{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dependency Parsing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "This tutorial is available as an IPython notebook at [Malaya/example/dependency](https://github.com/huseinzol05/Malaya/tree/master/example/dependency).\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "This module only trained on standard language structure, so it is not save to use it for local language structure.\n", " \n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 6.15 s, sys: 1.31 s, total: 7.46 s\n", "Wall time: 9.21 s\n" ] } ], "source": [ "%%time\n", "import malaya" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Models accuracy\n", "\n", "We use `sklearn.metrics.classification_report` for accuracy reporting, check at https://malaya.readthedocs.io/en/latest/models-accuracy.html#dependency-parsing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Describe supported dependencies" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:you can read more from https://universaldependencies.org/treebanks/id_pud/index.html\n" ] }, { "data": { "text/html": [ "
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TagDescription
0aclclausal modifier of noun
1advcladverbial clause modifier
2advmodadverbial modifier
3amodadjectival modifier
4apposappositional modifier
5auxauxiliary
6casecase marking
7ccompclausal complement
8compoundcompound
9compound:plurplural compound
10conjconjunct
11copcop
12csubjclausal subject
13depdependent
14detdeterminer
15fixedmulti-word expression
16flatname
17iobjindirect object
18markmarker
19nmodnominal modifier
20nsubjnominal subject
21objdirect object
22parataxisparataxis
23rootroot
24xcompopen clausal complement
\n", "
" ], "text/plain": [ " Tag Description\n", "0 acl clausal modifier of noun\n", "1 advcl adverbial clause modifier\n", "2 advmod adverbial modifier\n", "3 amod adjectival modifier\n", "4 appos appositional modifier\n", "5 aux auxiliary\n", "6 case case marking\n", "7 ccomp clausal complement\n", "8 compound compound\n", "9 compound:plur plural compound\n", "10 conj conjunct\n", "11 cop cop\n", "12 csubj clausal subject\n", "13 dep dependent\n", "14 det determiner\n", "15 fixed multi-word expression\n", "16 flat name\n", "17 iobj indirect object\n", "18 mark marker\n", "19 nmod nominal modifier\n", "20 nsubj nominal subject\n", "21 obj direct object\n", "22 parataxis parataxis\n", "23 root root\n", "24 xcomp open clausal complement" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "malaya.dependency.describe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### List available transformer Dependency models\n", "\n", "```python\n", "def available_transformer(version: str = 'v2'):\n", " \"\"\"\n", " List available transformer dependency parsing models.\n", "\n", " Parameters\n", " ----------\n", " version : str, optional (default='v2')\n", " Version supported. Allowed values:\n", "\n", " * ``'v1'`` - version 1, maintain for knowledge graph.\n", " * ``'v2'`` - Trained on bigger dataset, better version.\n", "\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:tested on 20% test set.\n" ] }, { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Size (MB)Quantized Size (MB)Arc AccuracyTypes AccuracyRoot Accuracy
