Add first version of SentiCoref loading script

README.md

@@ -1,3 +1,45 @@
----
-license: cc-by-sa-4.0
----
+---
+dataset_info:
+  features:
+  - name: id_doc
+    dtype: string
+  - name: words
+    sequence:
+      sequence:
+        sequence: string
+  - name: lemmas
+    sequence:
+      sequence:
+        sequence: string
+  - name: msds
+    sequence:
+      sequence:
+        sequence: string
+  - name: ne_tags
+    sequence:
+      sequence:
+        sequence: string
+  - name: mentions
+    list:
+    - name: id_mention
+      dtype: string
+    - name: mention_data
+      struct:
+      - name: idx_par
+        dtype: uint32
+      - name: idx_sent
+        dtype: uint32
+      - name: word_indices
+        sequence: uint32
+      - name: global_word_indices
+        sequence: uint32
+  - name: coref_clusters
+    sequence:
+      sequence: string
+  splits:
+  - name: train
+    num_bytes: 21547216
+    num_examples: 756
+  download_size: 21892324
+  dataset_size: 21547216
+---

dataset_infos.json

@@ -0,0 +1 @@
+{"default": {"description": "Slovene corpus for coreference resolution. Contains automatically(?) annotated named entities, manually annotated \ncoreferences, and manually verified lemmas and morphosyntactic tags.\n", "citation": "@misc{suk,\n    title = {Training corpus {SUK} 1.0},\n    author = {Arhar Holdt, {\u000b S}pela and Krek, Simon and Dobrovoljc, Kaja and Erjavec, Toma{\u000b z} and Gantar, Polona and {\u000b C}ibej, Jaka and Pori, Eva and Ter{\u000b c}on, Luka and Munda, Tina and {\u000b Z}itnik, Slavko and Robida, Nejc and Blagus, Neli and Mo{\u000b z}e, Sara and Ledinek, Nina and Holz, Nanika and Zupan, Katja and Kuzman, Taja and Kav{\u000b c}i{\u000b c}, Teja and {\u000b S}krjanec, Iza and Marko, Dafne and Jezer{\u000b s}ek, Lucija and Zajc, Anja},\n    url = {http://hdl.handle.net/11356/1747},\n    note = {Slovenian language resource repository {CLARIN}.{SI}},\n    year = {2022}\n}\n", "homepage": "http://hdl.handle.net/11356/1747", "license": "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)", "features": {"id_doc": {"dtype": "string", "id": null, "_type": "Value"}, "words": {"feature": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "lemmas": {"feature": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "msds": {"feature": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "ne_tags": {"feature": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}, "mentions": [{"id_mention": {"dtype": "string", "id": null, "_type": "Value"}, "mention_data": {"idx_par": {"dtype": "uint32", "id": null, "_type": "Value"}, "idx_sent": {"dtype": "uint32", "id": null, "_type": "Value"}, "word_indices": {"feature": {"dtype": "uint32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "global_word_indices": {"feature": {"dtype": "uint32", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}}], "coref_clusters": {"feature": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "senticoref", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 21632604, "num_examples": 756, "dataset_name": "senticoref"}}, "download_checksums": {"https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1747/SUK.TEI.zip": {"num_bytes": 20906601, "checksum": "ae81fd3712e277f9ec6b2b3b076eb80b50c01704d6e644ca932b2013108a8f99"}}, "download_size": 20906601, "post_processing_size": null, "dataset_size": 21632604, "size_in_bytes": 42539205}}

