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113
lab/lab_01.ipynb
113
lab/lab_01.ipynb
@ -213,13 +213,34 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 13,
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"id": "protected-rings",
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"metadata": {},
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"outputs": [],
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"source": [
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"def tm_lookup(sentence):\n",
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" return ''"
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" return [entry[1].casefold() for entry in translation_memory if entry[0] == sentence]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "99d75100-0f9d-4586-82ef-ab42180472a2",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['press the enter button', 'press the enter key']"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tm_lookup('Wciśnij przycisk Enter')"
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]
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},
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{
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@ -232,17 +253,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"execution_count": 15,
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"id": "severe-alloy",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"''"
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"[]"
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]
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},
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"execution_count": 18,
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"execution_count": 15,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -261,13 +282,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 16,
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"id": "structural-diesel",
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"metadata": {},
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"outputs": [],
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"source": [
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"import string \n",
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"\n",
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"def tm_lookup(sentence):\n",
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" return ''"
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" return [entry[1].casefold() for entry in translation_memory if entry[0] == sentence]\n",
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" translator = str.maketrans('', '', string.punctuation) \n",
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" return sentence.translate(translator)"
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]
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},
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{
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@ -280,17 +305,17 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 20,
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"id": "brief-senegal",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"''"
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"[]"
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]
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},
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"execution_count": 12,
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"execution_count": 20,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -317,15 +342,41 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 24,
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"id": "mathematical-customs",
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"metadata": {},
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"outputs": [],
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"source": [
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"def tm_lookup(sentence):\n",
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" translator = str.maketrans('', '', string.punctuation)\n",
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" sentence = sentence.translate(translator).casefold()\n",
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" for entry in translation_memory:\n",
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" if any(word in entry[0].casefold() for word in sentence.split()):\n",
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" return entry[1]\n",
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" return ''"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "f6537825-62a6-4503-91a5-bbb17d84170b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'System restart required'"
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tm_lookup('Wymagane ponowne uruchomienie maszyny')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "meaningful-virus",
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@ -344,7 +395,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 26,
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"id": "humanitarian-wrong",
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"metadata": {},
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"outputs": [],
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@ -362,7 +413,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": 27,
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"id": "located-perception",
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"metadata": {},
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"outputs": [],
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@ -374,7 +425,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 28,
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"id": "advised-casting",
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"metadata": {},
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"outputs": [
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@ -384,7 +435,7 @@
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"[('przycisk', 'button'), ('drukarka', 'printer')]"
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]
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},
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"execution_count": 17,
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -419,13 +470,35 @@
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"execution_count": 37,
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"id": "original-tunisia",
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"metadata": {},
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"outputs": [],
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"source": [
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"def glossary_lookup(sentence):\n",
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" return ''"
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" sentence_words = sentence.casefold().split()\n",
