diff --git a/tania_scripts/.ipynb_checkpoints/tania-some-other-metrics-checkpoint.ipynb b/tania_scripts/.ipynb_checkpoints/tania-some-other-metrics-checkpoint.ipynb
index 432660ba42e45d9089d7ec0866fa314d38ec94a8..fceab332c4fcac5387bfb477dd709e57b70d85da 100644
--- a/tania_scripts/.ipynb_checkpoints/tania-some-other-metrics-checkpoint.ipynb
+++ b/tania_scripts/.ipynb_checkpoints/tania-some-other-metrics-checkpoint.ipynb
@@ -10,30 +10,10 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 1,
"id": "510c3726-366d-4e26-a2bb-b55391b473bd",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[nltk_data] Downloading package punkt to\n",
- "[nltk_data] /home/tatiana.bladier/nltk_data...\n",
- "[nltk_data] Package punkt is already up-to-date!\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"import re\n",
"import nltk\n",
@@ -45,7 +25,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 2,
"id": "ee59c294-fdcd-429e-a126-734480d1b0ba",
"metadata": {},
"outputs": [],
@@ -71,7 +51,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 3,
"id": "b6ae41ef-116f-473d-b3f3-115d90fe65b7",
"metadata": {},
"outputs": [],
@@ -108,7 +88,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 5,
"id": "2a882cc9-8f9d-4457-becb-d2e26ab3f14f",
"metadata": {},
"outputs": [
@@ -116,18 +96,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Type/Token Ratio: 0.933\n"
+ "Type/Token Ratio: 0.923\n"
]
}
],
"source": [
- "ttr = compute_ttr(sample_text)\n",
+ "ttr = compute_ttr_cleaned(sample_text)\n",
"print(f\"Type/Token Ratio: {ttr:.3f}\")"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 6,
"id": "8897dcc3-4218-4ee5-9984-17b9a6d8dce2",
"metadata": {},
"outputs": [],
@@ -152,6 +132,7 @@
" verb_tokens = []\n",
" noun_tokens = []\n",
"\n",
+ "\n",
" for line in text.strip().splitlines():\n",
" if line.strip():\n",
" parts = line.split('\\t')\n",
@@ -172,15 +153,17 @@
" ttr_verb = len(set(verb_tokens)) / len(verb_tokens) if verb_tokens else 0.0\n",
" ttr_noun = len(set(noun_tokens)) / len(noun_tokens) if noun_tokens else 0.0\n",
"\n",
+ "\n",
+ "\n",
" return {\n",
" 'verb_ttr': ttr_verb,\n",
- " 'noun_ttr': ttr_noun\n",
+ " 'noun_ttr': ttr_noun, \n",
" }"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 7,
"id": "1363f307-fa4b-43ba-93d5-2d1c11ceb9e4",
"metadata": {},
"outputs": [
@@ -201,7 +184,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 8,
"id": "1362e192-514a-4a77-a8cb-5c012026e2bb",
"metadata": {},
"outputs": [],
@@ -218,9 +201,13 @@
" \"\"\"\n",
" verb_pos = {'v', 'vpp', 'vpr'}\n",
" noun_pos = {'nc', 'npp'}\n",
+ " adj_pos = {'adj'}\n",
+ " adv_pos = {'adv'}\n",
"\n",
" verb_count = 0\n",
" noun_count = 0\n",
+ " adj_count = 0\n",
+ " adv_count = 0\n",
"\n",
" for line in text.strip().splitlines():\n",
" if line.strip():\n",
@@ -229,21 +216,29 @@
" pos = parts[1]\n",
" if pos in verb_pos:\n",
" verb_count += 1\n",
- " elif pos in noun_pos:\n",
+ " if pos in noun_pos:\n",
" noun_count += 1\n",
+ " if pos in adj_pos:\n",
+ " adj_count += 1\n",
+ " if pos in adv_pos:\n",
+ " adv_count += 1\n",
"\n",
" nominal_verb_ratio = noun_count / verb_count if verb_count else float('inf')\n",
" verb_nominal_ratio = verb_count / noun_count if noun_count else float('inf')\n",
+ " adv_verb_ratio = adv_count / verb_count if verb_count else float('inf')\n",
+ " adj_noun_verb_ratio = (adj_count + noun_count) / verb_count if verb_count else float('inf')\n",
"\n",
" return {\n",
" 'nominal_verb_ratio': nominal_verb_ratio,\n",
- " 'verb_nominal_ratio': verb_nominal_ratio\n",
