curr_link.attrib["ana"]
+            elif child_el.attrib["type"] in {"frame", "metaphor"}:
+                ann_type = EL_TYPE if child_el.attrib["type"] == "metaphor" else EL_FRAME
+                subtype = curr_link.attrib["ana"].split(":")[-1]
+            else:
+                subtype = "idiom"
+
+            tokens_involved = list(map(lambda _tok_id: _tok_id[1:] if _tok_id.startswith("#") else _tok_id,
+                                       curr_link.attrib["target"].split(" ")))
+            annotations.append((ann_type, subtype, tokens_involved))
+
+    return annotations
+
+
+class GKomet(datasets.GeneratorBasedBuilder):
+    """G-KOMET 1.0 is a corpus of metaphorical expressions in spoken Slovene language. """
+
+    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["gkomet"])
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"data_dir": os.path.join(data_dir, "G-Komet")},
+            )
+        ]
+
+    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 != "G-Komet.xml":  # G-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]]
+                            })
+
+                # Pass#3: extract annotations from <linkGrp>s
+                for idx_sent, curr_sent in enumerate(curr_par.iterfind(f"{NAMESPACE}s")):
+                    annotated_linkgroups = linkgroup_info(curr_sent)
+
+                    for curr_ann in annotated_linkgroups:
+                        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