bert455.0114.000.8204500.799700.98936
tiny-bert69.717.500.7952520.724700.98939
albert60.815.300.8218950.797521.00000
tiny-albert33.48.510.7865000.758701.00000
xlnet480.2121.000.8481100.827410.92101
alxlnet61.216.400.8492900.828100.92099
\n", "
" ], "text/plain": [ " Size (MB) Quantized Size (MB) Arc Accuracy Types Accuracy \\\n", "bert 455.0 114.00 0.820450 0.79970 \n", "tiny-bert 69.7 17.50 0.795252 0.72470 \n", "albert 60.8 15.30 0.821895 0.79752 \n", "tiny-albert 33.4 8.51 0.786500 0.75870 \n", "xlnet 480.2 121.00 0.848110 0.82741 \n", "alxlnet 61.2 16.40 0.849290 0.82810 \n", "\n", " Root Accuracy \n", "bert 0.98936 \n", "tiny-bert 0.98939 \n", "albert 1.00000 \n", "tiny-albert 1.00000 \n", "xlnet 0.92101 \n", "alxlnet 0.92099 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "malaya.dependency.available_transformer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load xlnet dependency model\n", "\n", "```python\n", "def transformer(version: str = 'v2', model: str = 'xlnet', quantized: bool = False, **kwargs):\n", " \"\"\"\n", " Load Transformer Dependency Parsing model, transfer learning Transformer + biaffine attention.\n", "\n", " Parameters\n", " ----------\n", " version : str, optional (default='v2')\n", " Version supported. Allowed values:\n", "\n", " * ``'v1'`` - version 1, maintain for knowledge graph.\n", " * ``'v2'`` - Trained on bigger dataset, better version.\n", "\n", " model : str, optional (default='xlnet')\n", " Model architecture supported. Allowed values:\n", "\n", " * ``'bert'`` - Google BERT BASE parameters.\n", " * ``'tiny-bert'`` - Google BERT TINY parameters.\n", " * ``'albert'`` - Google ALBERT BASE parameters.\n", " * ``'tiny-albert'`` - Google ALBERT TINY parameters.\n", " * ``'xlnet'`` - Google XLNET BASE parameters.\n", " * ``'alxlnet'`` - Malaya ALXLNET BASE parameters.\n", "\n", " quantized : bool, optional (default=False)\n", " if True, will load 8-bit quantized model.\n", " Quantized model not necessary faster, totally depends on the machine.\n", "\n", " Returns\n", " -------\n", " result: model\n", " List of model classes:\n", "\n", " * if `bert` in model, will return `malaya.model.bert.DependencyBERT`.\n", " * if `xlnet` in model, will return `malaya.model.xlnet.DependencyXLNET`.\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:running dependency-v2/albert using device /device:CPU:0\n" ] } ], "source": [ "model = malaya.dependency.transformer(model = 'albert')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load Quantized model\n", "\n", "To load 8-bit quantized model, simply pass `quantized = True`, default is `False`.\n", "\n", "We can expect slightly accuracy drop from quantized model, and not necessary faster than normal 32-bit float model, totally depends on machine." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:root:Load quantized model will cause accuracy drop.\n", "INFO:root:running dependency-v2/albert-quantized using device /device:CPU:0\n" ] } ], "source": [ "quantized_model = malaya.dependency.transformer(model = 'albert', quantized = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Predict\n", "\n", "```python\n", "def predict(self, string: str):\n", " \"\"\"\n", " Tag a string.