Datasets:

matejklemen
/

vuamc

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README.md DELETED Viewed

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----
-annotations_creators:
-- expert-generated
-language:
-- en
-language_creators:
-- found
-license:
-- other
-multilinguality:
-- monolingual
-pretty_name: VUA Metaphor Corpus
-size_categories:
-- 10K<n<100K
-- 100K<n<1M
-source_datasets: []
-tags:
-- metaphor-classification
-- multiword-expression-detection
-- vua20
-- vua18
-- mipvu
-task_categories:
-- text-classification
-- token-classification
-task_ids:
-- multi-class-classification
----
-# Dataset Card for VUA Metaphor Corpus
-**Important note#1**: This is a slightly simplified but mostly complete parse of the corpus. What is missing are lemmas and some metadata that was not important at the time of writing the parser. See the section `Simplifications` for more information on this.
-**Important note#2**: The dataset contains metadata - to ignore it and correctly remap the annotations, see the section `Discarding metadata`.
-### Dataset Summary
-VUA Metaphor Corpus (VUAMC) contains a selection of excerpts from BNC-Baby files that have been annotated for metaphor. There are four registers, each comprising about 50 000 words: academic texts, news texts, fiction, and conversations.
-Words have been separately labelled as participating in multi-word expressions (about 1.5%) or as discarded for metaphor analysis (0.02%). Main categories include words that are related to metaphor (MRW), words that signal metaphor (MFlag), and words that are not related to metaphor. For metaphor-related words, subdivisions have been made between clear cases of metaphor versus borderline cases (WIDLII, When In Doubt, Leave It In). Another parameter of metaphor-related words makes a distinction between direct metaphor, indirect metaphor, and implicit metaphor.
-### Supported Tasks and Leaderboards
-Metaphor detection, metaphor type classification.
-### Languages
-English.
-## Dataset Structure
-### Data Instances
-A sample instance from the dataset:
-```
-{
-  'document_name': 'kcv-fragment42',
-  'words': ['', 'I', 'think', 'we', 'should', 'have', 'different', 'holidays', '.'],
-  'pos_tags': ['N/A', 'PNP', 'VVB', 'PNP', 'VM0', 'VHI', 'AJ0', 'NN2', 'PUN'],
-  'met_type': [
-    {'type': 'mrw/met', 'word_indices': [5]}
-  ],
-  'meta': ['vocal/laugh', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A', 'N/A']
-}
-```
-### Data Fields
-The instances are ordered as they appear in the corpus.
-- `document_name`: a string containing the name of the document in which the sentence appears;
-- `words`: words in the sentence (`""` when the word represents metadata);
-- `pos_tags`: POS tags of the words, encoded using the BNC basic tagset (`"N/A"` when the word does not have an associated POS tag);
-- `met_type`: metaphors in the sentence, marked by their type and word indices;
-- `meta`: selected metadata tags providing additional context to the sentence. Metadata may not correspond to a specific word. In this case, the metadata is represented with an empty string (`""`) in `words` and a `"N/A"` tag in `pos_tags`.
-## Dataset Creation
-For detailed information on the corpus, please check out the references in the `Citation Information` section or contact the dataset authors.
-## Simplifications
-The raw corpus is equipped with rich metadata and encoded in the TEI XML format. The textual part is fully parsed except for the lemmas, i.e. all the sentences in the raw corpus are present in the dataset.
-However, parsing the metadata fully is unnecessarily tedious, so certain simplifications were made:
-- paragraph information is not preserved as the dataset is parsed at sentence level;
-- manual corrections (`<corr>`) of incorrectly written words are ignored, and the original, incorrect form of the words is used instead;
-- `<ptr>` and `<anchor>` tags are ignored as I cannot figure out what they represent;
-- the attributes `rendition` (in `<hi>` tags) and `new` (in `<shift>` tags) are not exposed.
-## Discarding metadata
-The dataset contains rich metadata, which is stored in the `meta` attribute. To keep data aligned, empty words or `"N/A"`s are inserted into the other attributes. If you want to ignore the metadata and correct the metaphor type annotations, you can use code similar to the following snippet:
-```python3
-data = datasets.load_dataset("matejklemen/vuamc")["train"]
-data = data.to_pandas()
-for idx_ex in range(data.shape[0]):
-	curr_ex = data.iloc[idx_ex]
-	idx_remap = {}
-	for idx_word, word in enumerate(curr_ex["words"]):
-		if len(word) != 0:
-			idx_remap[idx_word] = len(idx_remap)
-	# Note that lists are stored as np arrays by datasets, while we are storing new data in a list!