senticoref.py

@@ -0,0 +1,271 @@
+""" Slovene corpus for coreference resolution. """
+import os
+from collections import OrderedDict
+from copy import deepcopy
+from typing import Dict
+
+import datasets
+import xml.etree.ElementTree as ET
+import re
+
+
+_CITATION = """\
+@misc{suk,
+    title = {Training corpus {SUK} 1.1},
+    author = {Arhar Holdt, {\v S}pela and Krek, Simon and Dobrovoljc, Kaja and Erjavec, Toma{\v z} and Gantar, Polona and {\v C}ibej, Jaka and Pori, Eva and Ter{\v c}on, Luka and Munda, Tina and {\v Z}itnik, Slavko and Robida, Nejc and Blagus, Neli and Mo{\v z}e, Sara and Ledinek, Nina and Holz, Nanika and Zupan, Katja and Kuzman, Taja and Kav{\v c}i{\v c}, Teja and {\v S}krjanec, Iza and Marko, Dafne and Jezer{\v s}ek, Lucija and Zajc, Anja},
+    url = {http://hdl.handle.net/11356/1959},
+    note = {Slovenian language resource repository {CLARIN}.{SI}},
+    year = {2024}
+}
+"""
+
+_DESCRIPTION = """\
+Slovene corpus for coreference resolution. Contains automatically(?) annotated named entities, manually annotated 
+coreferences, and manually verified lemmas and morphosyntactic tags.
+"""
+
+_HOMEPAGE = "http://hdl.handle.net/11356/1959"
+
+_LICENSE = "Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)"
+
+_URLS = {
+    "suk.tei": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1959/SUK.TEI.zip",
+}
+
+
+XML_NAMESPACE = "{http://www.w3.org/XML/1998/namespace}"
+
+
+def namespace(element):
+    # https://stackoverflow.com/a/12946675
+    m = re.match(r'\{.*\}', element.tag)
+    return m.group(0) if m else ''
+
+
+def recursively_parse_el(el_tag, opened_ne: str = "O", opened_mentions: list = None) -> Dict:
+    """
+    :param el_tag: XML ETree tag
+    :param opened_ne: Named entity tag encountered at the previous level(s) of the recursive parse
+    :param opened_mentions: IDs of mentions encountered at the previous level(s) of the recursive parse.
+        The word in the current tag is part of these mentions.
+    """
+    eff_opened_mentions = opened_mentions if opened_mentions is not None else []
+    id_words, words, lemmas, msds, ne_tags = [], [], [], [], []
+    mention_to_id_word = {}
+
+    if el_tag.tag.endswith(("w", "pc")):
+        id_word = el_tag.attrib[f"{XML_NAMESPACE}id"]
+        word_str = el_tag.text.strip()
+        lemma_str = el_tag.attrib["lemma"]
+        msd_str = el_tag.attrib["ana"]
+
+        id_words.append(id_word)
+        words.append(word_str)
+        lemmas.append(lemma_str)
+        msds.append(msd_str)
+        ne_tags.append(opened_ne)
+
+        for _id in eff_opened_mentions:
+            _existing = mention_to_id_word.get(_id, [])
+            _existing.append(id_word)
+
+            mention_to_id_word[_id] = _existing
+
+    # Named entity or some other type of coreference mention
+    elif el_tag.tag.endswith("seg"):
+        new_ne = opened_ne
+        if el_tag.attrib["type"] == "name":
+            assert opened_ne == "O", f"Potentially encountered a nested NE ({opened_ne}, {el_tag['subtype'].upper()})"
+            new_ne = el_tag.attrib["subtype"].upper()
+
+            # Discard information about derived named entities
+            if new_ne.startswith("DERIV-"):
+                new_ne = new_ne[len("DERIV-"):]
+
+        # The mentions can be nested multiple levels, keep track of all mentions at current or shallower level
+        id_mention = el_tag.attrib[f"{XML_NAMESPACE}id"]
+        _opened_copy = deepcopy(eff_opened_mentions)
+        _opened_copy.append(id_mention)
+
+        for _i, _child in enumerate(el_tag):
+            _res = recursively_parse_el(_child, opened_ne=new_ne, opened_mentions=_opened_copy)
+
+            id_words.extend(_res["id_words"])
+            words.extend(_res["words"])
+            lemmas.extend(_res["lemmas"])
+            msds.extend(_res["msds"])
+            ne_tags.extend(_res["ne_tags"])
+
+            for _id_mention, _id_words in _res["mentions"].items():
+                _existing = mention_to_id_word.get(_id_mention, [])
+                _existing.extend(_id_words)
+                mention_to_id_word[_id_mention] = _existing
+
+        if new_ne != "O":  # IOB2
+            ne_tags = [f"B-{_tag}" if _i == 0 else f"I-{_tag}" for _i, _tag in enumerate(ne_tags)]
+
+    else:
+        print(f"WARNING: unrecognized tag in `recursively_parse_el`: {el_tag}. "
+              f"Please open an issue on the HuggingFace datasets repository.")
+
+    return {
+        "id_words": id_words, "words": words, "lemmas": lemmas, "msds": msds, "ne_tags": ne_tags,
+        "mentions": mention_to_id_word
+    }
+
+
+def parse_sent(sent_tag):
+    sent_info = {
+        "id_words": [], "words": [], "lemmas": [], "msds": [], "ne_tags": [],
+        "mentions": {}
+    }
+
+    for el in sent_tag:
+        if el.tag.endswith("linkGrp"):
+            # Parse coreference clusters later, outside of this function
+            continue
+
+        res = recursively_parse_el(el)
+        sent_info["id_words"].extend(res["id_words"])
+        sent_info["words"].extend(res["words"])
+        sent_info["lemmas"].extend(res["lemmas"])
+        sent_info["msds"].extend(res["msds"])
+        sent_info["ne_tags"].extend(res["ne_tags"])
+        sent_info["mentions"].update(res["mentions"])
+
+    return sent_info
+
+
+class SentiCoref(datasets.GeneratorBasedBuilder):
+    """Slovene corpus for coreference resolution."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id_doc": datasets.Value("string"),
+                "words": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("string")))),
+                "lemmas": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("string")))),
