update files with spacy tokenizer

README.md

@@ -5,25 +5,24 @@
 Inspec is a dataset for benchmarking keyphrase extraction and generation models.
 The dataset is composed of 2,000 abstracts of scientific papers collected from the [Inspec database](https://www.theiet.org/resources/inspec/).
 Keyphrases were annotated by professional indexers in an uncontrolled setting (that is, not limited to thesaurus entries).
-Details about the inspec dataset can be found in the original paper:
- - Anette Hulth. 2003.
-   [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.
+Details about the inspec dataset can be found in the original paper [(Hulth, 2003)][hulth-2003].
  
-## Content
+
+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 [(Boudin and Gallina, 2021)][boudin-2021].
+
+Text pre-processing (tokenization) is carried out using `spacy` (`en_core_web_sm` model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token).
+Stemming (Porter's stemmer implementation provided in `nltk`) is performed before reference keyphrases are matched against the source text.
+Details about the process can be found in `prmu.py`.
+
+## Content and statistics
 
 The dataset is divided into the following three splits:
 
 | Split      | # documents | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
 | :--------- | ----------: | -----------: | --------: | ----------: | ------: | -------: |
 | Train      | 1,000       | 9.79         | 77.83     | 9.90        | 6.30    | 5.98     |
-| Test       | 500         | 9.83         | 78.49     | 9.82        | 6.76    | 4.92     |
 | Validation | 500         | 9.15         | 77.90     | 9.82        | 6.74    | 5.54     |
+| Test       | 500         | 9.83         | 78.49     | 9.82        | 6.76    | 4.92     |
 
 The following data fields are available :
 
@@ -33,3 +32,14 @@ The following data fields are available :
 - **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.
 
+## References
+
+- (Hulth, 2003) Anette Hulth. 2003.
+  [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.
+- (Boudin and Gallina, 2021) 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.
+
+[hulth-2003]: https://aclanthology.org/W03-1028/
+[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/

prmu.py

@@ -0,0 +1,99 @@
+# -*- coding: utf-8 -*-
+
+import sys
+import json
+import spacy
+
+from nltk.stem.snowball import SnowballStemmer as Stemmer
+
+nlp = spacy.load("en_core_web_sm")
+
+# https://spacy.io/usage/linguistic-features#native-tokenizer-additions
+
+from spacy.lang.char_classes import ALPHA, ALPHA_LOWER, ALPHA_UPPER
+from spacy.lang.char_classes import CONCAT_QUOTES, LIST_ELLIPSES, LIST_ICONS
+from spacy.util import compile_infix_regex
+
+# Modify tokenizer infix patterns
+infixes = (
+    LIST_ELLIPSES
+    + LIST_ICONS
+    + [
+        r"(?<=[0-9])[+\-\*^](?=[0-9-])",
+        r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
+            al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
+        ),
+        r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
+        # ✅ Commented out regex that splits on hyphens between letters:
+        # r"(?<=[{a}])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
+        r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
+    ]
+)
+
+infix_re = compile_infix_regex(infixes)
+nlp.tokenizer.infix_finditer = infix_re.finditer
+
+
+def contains(subseq, inseq):
+    return any(inseq[pos:pos + len(subseq)] == subseq for pos in range(0, len(inseq) - len(subseq) + 1))
+
+
+def find_pmru(tok_title, tok_text, tok_kp):
+    """Find PRMU category of a given keyphrase."""
+
+    # if kp is present
+    if contains(tok_kp, tok_title) or contains(tok_kp, tok_text):
+        return "P"
+
+    # if kp is considered as absent
+    else:
+
+        # find present and absent words
+        present_words = [w for w in tok_kp if w in tok_title or w in tok_text]
+
+        # if "all" words are present
+        if len(present_words) == len(tok_kp):
+            return "R"
+        # if "some" words are present
+        elif len(present_words) > 0:
+            return "M"
+        # if "no" words are present
+        else:
+            return "U"
+
+
+if __name__ == '__main__':
+
+    data = []
+
+    # read the dataset
+    with open(sys.argv[1], 'r') as f:
+        # loop through the documents
+        for line in f:
+            doc = json.loads(line.strip())
+
+            title_spacy = nlp(doc['title'])
+            abstract_spacy = nlp(doc['abstract'])
+
+            title_tokens = [token.text for token in title_spacy]
+            abstract_tokens = [token.text for token in abstract_spacy]
+
+            title_stems = [Stemmer('porter').stem(w.lower()) for w in title_tokens]
+            abstract_stems = [Stemmer('porter').stem(w.lower()) for w in abstract_tokens]
+
+            keyphrases_stems = []
+            for keyphrase in doc['keyphrases']:
+                keyphrase_spacy = nlp(keyphrase)
+                keyphrase_tokens = [token.text for token in keyphrase_spacy]
+                keyphrase_stems = [Stemmer('porter').stem(w.lower()) for w in keyphrase_tokens]
+                keyphrases_stems.append(keyphrase_stems)
+
+            prmu = [find_pmru(title_stems, abstract_stems, kp) for kp in keyphrases_stems]
+            doc['prmu'] = prmu
+
+            data.append(json.dumps(doc))
+            print(doc['id'])
+
+    # write the json
+    with open(sys.argv[2], 'w') as o:
+        o.write("\n".join(data))