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" return [entry for entry in glossary if entry[0] in sentence_words]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"id": "b3ae5504-4168-4fe0-ad25-60558242a31d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('przycisk', 'button'), ('drukarka', 'printer')]"
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]
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},
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"execution_count": 39,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"glossary_lookup('Każda Drukarka posiada przycisk wznowienia drukowania')"
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]
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},
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{
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@ -438,7 +511,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"execution_count": 38,
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"id": "adolescent-semiconductor",
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"metadata": {},
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"outputs": [],
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@ -452,7 +525,7 @@
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"author": "Rafał Jaworski",
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"email": "rjawor@amu.edu.pl",
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@ -467,7 +540,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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"version": "3.10.12"
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},
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"subtitle": "1. Podstawowe techniki wspomagania tłumaczenia",
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"title": "Komputerowe wspomaganie tłumaczenia",
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112
lab/lab_02.ipynb
112
lab/lab_02.ipynb
@ -57,7 +57,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 31,
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"id": "confident-prison",
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"metadata": {},
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"outputs": [],
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@ -80,13 +80,49 @@
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 29,
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"id": "continental-submission",
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"metadata": {},
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"outputs": [],
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"source": [
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"def ice_lookup(sentence, prev_sentence, next_sentence):\n",
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" return []"
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"def ice_lookup(sentence, prev_sentence, next_sentence, translation_memory):\n",
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" \n",
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" ice_previous = \"\"\n",
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" ice_next = \"\"\n",
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" \n",
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" for original, translation in translation_memory:\n",
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" if sentence == original:\n",
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" index = translation_memory.index((original, translation))\n",
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" if index > 0:\n",
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" ice_previous = translation_memory[index - 1][1]\n",
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" if index < len(translation_memory) - 1:\n",
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" ice_next = translation_memory[index + 1][1]\n",
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" break\n",
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" \n",
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" return (ice_previous, ice_next)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "f125ddd2-89fc-4496-93d9-9d640b7f616e",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"prev: Press the ENTER button , next: The printer is switched off\n"
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]
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}
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],
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"source": [
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"sentence = \"Sprawdź ustawienia sieciowe\"\n",
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"prev_sentence = \"Wciśnij przycisk Enter\"\n",
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"next_sentence = \"Wymagane ponowne uruchomienie komputera\"\n",
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"\n",
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"ice_result = ice_lookup(sentence, prev_sentence, next_sentence, translation_memory)\n",
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"print('prev: ', ice_result[0], ', next: ', ice_result[1])"
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]
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},
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{
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@ -119,7 +155,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 36,
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"id": "fourth-pillow",
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"metadata": {},
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"outputs": [],
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@ -141,7 +177,7 @@
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"id": "graduate-theorem",
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"metadata": {},
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"source": [
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"Odpowiedź:"
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"Odpowiedź: Funkcja nie jest dobrą funkcją dystansu, gdyż bierze pod uwagaę jedynie różnice w długości zdań."
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]
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},
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{
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@ -154,7 +190,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": 56,
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"id": "continued-christopher",
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"metadata": {},
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"outputs": [],
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@ -179,7 +215,7 @@
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"id": "metallic-leave",
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"metadata": {},
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"source": [
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"Odpowiedź:"
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"Odpowiedź: Nie jest to dobra funkcja dystansu, gdyż znajduje jedynie fakt, że zdania się mogą między sobą różnić."
