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added lab3

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This commit is contained in:
Adam Stelmaszyk 2024-04-15 22:09:49 +02:00
parent e343070e32
commit 854b3629df
1 changed files with 231 additions and 27 deletions
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258
lab/lab_03.ipynb
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@ -86,7 +86,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 55,
"id": "loving-prince", "id": "loving-prince",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
@ -100,6 +100,14 @@
"text += \" Create a program that uses Swing components. Compile the program. Run the program.\"" "text += \" Create a program that uses Swing components. Compile the program. Run the program.\""
] ]
}, },
{
"cell_type": "code",
"execution_count": null,
"id": "05436dad",
"metadata": {},
"outputs": [],
"source": []
},
{ {
"cell_type": "markdown", "cell_type": "markdown",
"id": "extreme-cycling", "id": "extreme-cycling",
@ -110,12 +118,12 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 56,
"id": "bound-auction", "id": "bound-auction",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"dictionary = ['program', 'application', 'applet' 'compile']" "dictionary = ['program', 'application', 'applet', 'compile']"
] ]
}, },
{ {
@ -128,13 +136,41 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 17,
"id": "cognitive-cedar", "id": "cognitive-cedar",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"{'program': [(468, 475), (516, 523), (533, 540)],\n",
" 'application': [(80, 91), (164, 175)],\n",
" 'compile': [(56, 63), (504, 511)]}"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"def terminology_lookup():\n", "import re\n",
" return []" "\n",
"def terminology_lookup(dictionary, text):\n",
" termValues = dict()\n",
" for element in dictionary:\n",
" values = []\n",
" pattern = re.compile(r'\\b{}\\b'.format(re.escape(element)))\n",
" for match in pattern.finditer(text.lower()):\n",
" values.append((match.start(), match.end()))\n",
" \n",
" if len(values) != 0:\n",
" termValues[element] = values\n",
" \n",
" return termValues\n",
"\n",
"terminology_lookup(dictionary, text)\n",
"\n"
] ]
}, },
{ {
@ -161,7 +197,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 18,
"id": "tribal-attention", "id": "tribal-attention",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -205,7 +241,7 @@
"IDE\n", "IDE\n",
",\n", ",\n",
"see\n", "see\n",
"Running\n", "run\n",
"Tutorial\n", "Tutorial\n",
"Examples\n", "Examples\n",
"in\n", "in\n",
@ -218,7 +254,7 @@
"work\n", "work\n",
"for\n", "for\n",
"all\n", "all\n",
"swing\n", "Swing\n",
"program\n", "program\n",
"—\n", "—\n",
"applet\n", "applet\n",
@ -232,7 +268,7 @@
"be\n", "be\n",
"the\n", "the\n",
"step\n", "step\n",
"-PRON-\n", "you\n",
"need\n", "need\n",
"to\n", "to\n",
"follow\n", "follow\n",
@ -248,7 +284,7 @@
"platform\n", "platform\n",
",\n", ",\n",
"if\n", "if\n",
"-PRON-\n", "you\n",
"have\n", "have\n",
"not\n", "not\n",
"already\n", "already\n",
@ -260,7 +296,7 @@
"program\n", "program\n",
"that\n", "that\n",
"use\n", "use\n",
"Swing\n", "swing\n",
"component\n", "component\n",
".\n", ".\n",
"compile\n", "compile\n",
@ -302,13 +338,48 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 43,
"id": "surgical-demonstration", "id": "surgical-demonstration",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"{'program': [(291, 299), (468, 475), (516, 523), (533, 540)],\n",
" 'application': [(80, 91), (164, 175), (322, 334)],\n",
" 'applet': [(302, 309)],\n",
" 'compile': [(56, 63), (134, 143), (504, 511)]}"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"def terminology_lookup():\n", "def terminology_lookup(dictionary, text):\n",
" return []" " termValues = dict()\n",
" lowerText = text.lower()\n",
" nlp = spacy.load(\"en_core_web_sm\")\n",
"\n",