-	# (unhandled for simplicity)
-	words, pos_tags, met_type = curr_ex[["words", "pos_tags", "met_type"]].tolist()
-	if len(idx_remap) != len(curr_ex["words"]):
-		words = list(filter(lambda _word: len(_word) > 0, curr_ex["words"]))
-		pos_tags = list(filter(lambda _pos: _pos != "N/A", curr_ex["pos_tags"]))
-		met_type = []
-		for met_info in curr_ex["met_type"]:
-			met_type.append({
-				"type": met_info["type"],
-				"word_indices": list(map(lambda _i: idx_remap[_i], met_info["word_indices"]))
-			})
-```
-## Additional Information
-### Dataset Curators
-Gerard Steen; et al. (please see http://hdl.handle.net/20.500.12024/2541 for the full list).
-### Licensing Information
-Available for non-commercial use on condition that the terms of the [BNC Licence](http://www.natcorp.ox.ac.uk/docs/licence.html) are observed and that this header is included in its entirety with any copy distributed.
-### Citation Information
-```
-@book{steen2010method,
-  title={A method for linguistic metaphor identification: From MIP to MIPVU},
-  author={Steen, Gerard and Dorst, Lettie and Herrmann, J. and Kaal, Anna and Krennmayr, Tina and Pasma, Trijntje},
-  volume={14},
-  year={2010},
-  publisher={John Benjamins Publishing}
-}
-```
-```
-@inproceedings{leong-etal-2020-report,
-    title = "A Report on the 2020 {VUA} and {TOEFL} Metaphor Detection Shared Task",
-    author = "Leong, Chee Wee (Ben)  and
-      Beigman Klebanov, Beata  and
-      Hamill, Chris  and
-      Stemle, Egon  and
-      Ubale, Rutuja  and
-      Chen, Xianyang",
-    booktitle = "Proceedings of the Second Workshop on Figurative Language Processing",
-    year = "2020",
-    url = "https://aclanthology.org/2020.figlang-1.3",
-    doi = "10.18653/v1/2020.figlang-1.3",
-    pages = "18--29"
-}
-```
-### Contributions
-Thanks to [@matejklemen](https://github.com/matejklemen) for adding this dataset.

dataset_infos.json DELETED Viewed

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- {"default": {"description": "The resource contains a selection of excerpts from BNC-Baby files that have been annotated for metaphor. \nThere are four registers, each comprising about 50,000 words: academic texts, news texts, fiction, and conversations. \nWords have been separately labelled as participating in multi-word expressions (about 1.5%) or as discarded for \nmetaphor analysis (0.02%). Main categories include words that are related to metaphor (MRW), words that signal \nmetaphor (MFlag), and words that are not related to metaphor. For metaphor-related words, subdivisions have been made \nbetween clear cases of metaphor versus borderline cases (WIDLII, When In Doubt, Leave It In). Another parameter of \nmetaphor-related words makes a distinction between direct metaphor, indirect metaphor, and implicit metaphor.\n", "citation": "@book{steen2010method,\n title={A method for linguistic metaphor identification: From MIP to MIPVU},\n author={Steen, Gerard and Dorst, Lettie and Herrmann, J. and Kaal, Anna and Krennmayr, Tina and Pasma, Trijntje},\n volume={14},\n year={2010},\n publisher={John Benjamins Publishing}\n}\n", "homepage": "https://hdl.handle.net/20.500.12024/2541", "license": "Available for non-commercial use on condition that the terms of the BNC Licence are observed and that this header is included in its entirety with any copy distributed.", "features": {"document_name": {"dtype": "string", "id": null, "_type": "Value"}, "words": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "pos_tags": {"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"}}], "meta": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "vuamc", "config_name": "default", "version": {"version_str": "1.0.1", "description": null, "major": 1, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 6487858, "num_examples": 16740, "dataset_name": "vuamc"}}, "download_checksums": {"https://ota.bodleian.ox.ac.uk/repository/xmlui/bitstream/handle/20.500.12024/2541/VUAMC.xml": {"num_bytes": 16820946, "checksum": "0ac1a77cc1879aa0c87e2879481d0e1e3f28e36b1701893c096a33ff11aa6e0d"}}, "download_size": 16820946, "post_processing_size": null, "dataset_size": 6487858, "size_in_bytes": 23308804}}

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vuamc.py DELETED Viewed

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-""" English metaphor-annotated corpus. """
-import os
-from copy import deepcopy
-import datasets
-import logging
-import re
-import xml.etree.ElementTree as ET
-from typing import List, Tuple, Dict
-_CITATION = """\
-@book{steen2010method,
-  title={A method for linguistic metaphor identification: From MIP to MIPVU},
-  author={Steen, Gerard and Dorst, Lettie and Herrmann, J. and Kaal, Anna and Krennmayr, Tina and Pasma, Trijntje},
-  volume={14},
-  year={2010},
-  publisher={John Benjamins Publishing}
-}
-"""
-_DESCRIPTION = """\
-The resource contains a selection of excerpts from BNC-Baby files that have been annotated for metaphor.