+                "msds": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("string")))),
+                "ne_tags": datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value("string")))),
+                "mentions": [{
+                    "id_mention": datasets.Value("string"),
+                    "mention_data": {
+                        "idx_par": datasets.Value("uint32"),
+                        "idx_sent": datasets.Value("uint32"),
+                        "word_indices": datasets.Sequence(datasets.Value("uint32")),
+                        "global_word_indices": datasets.Sequence(datasets.Value("uint32"))
+                    }
+                }],
+                "coref_clusters": datasets.Sequence(datasets.Sequence(datasets.Value("string")))
+            }
+        )
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        urls = _URLS["suk.tei"]
+        data_dir = dl_manager.download_and_extract(urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"file_path": os.path.join(data_dir, "SUK.TEI", "senticoref.xml")}
+            )
+        ]
+
+    def _generate_examples(self, file_path):
+        curr_doc = ET.parse(file_path)
+        root = curr_doc.getroot()
+        NAMESPACE = namespace(root)
+
+        for idx_doc, doc in enumerate(root.iterfind(f"{NAMESPACE}div")):
+            id2tokinfo = {}
+
+            doc_words, doc_lemmas, doc_msds, doc_ne_tags = [], [], [], []
+            doc_mentions = {}
+            doc_position = 0
+
+            # Step 1: Extract everything but the coreference clusters
+            # Clusters are marked at sentence level so they are often duplicated - find unique clusters afterwards
+            for idx_par, par in enumerate(doc.findall(f"{NAMESPACE}p")):
+                par_words, par_lemmas, par_msds, par_ne_tags = [], [], [], []
+
+                for idx_sent, sent in enumerate(par.findall(f"{NAMESPACE}s")):
+                    sent_data = parse_sent(sent)
+
+                    par_words.append(sent_data["words"])
+                    par_lemmas.append(sent_data["lemmas"])
+                    par_msds.append(sent_data["msds"])
+                    par_ne_tags.append(sent_data["ne_tags"])
+
+                    for pos_in_sent, (id_token, word_str, lemma_str, msd_str) in enumerate(zip(sent_data["id_words"],
+                                                                                               sent_data["words"],
+                                                                                               sent_data["lemmas"],
+                                                                                               sent_data["msds"])):
+                        id2tokinfo[id_token] = {
+                            "idx_par": idx_par, "idx_sent": idx_sent, "pos_in_sent": pos_in_sent,
+                            "doc_position": doc_position
+                        }
+                        doc_position += 1
+
+                    for id_mention, word_ids in sent_data["mentions"].items():
+                        mention_fmt = {
+                            "idx_par": idx_par, "idx_sent": idx_sent, "word_indices": [],
+                            "global_word_indices": []
+                        }
+
+                        for _id in word_ids:
+                            _info = id2tokinfo[_id]
+                            mention_fmt["word_indices"].append(_info["pos_in_sent"])
+                            mention_fmt["global_word_indices"].append(_info["doc_position"])
+
+                        doc_mentions[id_mention] = mention_fmt
+
+                doc_words.append(par_words)
+                doc_lemmas.append(par_lemmas)
+                doc_msds.append(par_msds)
+                doc_ne_tags.append(par_ne_tags)
+
+            # Step 2: extract coreference clusters
+            unique_clusters = OrderedDict()  # Preserving order just in case
+            for link_group in doc.findall(f".//{NAMESPACE}linkGrp[@type = 'COREF']"):
+                for link in link_group.findall(f"{NAMESPACE}link"):
+                    # Remove the reference marker ("#") in front of ID
+                    cluster = tuple(map(lambda _s: _s[1:], link.attrib["target"].split(" ")))
+                    unique_clusters[cluster] = None
+
+            doc_clusters = []
+            for cluster in unique_clusters:
+                doc_clusters.append(list(cluster))
+                for id_mention in cluster:
+                    if id_mention not in doc_mentions:
+                        # Mention may be a regular token, i.e. a word referring to an entity
+                        # (`id_mention` is then the ID of a token)
+                        _info = id2tokinfo[id_mention]
+                        doc_mentions[id_mention] = {
+                            "idx_par": _info["idx_par"], "idx_sent": _info["idx_sent"],
+                            "word_indices": [_info["pos_in_sent"]],
+                            "global_word_indices": [_info["doc_position"]]
+                        }
+
+            # Convert to list of dictionaries as datasets expects fixed key names
+            doc_mentions_list = []
+            for id_mention, mention_data in doc_mentions.items():
+                doc_mentions_list.append({
+                    "id_mention": id_mention,
+                    "mention_data": mention_data
+                })
+
+            yield idx_doc, {
+                "id_doc": doc.attrib[f"{XML_NAMESPACE}id"],
+                "words": doc_words, "lemmas": doc_lemmas, "msds": doc_msds, "ne_tags": doc_ne_tags,
+                "mentions": doc_mentions_list,
+                "coref_clusters": doc_clusters
+            }