228 lines
9.8 KiB
Plaintext
228 lines
9.8 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": false
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},
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"source": [
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"\n",
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"<div class=\"alert alert-block alert-info\">\n",
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"<h1> Systemy Dialogowe </h1>\n",
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"<h2> 7. <i>Parsing semantyczny z wykorzystaniem gramatyk</i> [laboratoria]</h2> \n",
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"<h3> Marek Kubis (2021)</h3>\n",
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"</div>\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Parsing semantyczny z wykorzystaniem gramatyk\n",
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"=============================================\n",
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"\n",
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"Warto\u015bci slot\u00f3w mo\u017cemy wydobywa\u0107 z wypowiedzi u\u017cytkownika korzystaj\u0105c z takich technik, jak:\n",
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"\n",
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" - wyszukiwanie s\u0142\u00f3w kluczowych w tek\u015bcie,\n",
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"\n",
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" - dopasowywanie wzorc\u00f3w zbudowanych przy u\u017cyciu wyra\u017ce\u0144 regularnych,\n",
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"\n",
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" - parsery regu\u0142owe (temat dzisiejszych zaj\u0119\u0107),\n",
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"\n",
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" - uczenie maszynowe (temat kolejnych zaj\u0119\u0107)."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Przyk\u0142ady parser\u00f3w regu\u0142owych\n",
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"-----------------------------\n",
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"\n",
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" - [Phoenix](http://wiki.speech.cs.cmu.edu/olympus/index.php/Phoenix_Server) \u2014 parser gramatyk\n",
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" bezkontekstowych whodz\u0105cy w sk\u0142ad systemu dialogowego [Olympus](http://wiki.speech.cs.cmu.edu/olympus/index.php/Olympus)\n",
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"\n",
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" - Parsery [DCG](https://www.swi-prolog.org/pldoc/man?section=DCG) (Definite Clause Grammars) j\u0119zyka [Prolog](https://www.swi-prolog.org/)\n",
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"\n",
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" - [JSpeech Grammar Format](https://www.w3.org/TR/jsgf/) (JSGF)\n",
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"\n",
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"Przyk\u0142ad\n",
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"--------\n",
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"Zapiszmy w JSGF gramatyk\u0119 semantyczn\u0105 dla aktu dialogowego reprezentuj\u0105cego zamiar rezerwacji\n",
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"stolika w restauracji."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%%writefile book.jsgf\n",
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"#JSGF V1.0 UTF-8 pl;\n",
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"\n",
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"grammar book;\n",
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"\n",
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"public <rezerwuj> = chcia\u0142bym zarezerwowa\u0107 stolik <dzien_rezerwacji> <godzina_rezerwacji> <liczba_osob> ;\n",
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"\n",
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"<dzien_rezerwacji> = na <dzien> {day};\n",
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"\n",
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"<dzien> = dzisiaj | jutro | poniedzia\u0142ek | wtorek | \u015brod\u0119 | czwartek | pi\u0105tek | sobot\u0119 | niedziel\u0119;\n",
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"\n",
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"<godzina_rezerwacji> = na [godzin\u0119] <godzina_z_minutami> {hour};\n",
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"\n",
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"<godzina_z_minutami> = <godzina> [<minuty>];\n",
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"\n",
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"<godzina> = dziewi\u0105t\u0105 | dziesi\u0105t\u0105 | jedenast\u0105 | dwunast\u0105;\n",
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"\n",
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"<minuty> = pietna\u015bcie | trzydzie\u015bci;\n",
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"\n",
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"<liczba_osob> = (na | dla) <liczba> {size} os\u00f3b;\n",
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"\n",
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"<liczba> = dwie | dw\u00f3ch | trzy | trzech | cztery | czterech | pi\u0119\u0107 | pieciu;\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Parser akceptuj\u0105cy powy\u017csz\u0105 gramatyk\u0119 utworzymy korzystaj\u0105c z biblioteki [pyjsgf](https://github.com/Danesprite/pyjsgf)."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import jsgf\n",
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"\n",
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"book_grammar = jsgf.parse_grammar_file('book.jsgf')\n",
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"book_grammar"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Wykorzystajmy gramatyk\u0119 `book.jsgf` do analizy nast\u0119puj\u0105cej wypowiedzi"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"utterance = 'chcia\u0142bym zarezerwowa\u0107 stolik na jutro na godzin\u0119 dwunast\u0105 trzydzie\u015bci na pi\u0119\u0107 os\u00f3b'\n",
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"matched = book_grammar.find_matching_rules(utterance)\n",