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]
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},
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{
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@ -206,7 +242,7 @@
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"id": "bibliographic-stopping",
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"metadata": {},
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"source": [
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"Odpowiedź:"
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"Odpowiedź: Dystans Lavenshteina jest poprawną funkcją dystansu, opisuje ilość operacji, które należy wykonać, aby porównywane do siebie zdania były takie same (np. zamiana liter, wstawienie innej litery, usunięcie litery, itp)"
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]
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},
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{
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@ -223,7 +259,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 62,
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"id": "secondary-wrist",
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"metadata": {},
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"outputs": [
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@ -233,7 +269,7 @@
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"2"
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]
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},
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"execution_count": 5,
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"execution_count": 62,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -254,7 +290,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 63,
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"id": "associate-tuner",
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"metadata": {},
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"outputs": [],
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@ -273,7 +309,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 64,
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"id": "focal-pathology",
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"metadata": {},
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"outputs": [
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@ -283,7 +319,7 @@
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"0.9166666666666666"
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]
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},
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"execution_count": 7,
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"execution_count": 64,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -294,7 +330,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 65,
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"id": "roman-ceiling",
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"metadata": {},
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"outputs": [
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@ -304,7 +340,7 @@
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"0.9428571428571428"
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]
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},
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"execution_count": 8,
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"execution_count": 65,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -315,7 +351,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 66,
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"id": "invisible-cambodia",
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"metadata": {},
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"outputs": [
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@ -325,7 +361,7 @@
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"0.631578947368421"
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]
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},
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"execution_count": 9,
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"execution_count": 66,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -344,13 +380,43 @@
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 71,
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"id": "genetic-cradle",
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"metadata": {},
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"outputs": [],
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"source": [
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"def fuzzy_lookup(sentence, threshold):\n",
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" return []"
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"import difflib\n",
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"\n",
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"def fuzzy_lookup(sentence, threshold, translation_memory):\n",
|
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" fuzzy_matches = []\n",
|
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" for original, translation in translation_memory:\n",
|
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" similarity = difflib.SequenceMatcher(None, sentence, original).ratio()\n",
|
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" if similarity >= threshold:\n",
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" fuzzy_matches.append((original, translation, similarity))\n",
|
||||
" return fuzzy_matches"
|
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]
|
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},
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{
|
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"cell_type": "code",
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"execution_count": 80,
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"id": "6bebcb12-8c73-4beb-b4c2-00553d3b375f",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[('Wciśnij przycisk Enter', 'Press the ENTER button', 0.8636363636363636)]"
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]
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},
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"execution_count": 80,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sentence = 'Wcisnij pszycisk ęnter'\n",
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"threshold = 0.8\n",
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"fuzzy_lookup(sentence, threshold, translation_memory)"
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]
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}
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],
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@ -358,7 +424,7 @@
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"author": "Rafał Jaworski",
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"email": "rjawor@amu.edu.pl",
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"kernelspec": {
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"display_name": "Python 3",
