adding prmu categories

README.md

@@ -10,6 +10,11 @@ Details about the inspec dataset can be found in the original paper:
    [Improved automatic keyword extraction given more linguistic knowledge](https://aclanthology.org/W03-1028). 
    In Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pages 216-223.
 
+Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper:
+ - Florian Boudin and Ygor Gallina. 2021.
+   [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/). 
+   In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
+ 
 ## Content
 
 The dataset is divided into the following three splits:
@@ -25,5 +30,6 @@ The following data fields are available :
 - **id**: unique identifier of the document.
 - **title**: title of the document.
 - **abstract**: abstract of the document.
-- **keyphrases**: list of reference keyphrases (uncontrolled).
+- **keyphrases**: list of reference keyphrases.
+- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
 

inspec.py

@@ -59,7 +59,6 @@ class Inspec(datasets.GeneratorBasedBuilder):
     # data = datasets.load_dataset('my_dataset', 'second_domain')
     BUILDER_CONFIGS = [
         datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data."),
-        datasets.BuilderConfig(name="preprocessed", version=VERSION, description="This part of my dataset covers the preprocessed data."),
     ]
 
     DEFAULT_CONFIG_NAME = "raw"  # It's not mandatory to have a default configuration. Just use one if it make sense.
@@ -73,15 +72,7 @@ class Inspec(datasets.GeneratorBasedBuilder):
                     "title": datasets.Value("string"),
                     "abstract": datasets.Value("string"),
                     "keyphrases": datasets.features.Sequence(datasets.Value("string")),
-                }
-            )
-        else:
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("int64"),
-                    "title": datasets.Value("string"),
-                    "abstract": datasets.Value("string"),
-                    "keyphrases": datasets.features.Sequence(datasets.Value("string")),
+                    "prmu": datasets.features.Sequence(datasets.Value("string")),
                 }
             )
         return datasets.DatasetInfo(
@@ -143,18 +134,12 @@ class Inspec(datasets.GeneratorBasedBuilder):
         with open(filepath, encoding="utf-8") as f:
             for key, row in enumerate(f):
                 data = json.loads(row)
-                if self.config.name == "raw":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "id": data["id"],
-                        "title": data["title"],
-                        "abstract": data["abstract"],
-                        "keyphrases":  data["keyphrases"],
-                    }
-                else:
-                    yield key, {
-                        "id": data["id"],
-                        "title": data["title"],
-                        "abstract": data["abstract"],
-                        "keyphrases":  data["keyphrases"],
-                    }
+                # Yields examples as (key, example) tuples
+                yield key, {
+                    "id": data["id"],
+                    "title": data["title"],
+                    "abstract": data["abstract"],
+                    "keyphrases":  data["keyphrases"],
+                    "prmu": data["prmu"],
+                }
+