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"matched"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Reprezentacj\u0119 znaczenia mo\u017cna wydoby\u0107 ze sparsowanej wypowiedzi na wiele sposob\u00f3w. My do\n",
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"wydobywania slot\u00f3w wykorzystamy mechanizm tag\u00f3w JSGF a za nazw\u0119 aktu dialogowego przyjmiemy nazw\u0119\n",
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"gramatyki. Wzoruj\u0105c si\u0119 na [DSTC2](https://github.com/matthen/dstc) wynikow\u0105 ram\u0119 zapiszemy korzystaj\u0105c ze s\u0142ownika o polach `act` i `slots`."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_dialog_act(rule):\n",
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" slots = []\n",
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" get_slots(rule.expansion, slots)\n",
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" return {'act': rule.grammar.name, 'slots': slots}\n",
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"\n",
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"def get_slots(expansion, slots):\n",
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" if expansion.tag != '':\n",
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" slots.append((expansion.tag, expansion.current_match))\n",
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" return\n",
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"\n",
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" for child in expansion.children:\n",
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" get_slots(child, slots)\n",
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"\n",
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" if not expansion.children and isinstance(expansion, jsgf.NamedRuleRef):\n",
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" get_slots(expansion.referenced_rule.expansion, slots)\n",
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"\n",
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"get_dialog_act(matched[0])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\u0141\u0105cz\u0105c powy\u017csze funkcje mo\u017cemy zbudowa\u0107 prosty modu\u0142 NLU."
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def nlu(utterance):\n",
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" matched = book_grammar.find_matching_rules(utterance)\n",
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"\n",
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" if matched:\n",
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" return get_dialog_act(matched[0])\n",
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" else:\n",
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" return {'act': 'null', 'slots': []}\n",
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"\n",
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"nlu('chcia\u0142bym zarezerwowa\u0107 stolik na jutro na godzin\u0119 dziesi\u0105t\u0105 dla trzech os\u00f3b')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Problemy\n",
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"--------\n",
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"\n",
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" - Co z normalizacj\u0105 wyra\u017ce\u0144 liczbowych takich, jak godziny, daty czy numery telefon\u00f3w?\n",
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"\n",
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" - Co w przypadku gdy wi\u0119cej ni\u017c jedna regu\u0142a zostanie dopasowana?"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Zadanie\n",
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"-------\n",
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"Zaimplementowa\u0107 analizator j\u0119zyka naturalnego (NLU) na potrzeby realizowanego agenta dialogowego.\n",
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"\n",
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"Modu\u0142 powinien by\u0107 zbudowany z wykorzystaniem parsingu regu\u0142owego i/lub technik uczenia maszynowego.\n",
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"\n",
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"Przygotowa\u0107 skrypt `evaluate.py` wyznaczaj\u0105cy *dok\u0142adno\u015b\u0107* (ang. accuracy) analizatora wzgl\u0119dem zgromadzonego korpusu eksperymentalnego,\n",
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"tj. stosunek liczby wypowiedzi u\u017cytkownika, w kt\u00f3rych akty dialogowe zosta\u0142y rozpoznane prawid\u0142owo do liczby wszystkich wypowiedzi u\u017cytkownika w korpusie.\n",
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"\n",
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"Analizator j\u0119zyka naturalnego umie\u015bci\u0107 w ga\u0142\u0119zi `master` repozytorium projektowego. Skrypt `evaluate.py` umie\u015bci\u0107 w katalogu g\u0142\u00f3wnym tej ga\u0142\u0119zi.\n",
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"\n",
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"Termin: 4.05.2022, godz. 23:59."
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]
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}
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],
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"metadata": {
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"jupytext": {
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"cell_metadata_filter": "-all",
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"main_language": "python",
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"notebook_metadata_filter": "-all"
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},
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"author": "Marek Kubis",
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"email": "mkubis@amu.edu.pl",
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"lang": "pl",
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"subtitle": "7.Parsing semantyczny z wykorzystaniem gramatyk[laboratoria]",
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"title": "Systemy Dialogowe",
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"year": "2021"
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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