+ " 'verb_nominal_ratio': verb_nominal_ratio, \n",
+ " 'adv_verb_ratio': adv_verb_ratio,\n",
+ " 'adj_noun_verb_ratio': adj_noun_verb_ratio\n",
" }"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 9,
"id": "544ff6aa-4104-4580-a01f-97429ffcc228",
"metadata": {},
"outputs": [
@@ -252,14 +247,18 @@
"output_type": "stream",
"text": [
"Nominal/Verb Ratio: 1.00\n",
- "Verb/Nominal Ratio: 1.00\n"
+ "Verb/Nominal Ratio: 1.00\n",
+ "Adverb/Verb Ratio: 0.00\n",
+ "Adj+Noun/Verb Ratio: 1.00\n"
]
}
],
"source": [
"ratios = compute_nv_ratios(sample_text)\n",
"print(f\"Nominal/Verb Ratio: {ratios['nominal_verb_ratio']:.2f}\")\n",
- "print(f\"Verb/Nominal Ratio: {ratios['verb_nominal_ratio']:.2f}\")"
+ "print(f\"Verb/Nominal Ratio: {ratios['verb_nominal_ratio']:.2f}\")\n",
+ "print(f\"Adverb/Verb Ratio: {ratios['adv_verb_ratio']:.2f}\")\n",
+ "print(f\"Adj+Noun/Verb Ratio: {ratios['adj_noun_verb_ratio']:.2f}\")"
]
},
{
@@ -329,7 +328,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 10,
"id": "b9052dc2-ce45-4af4-a0a0-46c60a13da12",
"metadata": {},
"outputs": [],
@@ -389,7 +388,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 11,
"id": "1e9dd0fb-db6a-47d1-8bfb-1015845f6d3e",
"metadata": {},
"outputs": [
@@ -399,7 +398,7 @@
"{'Flesch-Douma': 88.68, 'LIX': 11.55, 'Kandel-Moles': 5.86}"
]
},
- "execution_count": 25,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -412,10 +411,372 @@
"compute_french_readability(sample_french_text)"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "8a0c0fff-d605-4349-a698-a11fd404e2e8",
+ "metadata": {},
+ "source": [
+ "## Calculate avg scores"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "24bc84a5-b2df-4194-838a-8f24302599bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the function to compute average word length and sentence length\n",
+ "def compute_avg_lengths(sample_text):\n",
+ " sentences = []\n",
+ " current_sentence = []\n",
+ " \n",
+ " for line in sample_text.strip().split('\\n'):\n",
+ " cols = line.strip().split('\\t')\n",
+ " if not cols or len(cols) < 1:\n",
+ " continue\n",
+ " token = cols[0]\n",
+ "\n",
+ " if token == '<s>':\n",
+ " current_sentence = []\n",
+ " elif token == '</s>':\n",
+ " if current_sentence:\n",
+ " sentences.append(current_sentence)\n",
+ " else:\n",
+ " current_sentence.append(token)\n",
+ "\n",
+ " total_words = 0\n",
+ " total_word_length = 0\n",
+ " sentence_lengths = []\n",
+ "\n",
+ " for sentence in sentences:\n",
+ " words = [w for w in sentence if re.match(r'\\w+', w) and w not in ['<s>', '</s>']]\n",
+ " sentence_lengths.append(len(words))\n",
+ " total_words += len(words)\n",
+ " total_word_length += sum(len(w) for w in words)\n",
+ "\n",
+ " avg_word_length = total_word_length / total_words if total_words else 0\n",
+ " avg_sentence_length = sum(sentence_lengths) / len(sentence_lengths) if sentence_lengths else 0\n",
+ "\n",
+ " return {\n",
+ " \"Average Word Length\": round(avg_word_length, 2),\n",
+ " \"Average Sentence Length\": round(avg_sentence_length, 2)\n",
+ " }\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "0cdb972f-31b6-4e7e-82a8-371eda344f2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Average Word Length': 3.79, 'Average Sentence Length': 7.0}"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Sample text from the user\n",
+ "sample_text = \"\"\"\n",
+ "<s>\t<s>\t<s>\t0\n",
+ "Aramis\tnpp\t<nul>@@<nul>\t0\n",
+ "était\tv\t<nul>@@<nul>\t0\n",
+ "à\tp\t<nul>@@<nul>\t0\n",
+ "son\tdet\tNP@@<nul>\t0\n",
+ "poste\tnc\t<nul>@@<nul>\t1\n",
+ ",\tponct\t<nul>@@<nul>\t0\n",