\n", "\n", " Parameters\n", " ----------\n", " string: str\n", "\n", " Returns\n", " -------\n", " result: Tuple\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "string = 'Dr Mahathir menasihati mereka supaya berhenti berehat dan tidur sebentar sekiranya mengantuk ketika memandu.'" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "G\n", "\n", "\n", "\n", "0\n", "0 (None)\n", "\n", "\n", "\n", "3\n", "3 (menasihati)\n", "\n", "\n", "\n", "0->3\n", "\n", "\n", "root\n", "\n", "\n", "\n", "1\n", "1 (Dr)\n", "\n", "\n", "\n", "3->1\n", "\n", "\n", "nsubj\n", "\n", "\n", "\n", "4\n", "4 (mereka)\n", "\n", "\n", "\n", "3->4\n", "\n", "\n", "obj\n", "\n", "\n", "\n", "6\n", "6 (berhenti)\n", "\n", "\n", "\n", "3->6\n", "\n", "\n", "conj\n", "\n", "\n", "\n", "15\n", "15 (.)\n", "\n", "\n", "\n", "3->15\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "2\n", "2 (Mahathir)\n", "\n", "\n", "\n", "1->2\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "5\n", "5 (supaya)\n", "\n", "\n", "\n", "6->5\n", "\n", "\n", "cc\n", "\n", "\n", "\n", "7\n", "7 (berehat)\n", "\n", "\n", "\n", "6->7\n", "\n", "\n", "xcomp\n", "\n", "\n", "\n", "9\n", "9 (tidur)\n", "\n", "\n", "\n", "6->9\n", "\n", "\n", "conj\n", "\n", "\n", "\n", "8\n", "8 (dan)\n", "\n", "\n", "\n", "9->8\n", "\n", "\n", "cc\n", "\n", "\n", "\n", "12\n", "12 (mengantuk)\n", "\n", "\n", "\n", "9->12\n", "\n", "\n", "xcomp\n", "\n", "\n", "\n", "14\n", "14 (memandu)\n", "\n", "\n", "\n", "9->14\n", "\n", "\n", "advcl\n", "\n", "\n", "\n", "10\n", "10 (sebentar)\n", "\n", "\n", "\n", "12->10\n", "\n", "\n", "case\n", "\n", "\n", "\n", "11\n", "11 (sekiranya)\n", "\n", "\n", "\n", "12->11\n", "\n", "\n", "advmod\n", "\n", "\n", "\n", "13\n", "13 (ketika)\n", "\n", "\n", "\n", "14->13\n", "\n", "\n", "mark\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d_object, tagging, indexing = model.predict(string)\n", "d_object.to_graphvis()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "G\n", "\n", "\n", "\n", "0\n", "0 (None)\n", "\n", "\n", "\n", "3\n", "3 (menasihati)\n", "\n", "\n", "\n", "0->3\n", "\n", "\n", "root\n", "\n", "\n", "\n", "1\n", "1 (Dr)\n", "\n", "\n", "\n", "3->1\n", "\n", "\n", "nsubj\n", "\n", "\n", "\n", "4\n", "4 (mereka)\n", "\n", "\n", "\n", "3->4\n", "\n", "\n", "obj\n", "\n", "\n", "\n", "6\n", "6 (berhenti)\n", "\n", "\n", "\n", "3->6\n", "\n", "\n", "conj\n", "\n", "\n", "\n", "15\n", "15 (.)\n", "\n", "\n", "\n", "3->15\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "2\n", "2 (Mahathir)\n", "\n", "\n", "\n", "1->2\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "5\n", "5 (supaya)\n", "\n", "\n", "\n", "6->5\n", "\n", "\n", "cc\n", "\n", "\n", "\n", "7\n", "7 (berehat)\n", "\n", "\n", "\n", "6->7\n", "\n", "\n", "xcomp\n", "\n", "\n", "\n", "9\n", "9 (tidur)\n", "\n", "\n", "\n", "6->9\n", "\n", "\n", "conj\n", "\n", "\n", "\n", "8\n", "8 (dan)\n", "\n", "\n", "\n", "9->8\n", "\n", "\n", "cc\n", "\n", "\n", "\n", "12\n", "12 (mengantuk)\n", "\n", "\n", "\n", "9->12\n", "\n", "\n", "xcomp\n", "\n", "\n", "\n", "14\n", "14 (memandu)\n", "\n", "\n", "\n", "9->14\n", "\n", "\n", "advcl\n", "\n", "\n", "\n", "10\n", "10 (sebentar)\n", "\n", "\n", "\n", "12->10\n", "\n", "\n", "case\n", "\n", "\n", "\n", "11\n", "11 (sekiranya)\n", "\n", "\n", "\n", "12->11\n", "\n", "\n", "advmod\n", "\n", "\n", "\n", "13\n", "13 (ketika)\n", "\n", "\n", "\n", "14->13\n", "\n", "\n", "mark\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d_object, tagging, indexing = quantized_model.predict(string)\n", "d_object.to_graphvis()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Voting stack model" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:root:running dependency-v2/alxlnet using device /device:CPU:0\n" ] }, { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "G\n", "\n", "\n", "\n", "0\n", "0 (None)\n", "\n", "\n", "\n", "3\n", "3 (menasihati)\n", "\n", "\n", "\n", "0->3\n", "\n", "\n", "root\n", "\n", "\n", "\n", "1\n", "1 (Dr)\n", "\n", "\n", "\n", "3->1\n", "\n", "\n", "nsubj\n", "\n", "\n", "\n", "4\n", "4 (mereka)\n", "\n", "\n", "\n", "3->4\n", "\n", "\n", "obj\n", "\n", "\n", "\n", "6\n", "6 (berhenti)\n", "\n", "\n", "\n", "3->6\n", "\n", "\n", "conj\n", "\n", "\n", "\n", "15\n", "15 (.)\n", "\n", "\n", "\n", "3->15\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "2\n", "2 (Mahathir)\n", "\n", "\n", "\n", "1->2\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "5\n", "5 (supaya)\n", "\n", "\n", "\n", "6->5\n", "\n", "\n", "cc\n", "\n", "\n", "\n", "7\n", "7 (berehat)\n", "\n", "\n", "\n", "6->7\n", "\n", "\n", "xcomp\n", "\n", "\n", "\n", "9\n", "9 (tidur)\n", "\n", "\n", "\n", "6->9\n", "\n", "\n", "conj\n", "\n", "\n", "\n", "8\n", "8 (dan)\n", "\n", "\n", "\n", "9->8\n", "\n", "\n", "cc\n", "\n", "\n", "\n", "12\n", "12 (mengantuk)\n", "\n", "\n", "\n", "9->12\n", "\n", "\n", "xcomp\n", "\n", "\n", "\n", "14\n", "14 (memandu)\n", "\n", "\n", "\n", "9->14\n", "\n", "\n", "advcl\n", "\n", "\n", "\n", "10\n", "10 (sebentar)\n", "\n", "\n", "\n", "12->10\n", "\n", "\n", "case\n", "\n", "\n", "\n", "11\n", "11 (sekiranya)\n", "\n", "\n", "\n", "12->11\n", "\n", "\n", "advmod\n", "\n", "\n", "\n", "13\n", "13 (ketika)\n", "\n", "\n", "\n", "14->13\n", "\n", "\n", "mark\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "alxlnet = malaya.dependency.transformer(model = 'alxlnet')\n", "tagging, indexing = malaya.stack.voting_stack([model, model, alxlnet], string)\n", "malaya.dependency.dependency_graph(tagging, indexing).to_graphvis()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Harder example" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# https://www.astroawani.com/berita-malaysia/terbaik-tun-kita-geng-najib-razak-puji-tun-m-297884\n", "\n", "s = \"\"\"\n", "KUALA LUMPUR: Dalam hal politik, jarang sekali untuk melihat dua figura ini - bekas Perdana Menteri, Datuk Seri Najib Razak dan Tun Dr Mahathir Mohamad mempunyai 'pandangan yang sama' atau sekapal. Namun, situasi itu berbeza apabila melibatkan isu ketidakpatuhan terhadap prosedur operasi standard (SOP). Najib, yang juga Ahli Parlimen Pekan memuji sikap Ahli Parlimen Langkawi itu yang mengaku bersalah selepas melanggar SOP kerana tidak mengambil suhu badan ketika masuk ke sebuah surau di Langkawi pada Sabtu lalu.