|
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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@ -373,7 +439,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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"version": "3.10.12"
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},
|
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"subtitle": "2. Zaawansowane użycie pamięci tłumaczeń",
|
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"title": "Komputerowe wspomaganie tłumaczenia",
|
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168
lab/lab_03.ipynb
168
lab/lab_03.ipynb
@ -63,7 +63,7 @@
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"id": "diverse-sunglasses",
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"metadata": {},
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"source": [
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"Odpowiedź:"
|
||||
"Odpowiedź: metal cabinets guides. Proz.com"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -128,13 +128,41 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"execution_count": 7,
|
||||
"id": "cognitive-cedar",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def terminology_lookup():\n",
|
||||
" return []"
|
||||
"import re\n",
|
||||
"\n",
|
||||
"def terminology_lookup(text, dictionary):\n",
|
||||
" pattern = re.compile(r'\\b(?:' + '|'.join(dictionary) + r')\\b', re.IGNORECASE)\n",
|
||||
" matches = pattern.finditer(text)\n",
|
||||
" occurance = ''\n",
|
||||
" for match in matches:\n",
|
||||
" occurance += (f\"({match.start()}, {match.end()})\")\n",
|
||||
" return occurance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "5781b95b-3af9-4c82-8388-b98a11e6c343",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'(80, 91)(164, 175)(468, 475)(516, 523)(533, 540)'"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"terminology_lookup(text, dictionary)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -161,7 +189,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 12,
|
||||
"id": "tribal-attention",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
@ -205,7 +233,7 @@
|
||||
"IDE\n",
|
||||
",\n",
|
||||
"see\n",
|
||||
"Running\n",
|
||||
"run\n",
|
||||
"Tutorial\n",
|
||||
"Examples\n",
|
||||
"in\n",
|
||||
@ -218,7 +246,7 @@
|
||||
"work\n",
|
||||
"for\n",
|
||||
"all\n",
|
||||
"swing\n",
|
||||
"Swing\n",
|
||||
"program\n",
|
||||
"—\n",
|
||||
"applet\n",
|
||||
@ -232,7 +260,7 @@
|
||||
"be\n",
|
||||
"the\n",
|
||||
"step\n",
|
||||
"-PRON-\n",
|
||||
"you\n",
|
||||
"need\n",
|
||||
"to\n",
|
||||
"follow\n",
|
||||
@ -248,7 +276,7 @@
|
||||
"platform\n",
|
||||
",\n",
|
||||
"if\n",
|
||||
"-PRON-\n",
|
||||
"you\n",
|
||||
"have\n",
|
||||
"not\n",
|
||||
"already\n",
|
||||
@ -260,7 +288,7 @@
|
||||
"program\n",
|
||||
"that\n",
|
||||
"use\n",
|
||||
"Swing\n",
|
||||
"swing\n",
|
||||
"component\n",
|
||||
".\n",
|
||||
"compile\n",
|
||||
@ -302,13 +330,31 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 19,
|
||||
"id": "surgical-demonstration",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def terminology_lookup():\n",
|
||||
" return []"
|
||||
"def terminology_lookup(text, dictionary):\n",
|
||||
" nlp = spacy.load(\"en_core_web_sm\")\n",
|
||||
" doc = nlp(text)\n",
|
||||
"\n",
|
||||
" word_forms = set()\n",
|
||||
" for word in dictionary:\n",
|
||||
" word_forms.add(word)\n",
|
||||
" for token in doc:\n",
|
||||
" if token.text.lower() == word:\n",
|
||||
" word_forms.add(token.lemma_)\n",
|
||||
"\n",
|
||||
" matches = []\n",
|
||||
" for token in doc:\n",
|
||||
" if token.text.lower() in word_forms:\n",
|
||||
" matches.append((token.idx, token.idx + len(token)))\n",
|
||||
"\n",
|
||||
" occurrences = ''\n",
|
||||
" for match in matches:\n",
|
||||
" occurrences += f\"({match[0]}, {match[1]})\"\n",
|
||||
" return occurrences"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -337,13 +383,59 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"execution_count": 23,
|
||||
"id": "superb-butterfly",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_nouns(text):\n",
|
||||
" return []"
|
||||
" nlp = spacy.load(\"en_core_web_sm\")\n",
|
||||
" doc = nlp(text)\n",
|
||||
" nouns = [token.text for token in doc if token.pos_ == \"NOUN\"]\n",
|
||||
" \n",
|
||||
" return nouns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"id": "8203c3e5-74a6-42c1-add1-e378f09164fd",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"['programmers',\n",
|
||||
" 'section',\n",
|
||||
" 'Swing',\n",
|
||||
" 'application',\n",
|
||||
" 'command',\n",
|
||||
" 'line',\n",
|
||||
" 'information',\n",
|
||||
" 'Swing',\n",
|
||||
" 'application',\n",
|
||||
" 'compilation',\n",
|
||||
" 'instructions',\n",
|
||||
" 'programs',\n",
|
||||
" 'applets',\n",
|
||||
" 'applications',\n",
|
||||
" 'steps',\n",
|
||||
" 'release',\n",
|
||||
" 'platform',\n",
|
||||
" 'program',\n",
|
||||
" 'Swing',\n",
|
||||
" 'components',\n",
|
||||
" 'program',\n",
|
||||
" 'program']"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"get_nouns(text)"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -356,7 +448,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"execution_count": 25,
|
||||
"id": "acting-tolerance",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
@ -374,13 +466,29 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"execution_count": 30,
|
||||
"id": "eight-redhead",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def extract_terms(text):\n",
|
||||
" return []"
|
||||
" nlp = spacy.load(\"en_core_web_sm\")\n",
|
||||
" doc = nlp(text)\n",
|
||||
" \n",
|
||||
" tally = {}\n",
|
||||
" \n",
|
||||
" for token in doc:\n",
|
||||
" if token.pos_ != \"NOUN\":\n",
|
||||
" continue\n",
|
||||
" \n",
|
||||
" lemma = token.lemma_.lower()\n",
|
||||
" \n",
|
||||
" if lemma in tally:\n",
|
||||
" tally[lemma] += 1\n",
|
||||
" else:\n",
|
||||
" tally[lemma] = 1\n",
|
||||
" \n",
|
||||
" return tally"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -393,13 +501,29 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"execution_count": 32,
|
||||
"id": "monetary-mambo",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def extract_terms(text):\n",
|
||||
" return []"
|
||||
" nlp = spacy.load(\"en_core_web_sm\")\n",
|
||||
" doc = nlp(text)\n",
|
||||
" \n",
|
||||
" tally = {}\n",
|
||||
" \n",
|
||||
" for token in doc:\n",
|
||||
" if token.pos_ not in ['NOUN', 'VERB', 'ADJ']:\n",
|
||||
" continue\n",
|
||||
" \n",
|
||||
" lemma = token.lemma_.lower()\n",
|
||||
" \n",
|
||||
" if lemma in tally:\n",
|
||||
" tally[lemma] += 1\n",
|
||||
" else:\n",
|
||||
" tally[lemma] = 1\n",
|
||||
" \n",
|
||||
" return tally"
|
||||
]
|
||||
}
|
||||
],
|
||||
@ -407,7 +531,7 @@
|
||||
"author": "Rafał Jaworski",
|
||||
"email": "rjawor@amu.edu.pl",
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
@ -422,7 +546,7 @@
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.10"
|
||||
"version": "3.10.12"
|
||||
},
|
||||
"subtitle": "3. Terminologia",
|
||||
"title": "Komputerowe wspomaganie tłumaczenia",
|
||||
|
||||
Loading…
Reference in New Issue
Block a user