+ "il\tcls-suj\tVN@@<nul>\t0\n",
+ "était\tv\t<nul>@@<nul>\t1\n",
+ "tombé\tvpp\t<nul>@@<nul>\t1\n",
+ "de\tp\tPP-DE_OBJ@@Sint-MOD\t1\n",
+ "ses\tdet\tNP@@<nul>\t2\n",
+ "bras\tnc\t<nul>@@<nul>\t3\n",
+ ".\tponct\t<nul>@@<nul>\t0\n",
+ "</s>\t</s>\t</s>\t0\n",
+ "<s>\t<s>\t<s>\t0\n",
+ "Aramis\tnpp\t<nul>@@<nul>\t0\n",
+ "était\tv\t<nul>@@<nul>\t0\n",
+ "à\tp\t<nul>@@<nul>\t0\n",
+ "</s>\t</s>\t</s>\t0\n",
+ "\"\"\"\n",
+ "\n",
+ "# Compute and display the results\n",
+ "compute_avg_lengths(sample_text)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf5b0b52-e5c4-4b40-b925-495f4dd8e3be",
+ "metadata": {},
+ "source": [
+ "## Calculate POS frequencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "56af520c-d56b-404a-aebf-ad7c2a9ca503",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_pos_frequency(column_text):\n",
+ " verb_tags = {\"v\", \"vpp\", \"vpr\"}\n",
+ " noun_tags = {'nc', 'npp'}\n",
+ " adj_tags = {'adj'}\n",
+ " adv_tags = {'adv'}\n",
+ "\n",
+ " total_tokens = 0\n",
+ " verb_count = 0\n",
+ " noun_count = 0\n",
+ " adj_count = 0\n",
+ " adv_count = 0\n",
+ "\n",
+ " for line in column_text.strip().split('\\n'):\n",
+ " parts = line.strip().split('\\t')\n",
+ " if len(parts) < 2:\n",
+ " continue\n",
+ " token, pos = parts[0], parts[1]\n",
+ " if re.match(r'\\w+', token): # ignore punctuation\n",
+ " total_tokens += 1\n",
+ " if pos in verb_tags:\n",
+ " verb_count += 1\n",
+ " if pos in noun_tags:\n",
+ " noun_count += 1\n",
+ " if pos in adj_tags:\n",
+ " adj_count += 1\n",
+ " if pos in adv_tags:\n",
+ " adv_count += 1\n",
+ "\n",
+ " if total_tokens == 0:\n",
+ " return 0.0\n",
+ "\n",
+ " return {\n",
+ " 'verb_freq': round(verb_count / total_tokens, 4),\n",
+ " 'noun_freq': round(noun_count / total_tokens, 4), \n",
+ " 'adv_freq': round(adv_count / total_tokens, 4),\n",
+ " 'adj_freq': round(adj_count / total_tokens, 4),\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "f7c8b125-4651-4b21-bcc4-93ef78a4239b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Verb Frequency: 0.29\n",
+ "Noun Frequency: 0.29\n",
+ "Adj Frequency: 0.00\n",
+ "Adv Frequency: 0.00\n"
+ ]
+ }
+ ],
+ "source": [
+ "freqs = compute_pos_frequency(sample_text)\n",
+ "\n",
+ "print(f\"Verb Frequency: {freqs['verb_freq']:.2f}\")\n",
+ "print(f\"Noun Frequency: {freqs['noun_freq']:.2f}\")\n",
+ "print(f\"Adj Frequency: {freqs['adv_freq']:.2f}\")\n",
+ "print(f\"Adv Frequency: {freqs['adj_freq']:.2f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4cd15f8f-5618-4586-bd43-30f4919c7274",
+ "metadata": {},
+ "source": [
+ "### MSTTR-100 (Mean Segmental Type-Token Ratio)\n",
+ "\n",
+ "MSTTR-100 measures lexical diversity by dividing the text into consecutive segments of 100 tokens and computing the type-token ratio (TTR) for each segment. The final MSTTR-100 is the average TTR across all segments."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "daa17c33-adca-4695-90eb-741579382939",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "\n",
+ "def msttr(text, segment_size):\n",
+ " words = re.findall(r'\\b\\w+\\b', text.lower())\n",
+ " if len(words) < segment_size:\n",
+ " return len(set(words)) / len(words)\n",
+ "\n",
+ " segments = [words[i:i+segment_size] for i in range(0, len(words), segment_size)]\n",
+ " ttrs = [len(set(segment)) / len(segment) for segment in segments if len(segment) == segment_size]\n",
