Add first version of dataset script

README.md

@@ -1,3 +1,21 @@
 ---
 license: cc-by-sa-4.0
+dataset_info:
+  features:
+  - name: id
+    dtype: string
+  - name: words
+    sequence: string
+  - name: lemmas
+    sequence: string
+  - name: msds
+    sequence: string
+  - name: nes
+    sequence: string
+  splits:
+  - name: train
+    num_bytes: 2652674
+    num_examples: 2957
+  download_size: 2871765
+  dataset_size: 2652674
 ---

janes_tag.py

@@ -0,0 +1,157 @@
+import os
+import re
+import xml.etree.ElementTree as ET
+
+import datasets
+
+
+_CITATION = """\
+@misc{janes_tag,
+    title = {{CMC} training corpus Janes-Tag 3.0},
+    author = {Lenardi{\v c}, Jakob and {\v C}ibej, Jaka and Arhar Holdt, {\v S}pela and Erjavec, Toma{\v z} and Fi{\v s}er, Darja and Ljube{\v s}i{\'c}, Nikola and Zupan, Katja and Dobrovoljc, Kaja},
+    url = {http://hdl.handle.net/11356/1732},
+    note = {Slovenian language resource repository {CLARIN}.{SI}},
+    copyright = {Creative Commons - Attribution-{ShareAlike} 4.0 International ({CC} {BY}-{SA} 4.0)},
+    year = {2022}
+}
+"""
+
+
+_DESCRIPTION = """\
+Janes-Tag is a manually annotated corpus of Slovene Computer-Mediated Communication (CMC) consisting of mostly tweets 
+but also blogs, forums and news comments.
+"""
+
+_HOMEPAGE = "https://nl.ijs.si/janes/"
+
+_LICENSE = "Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)"
+
+_URLS = {
+    "janes_tag_tei": "https://www.clarin.si/repository/xmlui/bitstream/handle/11356/1732/Janes-Tag.3.0.TEI.zip"
+}
+
+XML_NAMESPACE = "{http://www.w3.org/XML/1998/namespace}"
+DEFAULT_NE = "O"
+
+
+def namespace(element):
+    # https://stackoverflow.com/a/12946675
+    m = re.match(r'\{.*\}', element.tag)
+    return m.group(0) if m else ''
+
+
+def word_info(wordlike_tag, _namespace):
+    if wordlike_tag.tag == f"{_namespace}c":
+        return None, None, None, None
+
+    if wordlike_tag.tag in {f"{_namespace}w", f"{_namespace}pc"}:
+        nes = None
+        if "lemma" in wordlike_tag.attrib:
+            words = [wordlike_tag.text.strip()]
+            lemmas = [wordlike_tag.attrib["lemma"].strip()]
+            msds = [wordlike_tag.attrib["ana"].strip()]
+
+        # If this happens, the word contains nested words indicating its normalized form
+        else:
+            words, lemmas, msds = [], [], []
+            for _child in wordlike_tag:
+                words.append(_child.attrib["norm"].strip())
+                lemmas.append(_child.attrib["lemma"].strip())
+                msds.append(_child.attrib["ana"].strip())
+
+        return words, lemmas, msds, nes
+
+    words, lemmas, msds, nes = [], [], [], []
+
+    if wordlike_tag.tag == f"{_namespace}seg":
+        ne_tag = wordlike_tag.attrib["subtype"].strip().upper()
+
+        for _child in wordlike_tag:
+            _child_words, _child_lemmas, _child_msds, _child_nes = word_info(_child, _namespace)
+
+            if _child_words is None:
+                continue
+
+            words.extend(_child_words)
+            lemmas.extend(_child_lemmas)
+            msds.extend(_child_msds)
+
+        nes = [f"B-{ne_tag}" if _i == 0 else f"I-{ne_tag}" for _i, _ in enumerate(words)]
+
+        return words, lemmas, msds, nes
+
+
+class JanesTag(datasets.GeneratorBasedBuilder):
+    """Janes-Tag is a manually annotated corpus of Slovene Computer-Mediated Communication"""
+
+    VERSION = datasets.Version("3.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "id": datasets.Value("string"),
+                "words": datasets.Sequence(datasets.Value("string")),
+                "lemmas": datasets.Sequence(datasets.Value("string")),
+                "msds": datasets.Sequence(datasets.Value("string")),
+                "nes": 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["janes_tag_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, "Janes-Tag.3.0.TEI", "janes-tag.xml")}
+            )
+        ]
+
+    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").find(f"{NAMESPACE}body")
+
+        idx_ex = 0
+        for curr_ex in root.iterfind(f"{NAMESPACE}ab"):  # anonymous block
+            curr_id = curr_ex.attrib[f"{XML_NAMESPACE}id"]
+            ex_words, ex_lemmas, ex_msds, ex_nes = [], [], [], []
+            for child_tag in curr_ex:
+                if child_tag.tag not in {f"{NAMESPACE}s", f"{NAMESPACE}c"}:
+                    continue
+
+                if child_tag.tag == f"{NAMESPACE}c":
+                    continue
+
+                # Iterate over elements of a <s>entence
+                for word_or_seg_tag in child_tag:
+                    _words, _lemmas, _msds, _nes = word_info(word_or_seg_tag, NAMESPACE)
+
+                    if _words is None:
+                        continue
+
+                    if _nes is None:
+                        _nes = [DEFAULT_NE for _ in range(len(_words))]
+
+                    ex_words.extend(_words)
+                    ex_lemmas.extend(_lemmas)
+                    ex_msds.extend(_msds)
+                    ex_nes.extend(_nes)
+
+            yield idx_ex, {
+                "id": curr_id,
+                "words": ex_words,
+                "lemmas": ex_lemmas,
+                "msds": ex_msds,
+                "nes": ex_nes
+            }
+            idx_ex += 1