Group together metaphor annotations for phrases

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"}, "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}}
+{"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}}

vuamc.py

@@ -1,6 +1,7 @@
 """ English metaphor-annotated corpus. """
 
 import os
+from copy import deepcopy
 
 import datasets
 import logging
@@ -90,7 +91,15 @@ def resolve_recursively(el, ns):
             words.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]})
+
+            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:
@@ -198,23 +207,43 @@ def resolve_recursively(el, ns):
     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]]:
+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)
-            mtype = list(map(lambda met_info: {
-                "type": met_info["type"],
-                "word_indices": list(map(lambda _i: base + _i, met_info["word_indices"]))
-            }, mtype))
+            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
 
 
@@ -268,7 +297,7 @@ def parse_text_body(body_el, ns):
 class VUAMC(datasets.GeneratorBasedBuilder):
     """English metaphor-annotated corpus. """
 
-    VERSION = datasets.Version("1.0.0")
+    VERSION = datasets.Version("1.0.1")
 
     def _info(self):
         features = datasets.Features(