+ " return sum(ttrs) / len(ttrs)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "80d8fa08-6b7d-4ab7-85cd-987823639277",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MSTTR: 0.8823529411764706\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"MSTTR: \", msttr(sample_french_text, 100))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "91c7969a-3fff-4935-9f26-7e1ebb6b64c6",
+ "metadata": {},
+ "source": [
+ "### BZIP TXT\n",
+ "\n",
+ "\"BZIP TXT\" refers to the compression ratio achieved by compressing the text using the BZIP2 algorithm. It serves as a proxy for the text's redundancy and complexity.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "c8bd9186-eab8-4ca6-93bd-82b260cd3d19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import bz2\n",
+ "\n",
+ "def bzip_compression_ratio(text):\n",
+ " original_size = len(text.encode('utf-8'))\n",
+ " compressed_size = len(bz2.compress(text.encode('utf-8')))\n",
+ " return compressed_size / original_size"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "3f9c7dc7-6820-4013-a85c-2af4f846d4f5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "BZIP: 1.1931818181818181\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"BZIP: \", bzip_compression_ratio(sample_french_text))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "88b6f5f8-90b7-4dfe-b8ee-d54380bf3194",
+ "metadata": {},
+ "source": [
+ "### Word Entropy\n",
+ "\n",
+ "Word entropy quantifies the unpredictability or information content of words in a text. It's calculated using Shannon's entropy formula over the distribution of word frequencies."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "65e1a630-c46e-4b18-9831-b97864de53ee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "import math\n",
+ "from collections import Counter\n",
+ "\n",
+ "def word_entropy(text):\n",
+ " words = re.findall(r'\\b\\w+\\b', text.lower())\n",
+ " total_words = len(words)\n",
+ " word_counts = Counter(words)\n",
+ " return -sum((count/total_words) * math.log2(count/total_words) for count in word_counts.values())\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "1612e911-12a8-47c9-b811-b2d6885c3647",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WORD ENTROPY: 3.807763576417195\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"WORD ENTROPY: \", word_entropy(sample_french_text))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a58d962f-5d90-4ee9-b347-64f5bb52c24a",
+ "metadata": {},
+ "source": [
+ "\n",
+ "### Bigram Entropy\n",
+ "\n",
+ "Bigram entropy measures the unpredictability of word pairs (bigrams) in a text, providing insight into the text's syntactic complexity."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "925a3a75-aaaa-4851-b77b-b42cb1e21e11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def bigram_entropy(text):\n",
+ " words = re.findall(r'\\b\\w+\\b', text.lower())\n",
+ " bigrams = list(zip(words, words[1:]))\n",
+ " total_bigrams = len(bigrams)\n",
+ " bigram_counts = Counter(bigrams)\n",
+ " return -sum((count/total_bigrams) * math.log2(count/total_bigrams) for count in bigram_counts.values())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "6fa60897-ad26-43b4-b8de-861290ca6bd3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "BIGRAM ENTROPY: 4.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"BIGRAM ENTROPY: \", bigram_entropy(sample_french_text))"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "b2cd53c6-1c16-4eaf-8ac8-af166afaa97b",
+ "id": "d2afe949-9351-4ec8-aefc-7fe79b7c5a88",
"metadata": {},
"outputs": [],
"source": []
diff --git a/tania_scripts/tania-some-other-metrics.ipynb b/tania_scripts/tania-some-other-metrics.ipynb