-There are four registers, each comprising about 50,000 words: academic texts, news texts, fiction, and conversations.
-Words have been separately labelled as participating in multi-word expressions (about 1.5%) or as discarded for
-metaphor analysis (0.02%). Main categories include words that are related to metaphor (MRW), words that signal
-metaphor (MFlag), and words that are not related to metaphor. For metaphor-related words, subdivisions have been made
-between clear cases of metaphor versus borderline cases (WIDLII, When In Doubt, Leave It In). Another parameter of
-metaphor-related words makes a distinction between direct metaphor, indirect metaphor, and implicit metaphor.
-"""
-_HOMEPAGE = "https://hdl.handle.net/20.500.12024/2541"
-_LICENSE = "Available for non-commercial use on condition that the terms of the BNC Licence are observed and that " \
-           "this header is included in its entirety with any copy distributed."
-_URLS = {
-    "vuamc": "https://ota.bodleian.ox.ac.uk/repository/xmlui/bitstream/handle/20.500.12024/2541/VUAMC.xml"
-}
-XML_NAMESPACE = "{http://www.w3.org/XML/1998/namespace}"
-VICI_NAMESPACE = "{http://www.tei-c.org/ns/VICI}"
-NA_STR = "N/A"
-def namespace(element):
-    # https://stackoverflow.com/a/12946675
-    m = re.match(r'\{.*\}', element.tag)
-    return m.group(0) if m else ''
-def resolve_recursively(el, ns):
-    words, pos_tags, met_type, meta_tags = [], [], [], []
-    if el.tag.endswith("w"):
-        # A <w>ord may be
-        #   (1) just text,
-        #   (2) a metaphor (text fully enclosed in another seg)
-        #   (3) a partial metaphor (optionally some text, followed by a seg, optionally followed by more text)
-        idx_word = 0
-        _w_text = el.text.strip() if el.text is not None else ""
-        if len(_w_text) > 0:
-            words.append(_w_text)
-            pos_tags.append(el.attrib["type"])
-            meta_tags.append(NA_STR)
-            idx_word += 1
-        met_els = el.findall(f"{ns}seg")
-        for met_el in met_els:
-            parse_tail = True
-            if met_el.text is None:
-                # Handle encoding inconsistency where the metaphor is encoded without a closing tag (I hate this format)
-                # <w lemma="to" type="PRP"><seg function="mrw" type="met" vici:morph="n"/>to </w>
-                parse_tail = False
-                _w_text = met_el.tail.strip()
-            else:
-                _w_text = met_el.text.strip()
-            curr_met_type = met_el.attrib[f"function"]
-            # Let the user decide how they want to aggregate metaphors
-            if "type" in met_el.attrib:
-                curr_met_type = f"{curr_met_type}/{met_el.attrib['type']}"
-            if "subtype" in met_el.attrib:
-                curr_met_type = f"{curr_met_type}/{met_el.attrib['subtype']}"
-            words.append(_w_text)
-            pos_tags.append(el.attrib["type"])
-            meta_tags.append(NA_STR)
-            met_dict = {"type": curr_met_type, "word_indices": [idx_word]}
-            # Multi-word metaphors are annotated with xml:id="..." or corresp="..."