" splitText = nlp(lowerText)\n",
" for findingWord in dictionary:\n",
" values = []\n",
" startFromIndex = 0\n",
"\n",
" for word in splitText:\n",
" if word.lemma_ == findingWord:\n",
" textBegining = lowerText.index(word.text,startFromIndex)\n",
" textEnding = textBegining + len(word)\n",
" startFromIndex = textEnding\n",
" values.append((textBegining,textEnding))\n",
" \n",
" if len(values) != 0:\n",
" termValues[findingWord] = values\n",
" \n",
" return termValues\n",
"\n",
"terminology_lookup(dictionary, text)"
] ]
}, },
{ {
@ -337,13 +408,31 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 54,
"id": "superb-butterfly", "id": "superb-butterfly",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"set()"
]
},
"execution_count": 54,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"import spacy\n",
"\n",
"def get_nouns(text):\n", "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",
" return set(nouns)\n",
"\n",
"get_nouns(text)"
] ]
}, },
{ {
@ -374,13 +463,66 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 71,
"id": "eight-redhead", "id": "eight-redhead",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"data": {
"text/plain": [
"{'line': 1,\n",
" 'release': 1,\n",
" 'compilation': 1,\n",
" 'component': 1,\n",
" 'section': 1,\n",
" 'information': 1,\n",
" 'program': 4,\n",
" 'command': 1,\n",
" 'platform': 1,\n",
" 'applet': 1,\n",
" 'application': 3,\n",
" 'swing': 4,\n",
" 'instruction': 1,\n",
" 'step': 1,\n",
" 'programmer': 1}"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"import spacy\n",
"\n",
"def get_nouns(text):\n",
" nlp = spacy.load(\"en_core_web_sm\")\n",
" doc = nlp(text)\n",
" nouns = [token.lemma_ for token in doc if token.pos_ == \"NOUN\"]\n",
" return set(nouns)\n",
"\n",
"def getElementsNumbers(dictionary, text):\n",
" termValues = dict()\n",
" lowerText = text.lower()\n",
" nlp = spacy.load(\"en_core_web_sm\")\n",
"\n",
" splitText = nlp(lowerText)\n",
" for findingWord in dictionary:\n",
" elementNumber = 0\n",
"\n",
" for word in splitText:\n",
" if word.lemma_ == findingWord:\n",
" elementNumber = elementNumber +1\n",
" \n",
" if elementNumber != 0:\n",
" termValues[findingWord] = elementNumber\n",
" \n",
" return termValues\n",
"\n",
"def extract_terms(text):\n", "def extract_terms(text):\n",
" return []" " return getElementsNumbers(get_nouns(text), text)\n",
"\n",
"extract_terms(text)"
] ]
}, },
{ {
@ -393,13 +535,75 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 86,
"id": "monetary-mambo", "id": "monetary-mambo",
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"def extract_terms(text):\n", "def get_dictonery_by_type(text, type):\n",
" return []" " nlp = spacy.load(\"en_core_web_sm\")\n",
" doc = nlp(text)\n",
" nouns = [token.lemma_ for token in doc if token.pos_ == type]\n",
" return set(nouns)\n",
"\n",
"\n",
"def extract_terms(text, type):\n",
" return getElementsNumbers(get_dictonery_by_type(text, type), text)\n",
"\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 87,
"id": "8f7eeb73",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'compile': 3,\n",
" 'work': 1,\n",
" 'install': 1,\n",
" 'create': 1,\n",
" 'explain': 1,\n",
" 'run': 4,\n",
" 'see': 1,\n",
" 'need': 1,\n",
" 'do': 1,\n",
" 'follow': 1,\n",
" 'use': 2}"
]
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"extract_terms(text, 'VERB')"
]
},
{
"cell_type": "code",
"execution_count": 93,
"id": "71c14cab",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'late': 1}"
]
},
"execution_count": 93,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"extract_terms(text, 'ADJ')"
] ]
} }
], ],
@ -422,7 +626,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.8.10" "version": "3.11.7"
}, },
"subtitle": "3. Terminologia", "subtitle": "3. Terminologia",
"title": "Komputerowe wspomaganie tłumaczenia", "title": "Komputerowe wspomaganie tłumaczenia",