index 432660ba42e45d9089d7ec0866fa314d38ec94a8..fceab332c4fcac5387bfb477dd709e57b70d85da 100644
--- a/tania_scripts/tania-some-other-metrics.ipynb
+++ b/tania_scripts/tania-some-other-metrics.ipynb
@@ -10,30 +10,10 @@
},
{
"cell_type": "code",
- "execution_count": 12,
+ "execution_count": 1,
"id": "510c3726-366d-4e26-a2bb-b55391b473bd",
"metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "[nltk_data] Downloading package punkt to\n",
- "[nltk_data] /home/tatiana.bladier/nltk_data...\n",
- "[nltk_data] Package punkt is already up-to-date!\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "True"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
+ "outputs": [],
"source": [
"import re\n",
"import nltk\n",
@@ -45,7 +25,7 @@
},
{
"cell_type": "code",
- "execution_count": 13,
+ "execution_count": 2,
"id": "ee59c294-fdcd-429e-a126-734480d1b0ba",
"metadata": {},
"outputs": [],
@@ -71,7 +51,7 @@
},
{
"cell_type": "code",
- "execution_count": 14,
+ "execution_count": 3,
"id": "b6ae41ef-116f-473d-b3f3-115d90fe65b7",
"metadata": {},
"outputs": [],
@@ -108,7 +88,7 @@
},
{
"cell_type": "code",
- "execution_count": 15,
+ "execution_count": 5,
"id": "2a882cc9-8f9d-4457-becb-d2e26ab3f14f",
"metadata": {},
"outputs": [
@@ -116,18 +96,18 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "Type/Token Ratio: 0.933\n"
+ "Type/Token Ratio: 0.923\n"
]
}
],
"source": [
- "ttr = compute_ttr(sample_text)\n",
+ "ttr = compute_ttr_cleaned(sample_text)\n",
"print(f\"Type/Token Ratio: {ttr:.3f}\")"
]
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 6,
"id": "8897dcc3-4218-4ee5-9984-17b9a6d8dce2",
"metadata": {},
"outputs": [],
@@ -152,6 +132,7 @@
" verb_tokens = []\n",
" noun_tokens = []\n",
"\n",
+ "\n",
" for line in text.strip().splitlines():\n",
" if line.strip():\n",
" parts = line.split('\\t')\n",
@@ -172,15 +153,17 @@
" ttr_verb = len(set(verb_tokens)) / len(verb_tokens) if verb_tokens else 0.0\n",
" ttr_noun = len(set(noun_tokens)) / len(noun_tokens) if noun_tokens else 0.0\n",
"\n",
+ "\n",
+ "\n",
" return {\n",
" 'verb_ttr': ttr_verb,\n",
- " 'noun_ttr': ttr_noun\n",
+ " 'noun_ttr': ttr_noun, \n",
" }"
]
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 7,
"id": "1363f307-fa4b-43ba-93d5-2d1c11ceb9e4",
"metadata": {},
"outputs": [
@@ -201,7 +184,7 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 8,
"id": "1362e192-514a-4a77-a8cb-5c012026e2bb",
"metadata": {},
"outputs": [],
@@ -218,9 +201,13 @@
" \"\"\"\n",
" verb_pos = {'v', 'vpp', 'vpr'}\n",
" noun_pos = {'nc', 'npp'}\n",
+ " adj_pos = {'adj'}\n",
+ " adv_pos = {'adv'}\n",
"\n",
" verb_count = 0\n",
" noun_count = 0\n",
+ " adj_count = 0\n",
+ " adv_count = 0\n",
"\n",
" for line in text.strip().splitlines():\n",
" if line.strip():\n",
@@ -229,21 +216,29 @@
" pos = parts[1]\n",
" if pos in verb_pos:\n",
" verb_count += 1\n",
- " elif pos in noun_pos:\n",
+ " if pos in noun_pos:\n",
" noun_count += 1\n",
+ " if pos in adj_pos:\n",
+ " adj_count += 1\n",
+ " if pos in adv_pos:\n",
+ " adv_count += 1\n",
"\n",
" nominal_verb_ratio = noun_count / verb_count if verb_count else float('inf')\n",
" verb_nominal_ratio = verb_count / noun_count if noun_count else float('inf')\n",
+ " adv_verb_ratio = adv_count / verb_count if verb_count else float('inf')\n",
+ " adj_noun_verb_ratio = (adj_count + noun_count) / verb_count if verb_count else float('inf')\n",
"\n",
" return {\n",
" 'nominal_verb_ratio': nominal_verb_ratio,\n",
- " 'verb_nominal_ratio': verb_nominal_ratio\n",
+ " 'verb_nominal_ratio': verb_nominal_ratio, \n",
