Remove lemmas as the dataset is sometimes broken into morphemes, for which lemmas are not available

dataset_infos.json

@@ -1 +1 @@
-{"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"}, "lemmas": {"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": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 8512176, "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": 8512176, "size_in_bytes": 25333122}}
+{"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": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 6495566, "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": 6495566, "size_in_bytes": 23316512}}

vuamc.py

@@ -52,7 +52,7 @@ def namespace(element):
 
 
 def resolve_recursively(el, ns):
-    words, lemmas, pos_tags, met_type, meta_tags = [], [], [], [], []
+    words, pos_tags, met_type, meta_tags = [], [], [], []
 
     if el.tag.endswith("w"):
         # A <w>ord may be
@@ -63,7 +63,6 @@ def resolve_recursively(el, ns):
         _w_text = el.text.strip() if el.text is not None else ""
         if len(_w_text) > 0:
             words.append(_w_text)
-            lemmas.append(_w_text)
             pos_tags.append(el.attrib["type"])
             meta_tags.append(NA_STR)
             idx_word += 1
@@ -89,7 +88,6 @@ def resolve_recursively(el, ns):
                 curr_met_type = f"{curr_met_type}/{met_el.attrib['subtype']}"
 
             words.append(_w_text)
-            lemmas.append(_w_text)
             pos_tags.append(el.attrib["type"])
             meta_tags.append(NA_STR)
             met_type.append({"type": curr_met_type, "word_indices": [idx_word]})
@@ -101,7 +99,6 @@ def resolve_recursively(el, ns):
             _w_text = met_el.tail.strip() if met_el.tail is not None else ""
             if len(_w_text) > 0:
                 words.append(_w_text)
-                lemmas.append(_w_text)
                 pos_tags.append(el.attrib["type"])
                 meta_tags.append(NA_STR)
                 idx_word += 1
@@ -111,13 +108,11 @@ def resolve_recursively(el, ns):
         description = desc_el.text.strip() if desc_el is not None else "unknown"
 
         words.append("")
-        lemmas.append("")
         pos_tags.append(NA_STR)
         meta_tags.append(f"vocal/{description}")  # vocal/<desc>
 
     elif el.tag.endswith("gap"):
         words.append("")
-        lemmas.append("")
         pos_tags.append(NA_STR)
         meta_tags.append(f"gap/{el.attrib.get('reason', 'unclear')}")  # gap/<reason>
 
@@ -126,7 +121,6 @@ def resolve_recursively(el, ns):
         description = desc_el.text.strip() if desc_el is not None else "unknown"
 
         words.append("")
-        lemmas.append("")
         pos_tags.append(NA_STR)
         meta_tags.append(f"incident/{description}")
 
@@ -138,9 +132,8 @@ def resolve_recursively(el, ns):
         # <u who="#PS05E"> <shift new="crying"/> </u>
         if len(children) > 0:
             for w_el in el:
-                _words, _lemmas, _pos, _mets, _metas = resolve_recursively(w_el, ns=ns)
+                _words, _pos, _mets, _metas = resolve_recursively(w_el, ns=ns)
                 words.extend(_words)
-                lemmas.extend(_lemmas)
                 pos_tags.extend(_pos)
                 meta_tags.extend(_metas)
 
@@ -149,33 +142,28 @@ def resolve_recursively(el, ns):
         word_el = el.find(f"{ns}w")
 
         words.append(word_el.text.strip())
-        lemmas.append(word_el.attrib["lemma"])
         pos_tags.append(word_el.attrib["type"])
         meta_tags.append(NA_STR)
 
     elif el.tag.endswith("pause"):
         words.append("")
-        lemmas.append("")
         pos_tags.append(NA_STR)
         meta_tags.append(f"pause")
 
     elif el.tag.endswith("sic"):
         for w_el in el:
-            _words, _lemmas, _pos, _mets, _metas = resolve_recursively(w_el, ns=ns)
+            _words, _pos, _mets, _metas = resolve_recursively(w_el, ns=ns)
             words.extend(_words)
-            lemmas.extend(_lemmas)
             pos_tags.extend(_pos)
             meta_tags.extend(_metas)
 
     elif el.tag.endswith("c"):
         words.append(el.text.strip())
-        lemmas.append(el.text.strip())
         pos_tags.append(el.attrib["type"])
         meta_tags.append(NA_STR)
 
     elif el.tag.endswith("pb"):
         words.append("")
-        lemmas.append("")
         pos_tags.append(NA_STR)
         meta_tags.append(NA_STR)
 