-            if f"{XML_NAMESPACE}id" in met_el.attrib:
-                met_dict["id"] = met_el.attrib[f"{XML_NAMESPACE}id"]
-            elif "corresp" in met_el.attrib:
-                met_dict["id"] = met_el.attrib["corresp"][1:]  # remove the "#" in front
-            met_type.append(met_dict)
-            idx_word += 1
-            if not parse_tail:
-                continue
-            _w_text = met_el.tail.strip() if met_el.tail is not None else ""
-            if len(_w_text) > 0:
-                words.append(_w_text)
-                pos_tags.append(el.attrib["type"])
-                meta_tags.append(NA_STR)
-                idx_word += 1
-    elif el.tag.endswith("vocal"):
-        desc_el = el.find(f"{ns}desc")
-        description = desc_el.text.strip() if desc_el is not None else "unknown"
-        words.append("")
-        pos_tags.append(NA_STR)
-        meta_tags.append(f"vocal/{description}")  # vocal/<desc>
-    elif el.tag.endswith("gap"):
-        words.append("")
-        pos_tags.append(NA_STR)
-        meta_tags.append(f"gap/{el.attrib.get('reason', 'unclear')}")  # gap/<reason>
-    elif el.tag.endswith("incident"):
-        desc_el = el.find(f"{ns}desc")
-        description = desc_el.text.strip() if desc_el is not None else "unknown"
-        words.append("")
-        pos_tags.append(NA_STR)
-        meta_tags.append(f"incident/{description}")
-    elif el.tag.endswith("shift"):
-        # TODO: this is not exposed
-        new_state = el.attrib.get("new", "normal")
-        children = list(iter(el))
-        # NOTE: Intentionally skip shifts like this, without children:
-        # <u who="#PS05E"> <shift new="crying"/> </u>
-        if len(children) > 0:
-            for w_el in el:
-                _words, _pos, _mets, _metas = resolve_recursively(w_el, ns=ns)
-                words.extend(_words)
-                pos_tags.extend(_pos)
-                meta_tags.extend(_metas)
-    elif el.tag.endswith("seg"):
-        # Direct <seg> descendant of a sentence indicates truncated text
-        word_el = el.find(f"{ns}w")
-        words.append(word_el.text.strip())
-        pos_tags.append(word_el.attrib["type"])
-        meta_tags.append(NA_STR)
-    elif el.tag.endswith("pause"):
-        words.append("")
-        pos_tags.append(NA_STR)
-        meta_tags.append(f"pause")
-    elif el.tag.endswith("sic"):
-        for w_el in el:
-            _words, _pos, _mets, _metas = resolve_recursively(w_el, ns=ns)
-            words.extend(_words)
-            pos_tags.extend(_pos)
-            meta_tags.extend(_metas)
-    elif el.tag.endswith("c"):
-        words.append(el.text.strip())
-        pos_tags.append(el.attrib["type"])
-        meta_tags.append(NA_STR)
-    elif el.tag.endswith("pb"):
-        words.append("")
-        pos_tags.append(NA_STR)
-        meta_tags.append(NA_STR)
-    elif el.tag.endswith("hi"):
-        # TODO: this is not exposed
-        rendition = el.attrib.get("rend", "normal")
-        for child_el in el:
-            _words, _pos, _mets, _metas = resolve_recursively(child_el, ns=ns)
-            words.extend(_words)
-            pos_tags.extend(_pos)
-            meta_tags.extend(_metas)
-    elif el.tag.endswith("choice"):
-        sic_el = el.find(f"{ns}sic")
-        _words, _pos, _mets, _metas = resolve_recursively(sic_el, ns=ns)
-        words.extend(_words)
-        pos_tags.extend(_pos)
-        met_type.extend(_mets)
-        meta_tags.extend(_metas)
-    elif el.tag.endswith(("ptr", "corr")):
-        # Intentionally skipping these:
-        # - no idea what <ptr> is
-        # - <sic> is being parsed instead of <corr>
-        pass
-    else:
-        logging.warning(f"Unrecognized child element: {el.tag}.\n"
-                        f"If you are seeing this message, please open an issue on HF datasets.")