+ " 'adv_verb_ratio': adv_verb_ratio,\n",
+ " 'adj_noun_verb_ratio': adj_noun_verb_ratio\n",
" }"
]
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 9,
"id": "544ff6aa-4104-4580-a01f-97429ffcc228",
"metadata": {},
"outputs": [
@@ -252,14 +247,18 @@
"output_type": "stream",
"text": [
"Nominal/Verb Ratio: 1.00\n",
- "Verb/Nominal Ratio: 1.00\n"
+ "Verb/Nominal Ratio: 1.00\n",
+ "Adverb/Verb Ratio: 0.00\n",
+ "Adj+Noun/Verb Ratio: 1.00\n"
]
}
],
"source": [
"ratios = compute_nv_ratios(sample_text)\n",
"print(f\"Nominal/Verb Ratio: {ratios['nominal_verb_ratio']:.2f}\")\n",
- "print(f\"Verb/Nominal Ratio: {ratios['verb_nominal_ratio']:.2f}\")"
+ "print(f\"Verb/Nominal Ratio: {ratios['verb_nominal_ratio']:.2f}\")\n",
+ "print(f\"Adverb/Verb Ratio: {ratios['adv_verb_ratio']:.2f}\")\n",
+ "print(f\"Adj+Noun/Verb Ratio: {ratios['adj_noun_verb_ratio']:.2f}\")"
]
},
{
@@ -329,7 +328,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 10,
"id": "b9052dc2-ce45-4af4-a0a0-46c60a13da12",
"metadata": {},
"outputs": [],
@@ -389,7 +388,7 @@
},
{
"cell_type": "code",
- "execution_count": 25,
+ "execution_count": 11,
"id": "1e9dd0fb-db6a-47d1-8bfb-1015845f6d3e",
"metadata": {},
"outputs": [
@@ -399,7 +398,7 @@
"{'Flesch-Douma': 88.68, 'LIX': 11.55, 'Kandel-Moles': 5.86}"
]
},
- "execution_count": 25,
+ "execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
@@ -412,10 +411,372 @@
"compute_french_readability(sample_french_text)"
]
},
+ {
+ "cell_type": "markdown",
+ "id": "8a0c0fff-d605-4349-a698-a11fd404e2e8",
+ "metadata": {},
+ "source": [
+ "## Calculate avg scores"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "24bc84a5-b2df-4194-838a-8f24302599bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Define the function to compute average word length and sentence length\n",
+ "def compute_avg_lengths(sample_text):\n",
+ " sentences = []\n",
+ " current_sentence = []\n",
+ " \n",
+ " for line in sample_text.strip().split('\\n'):\n",
+ " cols = line.strip().split('\\t')\n",
+ " if not cols or len(cols) < 1:\n",
+ " continue\n",
+ " token = cols[0]\n",
+ "\n",
+ " if token == '<s>':\n",
+ " current_sentence = []\n",
+ " elif token == '</s>':\n",
+ " if current_sentence:\n",
+ " sentences.append(current_sentence)\n",
+ " else:\n",
+ " current_sentence.append(token)\n",
+ "\n",
+ " total_words = 0\n",
+ " total_word_length = 0\n",
+ " sentence_lengths = []\n",
+ "\n",
+ " for sentence in sentences:\n",
+ " words = [w for w in sentence if re.match(r'\\w+', w) and w not in ['<s>', '</s>']]\n",
+ " sentence_lengths.append(len(words))\n",
+ " total_words += len(words)\n",
+ " total_word_length += sum(len(w) for w in words)\n",
+ "\n",
+ " avg_word_length = total_word_length / total_words if total_words else 0\n",
+ " avg_sentence_length = sum(sentence_lengths) / len(sentence_lengths) if sentence_lengths else 0\n",
+ "\n",
+ " return {\n",
+ " \"Average Word Length\": round(avg_word_length, 2),\n",
+ " \"Average Sentence Length\": round(avg_sentence_length, 2)\n",
+ " }\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "0cdb972f-31b6-4e7e-82a8-371eda344f2c",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'Average Word Length': 3.79, 'Average Sentence Length': 7.0}"
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Sample text from the user\n",
+ "sample_text = \"\"\"\n",
+ "<s>\t<s>\t<s>\t0\n",
+ "Aramis\tnpp\t<nul>@@<nul>\t0\n",
+ "était\tv\t<nul>@@<nul>\t0\n",
+ "à\tp\t<nul>@@<nul>\t0\n",
+ "son\tdet\tNP@@<nul>\t0\n",
+ "poste\tnc\t<nul>@@<nul>\t1\n",