@@ -184,17 +172,15 @@ def resolve_recursively(el, ns):
         rendition = el.attrib.get("rend", "normal")
 
         for child_el in el:
-            _words, _lemmas, _pos, _mets, _metas = resolve_recursively(child_el, ns=ns)
+            _words, _pos, _mets, _metas = resolve_recursively(child_el, ns=ns)
             words.extend(_words)
-            lemmas.extend(_lemmas)
             pos_tags.extend(_pos)
             meta_tags.extend(_metas)
 
     elif el.tag.endswith("choice"):
         sic_el = el.find(f"{ns}sic")
-        _words, _lemmas, _pos, _mets, _metas = resolve_recursively(sic_el, ns=ns)
+        _words, _pos, _mets, _metas = resolve_recursively(sic_el, ns=ns)
         words.extend(_words)
-        lemmas.extend(_lemmas)
         pos_tags.extend(_pos)
         met_type.extend(_mets)
         meta_tags.extend(_metas)
@@ -209,13 +195,13 @@ def resolve_recursively(el, ns):
         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, lemmas, pos_tags, met_type, meta_tags
+    return words, pos_tags, met_type, meta_tags
 
 
 def parse_sent(sent_el, ns) -> Tuple[List[str], List[str], List[str], List[Dict], List[str]]:
-    all_words, all_lemmas, all_pos_tags, all_met_types, all_metas = [], [], [], [], []
+    all_words, all_pos_tags, all_met_types, all_metas = [], [], [], []
     for child_el in sent_el:
-        word, lemma, pos, mtype, meta = resolve_recursively(child_el, ns=ns)
+        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)
@@ -225,26 +211,23 @@ def parse_sent(sent_el, ns) -> Tuple[List[str], List[str], List[str], List[Dict]
             }, mtype))
 
         all_words.extend(word)
-        all_lemmas.extend(lemma)
         all_pos_tags.extend(pos)
         all_met_types.extend(mtype)
         all_metas.extend(meta)
 
-    return all_words, all_lemmas, all_pos_tags, all_met_types, all_metas
+    return all_words, all_pos_tags, all_met_types, all_metas
 
 
 def parse_text_body(body_el, ns):
     all_words: List[List] = []
-    all_lemmas: 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, lemmas, pos_tags, met_types, meta_tags = parse_sent(body_el, ns=ns)
+        words, pos_tags, met_types, meta_tags = parse_sent(body_el, ns=ns)
         all_words.append(words)
-        all_lemmas.append(lemmas)
         all_pos.append(pos_tags)
         all_met_type.append(met_types)
         all_meta.append(meta_tags)
@@ -255,18 +238,16 @@ def parse_text_body(body_el, ns):
         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, lemmas, pos_tags, met_types, meta_tags = parse_sent(body_el, ns=ns)
+            words, pos_tags, met_types, meta_tags = parse_sent(body_el, ns=ns)
             all_words.append(words)
-            all_lemmas.append(lemmas)
             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, lemmas, pos_tags, met_types, meta_tags = parse_sent(_child, ns=ns)
+                words, pos_tags, met_types, meta_tags = parse_sent(_child, ns=ns)
                 all_words.append(words)
-                all_lemmas.append(lemmas)
                 all_pos.append(pos_tags)
                 all_met_type.append(met_types)
                 all_meta.append(meta_tags)
@@ -274,15 +255,14 @@ def parse_text_body(body_el, ns):
     # 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_lemmas, _c_pos, _c_met, _c_meta = parse_text_body(_child, ns=ns)
+            _c_word, _c_pos, _c_met, _c_meta = parse_text_body(_child, ns=ns)
 
             all_words.extend(_c_word)
-            all_lemmas.extend(_c_lemmas)
             all_pos.extend(_c_pos)
             all_met_type.extend(_c_met)
             all_meta.extend(_c_meta)
 
-    return all_words, all_lemmas, all_pos, all_met_type, all_meta
+    return all_words, all_pos, all_met_type, all_meta
 
 
 class VUAMC(datasets.GeneratorBasedBuilder):
@@ -295,7 +275,6 @@ class VUAMC(datasets.GeneratorBasedBuilder):
             {
                 "document_name": datasets.Value("string"),
                 "words": datasets.Sequence(datasets.Value("string")),
-                "lemmas": datasets.Sequence(datasets.Value("string")),
                 "pos_tags": datasets.Sequence(datasets.Value("string")),
                 "met_type": [{
                     "type": datasets.Value("string"),
@@ -335,7 +314,7 @@ class VUAMC(datasets.GeneratorBasedBuilder):
             body = doc.find(f"{NAMESPACE}body")
             body_data = parse_text_body(body, ns=NAMESPACE)
 
-            for sent_words, sent_lemmas, sent_pos, sent_met_type, sent_meta in zip(*body_data):
+            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
@@ -343,7 +322,6 @@ class VUAMC(datasets.GeneratorBasedBuilder):
                 yield idx_instance, {
                     "document_name": document_name,
                     "words": sent_words,
-                    "lemmas": sent_lemmas,
                     "pos_tags": sent_pos,
                     "met_type": sent_met_type,
                     "meta": sent_meta