updating stats and prmu categories

README.md

@@ -38,9 +38,9 @@ Details about the process can be found in `prmu.py`.
 
 The dataset contains the following test split:
 
-| Split      | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
-| :--------- |------------:|-------:|-------------:|----------:|------------:|--------:|---------:|
-| Test       |         400 |     -  |            - |         - |           - |       - |        - |
+| Split      | # documents |     #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
+| :--------- |------------:|-----------:|-------------:|----------:|------------:|--------:|---------:|
+| Test       |         400 |      156.9 |        11.81 |     40.60 |        7.32 |   19.28 |    32.80 |
 
 The following data fields are available :
 

prmu.py

@@ -0,0 +1,103 @@
+# -*- coding: utf-8 -*-
+
+import sys
+import json
+import spacy
+
+from nltk.stem.snowball import SnowballStemmer as Stemmer
+
+nlp = spacy.load("fr_core_news_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())
+
+            print(doc['id'])
+
+            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('french').stem(w.lower()) for w in title_tokens]
+            abstract_stems = [Stemmer('french').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('french').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]
+
+            if doc['prmu'] != prmu:
+                print("PRMU categories are not identical!")
+
+            doc['prmu'] = prmu
+            data.append(json.dumps(doc))
+
+    # write the json
+    with open(sys.argv[2], 'w') as o:
+        o.write("\n".join(data))

stats.ipynb

@@ -0,0 +1,184 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "eba2ee81",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "No config specified, defaulting to: wikinews/raw\n",
+      "Reusing dataset wikinews (/Users/boudin-f/.cache/huggingface/datasets/taln-ls2n___wikinews/raw/1.0.0/aa15bd435a75a532fac6070fe8169812db6efd9d00c6fbac93992165536d8183)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "51588bf1a2714239b22d99eeac8f0db7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from datasets import load_dataset\n",
+    "\n",
+    "dataset = load_dataset('taln-ls2n/termith-eval')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4ba72244",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc2eac8de82a4851901c76d873c7546f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/399 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "# keyphrases: 11.81\n",
+      "% P: 40.60\n",
+      "% R: 7.32\n",
+      "% M: 19.28\n",
+      "% U: 32.80\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "P, R, M, U, nb_kps = [], [], [], [], []\n",
+    "    \n",
+    "for sample in tqdm(dataset['test']):\n",
+    "    nb_kps.append(len(sample[\"keyphrases\"]))\n",
+    "    P.append(sample[\"prmu\"].count(\"P\") / nb_kps[-1])\n",
+    "    R.append(sample[\"prmu\"].count(\"R\") / nb_kps[-1])\n",
+    "    M.append(sample[\"prmu\"].count(\"M\") / nb_kps[-1])\n",
+    "    U.append(sample[\"prmu\"].count(\"U\") / nb_kps[-1])\n",
+    "        \n",
+    "print(\"# keyphrases: {:.2f}\".format(sum(nb_kps)/len(nb_kps)))\n",
+    "print(\"% P: {:.2f}\".format(sum(P)/len(P)*100))\n",
+    "print(\"% R: {:.2f}\".format(sum(R)/len(R)*100))\n",
+    "print(\"% M: {:.2f}\".format(sum(M)/len(M)*100))\n",
+    "print(\"% U: {:.2f}\".format(sum(U)/len(U)*100))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "52dda817",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import spacy\n",
+    "\n",
+    "nlp = spacy.load(\"fr_core_news_sm\")\n",
+    "\n",
+    "# https://spacy.io/usage/linguistic-features#native-tokenizer-additions\n",
+    "\n",
+    "from spacy.lang.char_classes import ALPHA, ALPHA_LOWER, ALPHA_UPPER\n",
+    "from spacy.lang.char_classes import CONCAT_QUOTES, LIST_ELLIPSES, LIST_ICONS\n",
+    "from spacy.util import compile_infix_regex\n",
+    "\n",
+    "# Modify tokenizer infix patterns\n",
+    "infixes = (\n",
+    "    LIST_ELLIPSES\n",
+    "    + LIST_ICONS\n",
+    "    + [\n",
+    "        r\"(?<=[0-9])[+\\-\\*^](?=[0-9-])\",\n",
+    "        r\"(?<=[{al}{q}])\\.(?=[{au}{q}])\".format(\n",
+    "            al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES\n",
+    "        ),\n",
+    "        r\"(?<=[{a}]),(?=[{a}])\".format(a=ALPHA),\n",
+    "        # ✅ Commented out regex that splits on hyphens between letters:\n",
+    "        # r\"(?<=[{a}])(?:{h})(?=[{a}])\".format(a=ALPHA, h=HYPHENS),\n",
+    "        r\"(?<=[{a}0-9])[:<>=/](?=[{a}])\".format(a=ALPHA),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "infix_re = compile_infix_regex(infixes)\n",
+    "nlp.tokenizer.infix_finditer = infix_re.finditer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "047ab1cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7d2dc99496ef4579b3b027ca651ed359",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/399 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "avg doc len: 156.9\n"
+     ]
+    }
+   ],
+   "source": [
+    "doc_len = []\n",
+    "for sample in tqdm(dataset['test']):\n",
+    "    doc_len.append(len(nlp(sample[\"title\"])) + len(nlp(sample[\"abstract\"])))\n",
+    "        \n",
+    "print(\"avg doc len: {:.1f}\".format(sum(doc_len)/len(doc_len)))        "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}