+ ",\tponct\t<nul>@@<nul>\t0\n",
+ "il\tcls-suj\tVN@@<nul>\t0\n",
+ "était\tv\t<nul>@@<nul>\t1\n",
+ "tombé\tvpp\t<nul>@@<nul>\t1\n",
+ "de\tp\tPP-DE_OBJ@@Sint-MOD\t1\n",
+ "ses\tdet\tNP@@<nul>\t2\n",
+ "bras\tnc\t<nul>@@<nul>\t3\n",
+ ".\tponct\t<nul>@@<nul>\t0\n",
+ "</s>\t</s>\t</s>\t0\n",
+ "<s>\t<s>\t<s>\t0\n",
+ "Aramis\tnpp\t<nul>@@<nul>\t0\n",
+ "était\tv\t<nul>@@<nul>\t0\n",
+ "à\tp\t<nul>@@<nul>\t0\n",
+ "</s>\t</s>\t</s>\t0\n",
+ "\"\"\"\n",
+ "\n",
+ "# Compute and display the results\n",
+ "compute_avg_lengths(sample_text)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf5b0b52-e5c4-4b40-b925-495f4dd8e3be",
+ "metadata": {},
+ "source": [
+ "## Calculate POS frequencies"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "56af520c-d56b-404a-aebf-ad7c2a9ca503",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def compute_pos_frequency(column_text):\n",
+ " verb_tags = {\"v\", \"vpp\", \"vpr\"}\n",
+ " noun_tags = {'nc', 'npp'}\n",
+ " adj_tags = {'adj'}\n",
+ " adv_tags = {'adv'}\n",
+ "\n",
+ " total_tokens = 0\n",
+ " verb_count = 0\n",
+ " noun_count = 0\n",
+ " adj_count = 0\n",
+ " adv_count = 0\n",
+ "\n",
+ " for line in column_text.strip().split('\\n'):\n",
+ " parts = line.strip().split('\\t')\n",
+ " if len(parts) < 2:\n",
+ " continue\n",
+ " token, pos = parts[0], parts[1]\n",
+ " if re.match(r'\\w+', token): # ignore punctuation\n",
+ " total_tokens += 1\n",
+ " if pos in verb_tags:\n",
+ " verb_count += 1\n",
+ " if pos in noun_tags:\n",
+ " noun_count += 1\n",
+ " if pos in adj_tags:\n",
+ " adj_count += 1\n",
+ " if pos in adv_tags:\n",
+ " adv_count += 1\n",
+ "\n",
+ " if total_tokens == 0:\n",
+ " return 0.0\n",
+ "\n",
+ " return {\n",
+ " 'verb_freq': round(verb_count / total_tokens, 4),\n",
+ " 'noun_freq': round(noun_count / total_tokens, 4), \n",
+ " 'adv_freq': round(adv_count / total_tokens, 4),\n",
+ " 'adj_freq': round(adj_count / total_tokens, 4),\n",
+ " }"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "f7c8b125-4651-4b21-bcc4-93ef78a4239b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Verb Frequency: 0.29\n",
+ "Noun Frequency: 0.29\n",
+ "Adj Frequency: 0.00\n",
+ "Adv Frequency: 0.00\n"
+ ]
+ }
+ ],
+ "source": [
+ "freqs = compute_pos_frequency(sample_text)\n",
+ "\n",
+ "print(f\"Verb Frequency: {freqs['verb_freq']:.2f}\")\n",
+ "print(f\"Noun Frequency: {freqs['noun_freq']:.2f}\")\n",
+ "print(f\"Adj Frequency: {freqs['adv_freq']:.2f}\")\n",
+ "print(f\"Adv Frequency: {freqs['adj_freq']:.2f}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4cd15f8f-5618-4586-bd43-30f4919c7274",
+ "metadata": {},
+ "source": [
+ "### MSTTR-100 (Mean Segmental Type-Token Ratio)\n",
+ "\n",
+ "MSTTR-100 measures lexical diversity by dividing the text into consecutive segments of 100 tokens and computing the type-token ratio (TTR) for each segment. The final MSTTR-100 is the average TTR across all segments."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "daa17c33-adca-4695-90eb-741579382939",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "\n",
+ "def msttr(text, segment_size):\n",
+ " words = re.findall(r'\\b\\w+\\b', text.lower())\n",
+ " if len(words) < segment_size:\n",
+ " return len(set(words)) / len(words)\n",
+ "\n",
+ " segments = [words[i:i+segment_size] for i in range(0, len(words), segment_size)]\n",
+ " ttrs = [len(set(segment)) / len(segment) for segment in segments if len(segment) == segment_size]\n",