-    return words, pos_tags, met_type, meta_tags
-def parse_sent(sent_el, ns) -> Tuple[List[str], List[str], List[Dict], List[str]]:
-    all_words, all_pos_tags, all_met_types, all_metas = [], [], [], []
-    for child_el in sent_el:
-        word, pos, mtype, meta = resolve_recursively(child_el, ns=ns)
-        # Need to remap local (index inside the word group) `word_indices` to global (index inside the sentence)
-        if len(mtype) > 0:
-            base = len(all_words)
-            for idx_met, met_info in enumerate(mtype):
-                mtype[idx_met]["word_indices"] = list(map(lambda _i: base + _i, met_info["word_indices"]))
-        all_words.extend(word)
-        all_pos_tags.extend(pos)
-        all_met_types.extend(mtype)
-        all_metas.extend(meta)
-    # Check if any of the independent metaphor annotations belong to the same word group (e.g., "taking" and "over")
-    if len(all_met_types) > 0:
-        grouped_met_type = {}
-        for met_info in all_met_types:
-            curr_id = met_info.get("id", f"met{len(grouped_met_type)}")
-            if curr_id in grouped_met_type:
-                existing_data = grouped_met_type[curr_id]
-                existing_data["word_indices"].extend(met_info["word_indices"])
-            else:
-                existing_data = deepcopy(met_info)
-            grouped_met_type[curr_id] = existing_data
-        new_met_types = []
-        for _, met_info in grouped_met_type.items():
-            if "id" in met_info:
-                del met_info["id"]
-            new_met_types.append(met_info)
-        all_met_types = new_met_types
-    return all_words, all_pos_tags, all_met_types, all_metas
-def parse_text_body(body_el, ns):
-    all_words: List[List] = []
-    all_pos: List[List] = []
-    all_met_type: List[List] = []
-    all_meta: List[List] = []
-    # Edge case#1: <s>entence
-    if body_el.tag.endswith("s"):
-        words, pos_tags, met_types, meta_tags = parse_sent(body_el, ns=ns)
-        all_words.append(words)
-        all_pos.append(pos_tags)
-        all_met_type.append(met_types)
-        all_meta.append(meta_tags)
-    # Edge case#2: <u>tterance either contains a sentence of metadata or contains multiple sentences as children
-    elif body_el.tag.endswith("u"):
-        children = list(filter(lambda _child: not _child.tag.endswith("ptr"), list(iter(body_el))))
-        is_utterance_sent = all(map(lambda _child: not _child.tag.endswith("s"), children))
-        if is_utterance_sent:
-            # <u> contains elements as children that are not a <s>entence, so it is itself considered a sentence
-            words, pos_tags, met_types, meta_tags = parse_sent(body_el, ns=ns)
-            all_words.append(words)
-            all_pos.append(pos_tags)
-            all_met_type.append(met_types)
-            all_meta.append(meta_tags)
-        else:
-            # <u> contains one or more of <s>entence children
-            for _child in children:
-                words, pos_tags, met_types, meta_tags = parse_sent(_child, ns=ns)
-                all_words.append(words)
-                all_pos.append(pos_tags)
-                all_met_type.append(met_types)
-                all_meta.append(meta_tags)
-    # Recursively go deeper through all the <p>aragraphs, <div>s, etc. until we reach the sentences
-    else:
-        for _child in body_el:
-            _c_word, _c_pos, _c_met, _c_meta = parse_text_body(_child, ns=ns)
-            all_words.extend(_c_word)
-            all_pos.extend(_c_pos)
-            all_met_type.extend(_c_met)
-            all_meta.extend(_c_meta)
-    return all_words, all_pos, all_met_type, all_meta
-class VUAMC(datasets.GeneratorBasedBuilder):
-    """English metaphor-annotated corpus. """
-    VERSION = datasets.Version("1.0.1")
-    def _info(self):
-        features = datasets.Features(
-            {
-                "document_name": datasets.Value("string"),
-                "words": datasets.Sequence(datasets.Value("string")),
-                "pos_tags": datasets.Sequence(datasets.Value("string")),
-                "met_type": [{
-                    "type": datasets.Value("string"),
-                    "word_indices": datasets.Sequence(datasets.Value("uint32"))
-                }],
-                "meta": 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["vuamc"]
-        data_path = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={"file_path": os.path.join(data_path)}
-            )
-        ]
-    def _generate_examples(self, file_path):
-        curr_doc = ET.parse(file_path)
-        root = curr_doc.getroot()
-        NAMESPACE = namespace(root)
-        root = root.find(f"{NAMESPACE}text")
-        idx_instance = 0
-        for idx_doc, doc in enumerate(root.iterfind(f".//{NAMESPACE}text")):
-            document_name = doc.attrib[f"{XML_NAMESPACE}id"]
-            body = doc.find(f"{NAMESPACE}body")
-            body_data = parse_text_body(body, ns=NAMESPACE)
-            for sent_words, sent_pos, sent_met_type, sent_meta in zip(*body_data):
-                # TODO: Due to some simplifications (not parsing certain metadata), some sentences may be empty
-                if len(sent_words) == 0:
-                    continue
-                yield idx_instance, {
-                    "document_name": document_name,
-                    "words": sent_words,
-                    "pos_tags": sent_pos,
-                    "met_type": sent_met_type,
-                    "meta": sent_meta
-                }
-                idx_instance += 1