+ " return sum(ttrs) / len(ttrs)\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "80d8fa08-6b7d-4ab7-85cd-987823639277",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "MSTTR: 0.8823529411764706\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"MSTTR: \", msttr(sample_french_text, 100))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "91c7969a-3fff-4935-9f26-7e1ebb6b64c6",
+ "metadata": {},
+ "source": [
+ "### BZIP TXT\n",
+ "\n",
+ "\"BZIP TXT\" refers to the compression ratio achieved by compressing the text using the BZIP2 algorithm. It serves as a proxy for the text's redundancy and complexity.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "c8bd9186-eab8-4ca6-93bd-82b260cd3d19",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import bz2\n",
+ "\n",
+ "def bzip_compression_ratio(text):\n",
+ " original_size = len(text.encode('utf-8'))\n",
+ " compressed_size = len(bz2.compress(text.encode('utf-8')))\n",
+ " return compressed_size / original_size"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "3f9c7dc7-6820-4013-a85c-2af4f846d4f5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "BZIP: 1.1931818181818181\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"BZIP: \", bzip_compression_ratio(sample_french_text))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "88b6f5f8-90b7-4dfe-b8ee-d54380bf3194",
+ "metadata": {},
+ "source": [
+ "### Word Entropy\n",
+ "\n",
+ "Word entropy quantifies the unpredictability or information content of words in a text. It's calculated using Shannon's entropy formula over the distribution of word frequencies."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "65e1a630-c46e-4b18-9831-b97864de53ee",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "\n",
+ "import math\n",
+ "from collections import Counter\n",
+ "\n",
+ "def word_entropy(text):\n",
+ " words = re.findall(r'\\b\\w+\\b', text.lower())\n",
+ " total_words = len(words)\n",
+ " word_counts = Counter(words)\n",
+ " return -sum((count/total_words) * math.log2(count/total_words) for count in word_counts.values())\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "1612e911-12a8-47c9-b811-b2d6885c3647",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WORD ENTROPY: 3.807763576417195\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"WORD ENTROPY: \", word_entropy(sample_french_text))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a58d962f-5d90-4ee9-b347-64f5bb52c24a",
+ "metadata": {},
+ "source": [
+ "\n",
+ "### Bigram Entropy\n",
+ "\n",
+ "Bigram entropy measures the unpredictability of word pairs (bigrams) in a text, providing insight into the text's syntactic complexity."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "925a3a75-aaaa-4851-b77b-b42cb1e21e11",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def bigram_entropy(text):\n",
+ " words = re.findall(r'\\b\\w+\\b', text.lower())\n",
+ " bigrams = list(zip(words, words[1:]))\n",
+ " total_bigrams = len(bigrams)\n",
+ " bigram_counts = Counter(bigrams)\n",
+ " return -sum((count/total_bigrams) * math.log2(count/total_bigrams) for count in bigram_counts.values())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "6fa60897-ad26-43b4-b8de-861290ca6bd3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "BIGRAM ENTROPY: 4.0\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"BIGRAM ENTROPY: \", bigram_entropy(sample_french_text))"
+ ]
+ },
{
"cell_type": "code",
"execution_count": null,
- "id": "b2cd53c6-1c16-4eaf-8ac8-af166afaa97b",
+ "id": "d2afe949-9351-4ec8-aefc-7fe79b7c5a88",
"metadata": {},
"outputs": [],
"source": []