\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "G\n", "\n", "\n", "\n", "0\n", "0 (None)\n", "\n", "\n", "\n", "11\n", "11 (melihat)\n", "\n", "\n", "\n", "0->11\n", "\n", "\n", "root\n", "\n", "\n", "\n", "1\n", "1 (KUALA)\n", "\n", "\n", "\n", "11->1\n", "\n", "\n", "nsubj\n", "\n", "\n", "\n", "8\n", "8 (jarang)\n", "\n", "\n", "\n", "11->8\n", "\n", "\n", "advmod\n", "\n", "\n", "\n", "9\n", "9 (sekali)\n", "\n", "\n", "\n", "11->9\n", "\n", "\n", "advmod\n", "\n", "\n", "\n", "10\n", "10 (untuk)\n", "\n", "\n", "\n", "11->10\n", "\n", "\n", "case\n", "\n", "\n", "\n", "29\n", "29 (mempunyai)\n", "\n", "\n", "\n", "11->29\n", "\n", "\n", "advcl\n", "\n", "\n", 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"\n", "\n", "71\n", "71 (selepas)\n", "\n", "\n", "\n", "72->71\n", "\n", "\n", "case\n", "\n", "\n", "\n", "73\n", "73 (SOP)\n", "\n", "\n", "\n", "72->73\n", "\n", "\n", "obj\n", "\n", "\n", "\n", "76\n", "76 (mengambil)\n", "\n", "\n", "\n", "72->76\n", "\n", "\n", "advcl\n", "\n", "\n", "\n", "74\n", "74 (kerana)\n", "\n", "\n", "\n", "76->74\n", "\n", "\n", "mark\n", "\n", "\n", "\n", "75\n", "75 (tidak)\n", "\n", "\n", "\n", "76->75\n", "\n", "\n", "advmod\n", "\n", "\n", "\n", "77\n", "77 (suhu)\n", "\n", "\n", "\n", "76->77\n", "\n", "\n", "obj\n", "\n", "\n", "\n", "80\n", "80 (masuk)\n", "\n", "\n", "\n", "76->80\n", "\n", "\n", "advcl\n", "\n", "\n", "\n", "78\n", "78 (badan)\n", "\n", "\n", "\n", "77->78\n", "\n", "\n", "compound\n", "\n", "\n", "\n", "79\n", "79 (ketika)\n", "\n", "\n", "\n", "80->79\n", "\n", "\n", "mark\n", "\n", "\n", "\n", "83\n", "83 (surau)\n", "\n", "\n", "\n", "80->83\n", "\n", "\n", "obl\n", "\n", "\n", "\n", "85\n", "85 (Langkawi)\n", "\n", "\n", "\n", "80->85\n", "\n", "\n", "obl\n", "\n", "\n", "\n", "87\n", "87 (Sabtu)\n", "\n", "\n", "\n", "80->87\n", "\n", "\n", "obl\n", "\n", "\n", "\n", "81\n", "81 (ke)\n", "\n", "\n", "\n", "83->81\n", "\n", "\n", "case\n", "\n", "\n", "\n", "82\n", "82 (sebuah)\n", "\n", "\n", "\n", "83->82\n", "\n", "\n", "det\n", "\n", "\n", "\n", "84\n", "84 (di)\n", "\n", "\n", "\n", "85->84\n", "\n", "\n", "case\n", "\n", "\n", "\n", "86\n", "86 (pada)\n", "\n", "\n", "\n", "87->86\n", "\n", "\n", "case\n", "\n", "\n", "\n", "88\n", "88 (lalu)\n", "\n", "\n", "\n", "87->88\n", "\n", "\n", "amod\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tagging, indexing = malaya.stack.voting_stack([model, model, alxlnet], s)\n", "malaya.dependency.dependency_graph(tagging, indexing).to_graphvis()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Dependency graph object\n", "\n", "To initiate a dependency graph from dependency models, you need to call `malaya.dependency.dependency_graph`." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph = malaya.dependency.dependency_graph(tagging, indexing)\n", "graph" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### generate graphvis" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n", "\n", "G\n", "\n", "\n", "\n", "0\n", "0 (None)\n", "\n", "\n", "\n", "11\n", "11 (melihat)\n", "\n", "\n", "\n", "0->11\n", "\n", "\n", "root\n", "\n", "\n", "\n", "1\n", "1 (KUALA)\n", "\n", "\n", "\n", "11->1\n", "\n", "\n", "nsubj\n", "\n", "\n", "\n", "8\n", "8 (jarang)\n", "\n", "\n", "\n", "11->8\n", "\n", "\n", "advmod\n", "\n", "\n", "\n", "9\n", "9 (sekali)\n", "\n", "\n", "\n", "11->9\n", "\n", "\n", "advmod\n", "\n", "\n", "\n", "10\n", "10 (untuk)\n", "\n", "\n", "\n", "11->10\n", "\n", "\n", "case\n", "\n", "\n", "\n", "29\n", "29 (mempunyai)\n", "\n", "\n", "\n", "11->29\n", "\n", "\n", "advcl\n", "\n", "\n", "\n", "42\n", "42 (berbeza)\n", "\n", "\n", "\n", "11->42\n", "\n", "\n", "dep\n", "\n", "\n", "\n", "2\n", "2 (LUMPUR)\n", "\n", "\n", "\n", "1->2\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "5\n", "5 (hal)\n", "\n", "\n", "\n", "1->5\n", "\n", "\n", "obl\n", "\n", "\n", "\n", "7\n", "7 (,)\n", "\n", "\n", "\n", "1->7\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "3\n", "3 (:)\n", "\n", "\n", "\n", "5->3\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "4\n", "4 (Dalam)\n", "\n", "\n", "\n", "5->4\n", "\n", "\n", "case\n", "\n", "\n", "\n", "6\n", "6 (politik)\n", "\n", "\n", "\n", "5->6\n", "\n", "\n", "compound\n", "\n", "\n", "\n", "13\n", "13 (figura)\n", "\n", "\n", "\n", "29->13\n", "\n", "\n", "obj\n", "\n", "\n", "\n", "31\n", "31 (pandangan)\n", "\n", "\n", "\n", "29->31\n", "\n", "\n", "obj\n", "\n", "\n", "\n", "37\n", "37 (.)\n", "\n", "\n", "\n", "29->37\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "38\n", "38 (Namun)\n", "\n", "\n", "\n", "29->38\n", "\n", "\n", "mark\n", "\n", "\n", "\n", "39\n", "39 (,)\n", "\n", "\n", "\n", "42->39\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "40\n", "40 (situasi)\n", "\n", "\n", "\n", "42->40\n", "\n", "\n", "nsubj\n", "\n", "\n", "\n", "54\n", "54 (.)\n", "\n", "\n", "\n", "42->54\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "89\n", "89 (.)\n", "\n", "\n", "\n", "42->89\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "44\n", "44 (melibatkan)\n", "\n", "\n", "\n", "42->44\n", "\n", "\n", "advcl\n", "\n", "\n", "\n", "55\n", "55 (Najib)\n", "\n", "\n", "\n", "42->55\n", "\n", "\n", "dep\n", "\n", "\n", "\n", "12\n", "12 (dua)\n", "\n", "\n", "\n", "13->12\n", "\n", "\n", "nummod\n", "\n", "\n", "\n", "15\n", "15 (-)\n", "\n", "\n", "\n", "13->15\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "16\n", "16 (bekas)\n", "\n", "\n", "\n", "13->16\n", "\n", "\n", "compound:plur\n", "\n", "\n", "\n", "17\n", "17 (Perdana)\n", "\n", "\n", "\n", "13->17\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "14\n", "14 (ini)\n", "\n", "\n", "\n", "17->14\n", "\n", "\n", "det\n", "\n", "\n", "\n", "18\n", "18 (Menteri)\n", "\n", "\n", "\n", "17->18\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "19\n", "19 (,)\n", "\n", "\n", "\n", "17->19\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "20\n", "20 (Datuk)\n", "\n", "\n", "\n", "17->20\n", "\n", "\n", "appos\n", "\n", "\n", "\n", "25\n", "25 (Tun)\n", "\n", "\n", "\n", "17->25\n", "\n", "\n", "conj\n", "\n", "\n", "\n", "21\n", "21 (Seri)\n", "\n", "\n", "\n", "20->21\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "24\n", "24 (dan)\n", "\n", "\n", "\n", "25->24\n", "\n", "\n", "cc\n", "\n", "\n", "\n", "26\n", "26 (Dr)\n", "\n", "\n", "\n", "25->26\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "22\n", "22 (Najib)\n", "\n", "\n", "\n", "21->22\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "23\n", "23 (Razak)\n", "\n", "\n", "\n", "22->23\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "27\n", "27 (Mahathir)\n", "\n", "\n", "\n", "26->27\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "28\n", "28 (Mohamad)\n", "\n", "\n", "\n", "27->28\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "30\n", "30 (')\n", "\n", "\n", "\n", "31->30\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "33\n", "33 (sama)\n", "\n", "\n", "\n", "31->33\n", "\n", "\n", "amod\n", "\n", "\n", "\n", "36\n", "36 (sekapal)\n", "\n", "\n", "\n", "33->36\n", "\n", "\n", "conj\n", "\n", "\n", "\n", "32\n", "32 (yang)\n", "\n", "\n", "\n", "36->32\n", "\n", "\n", "nsubj\n", "\n", "\n", "\n", "34\n", "34 (')\n", "\n", "\n", "\n", "36->34\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "35\n", "35 (atau)\n", "\n", "\n", "\n", "36->35\n", "\n", "\n", "cc\n", "\n", "\n", "\n", "41\n", "41 (itu)\n", "\n", "\n", "\n", "40->41\n", "\n", "\n", "det\n", "\n", "\n", "\n", "43\n", "43 (apabila)\n", "\n", "\n", "\n", "44->43\n", "\n", "\n", "mark\n", "\n", "\n", "\n", "45\n", "45 (isu)\n", "\n", "\n", "\n", "44->45\n", "\n", "\n", "obj\n", "\n", "\n", "\n", "56\n", "56 (,)\n", "\n", "\n", "\n", "55->56\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "59\n", "59 (Ahli)\n", "\n", "\n", "\n", "55->59\n", "\n", "\n", "nsubj\n", "\n", "\n", "\n", "62\n", "62 (memuji)\n", "\n", "\n", "\n", "55->62\n", "\n", "\n", "acl\n", "\n", "\n", "\n", "46\n", "46 (ketidakpatuhan)\n", "\n", "\n", "\n", "45->46\n", "\n", "\n", "compound\n", "\n", "\n", "\n", "48\n", "48 (prosedur)\n", "\n", "\n", "\n", "45->48\n", "\n", "\n", "nmod\n", "\n", "\n", "\n", "47\n", "47 (terhadap)\n", "\n", "\n", "\n", "48->47\n", "\n", "\n", "case\n", "\n", "\n", "\n", "49\n", "49 (operasi)\n", "\n", "\n", "\n", "48->49\n", "\n", "\n", "compound\n", "\n", "\n", "\n", "50\n", "50 (standard)\n", "\n", "\n", "\n", "48->50\n", "\n", "\n", "amod\n", "\n", "\n", "\n", "52\n", "52 (SOP)\n", "\n", "\n", "\n", "48->52\n", "\n", "\n", "appos\n", "\n", "\n", "\n", "51\n", "51 (()\n", "\n", "\n", "\n", "52->51\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "53\n", "53 ())\n", "\n", "\n", "\n", "52->53\n", "\n", "\n", "punct\n", "\n", "\n", "\n", "57\n", "57 (yang)\n", "\n", "\n", "\n", "59->57\n", "\n", "\n", "nsubj\n", "\n", "\n", "\n", "58\n", "58 (juga)\n", "\n", "\n", "\n", "59->58\n", "\n", "\n", "advmod\n", "\n", "\n", "\n", "60\n", "60 (Parlimen)\n", "\n", "\n", "\n", "59->60\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "63\n", "63 (sikap)\n", "\n", "\n", "\n", "62->63\n", "\n", "\n", "obj\n", "\n", "\n", "\n", "61\n", "61 (Pekan)\n", "\n", "\n", "\n", "60->61\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "64\n", "64 (Ahli)\n", "\n", "\n", "\n", "63->64\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "69\n", "69 (mengaku)\n", "\n", "\n", "\n", "63->69\n", "\n", "\n", "acl\n", "\n", "\n", "\n", "65\n", "65 (Parlimen)\n", "\n", "\n", "\n", "64->65\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "68\n", "68 (yang)\n", "\n", "\n", "\n", "69->68\n", "\n", "\n", "nsubj\n", "\n", "\n", "\n", "70\n", "70 (bersalah)\n", "\n", "\n", "\n", "69->70\n", "\n", "\n", "xcomp\n", "\n", "\n", "\n", "66\n", "66 (Langkawi)\n", "\n", "\n", "\n", "65->66\n", "\n", "\n", "flat\n", "\n", "\n", "\n", "67\n", "67 (itu)\n", "\n", "\n", "\n", "66->67\n", "\n", "\n", "det\n", "\n", "\n", "\n", "72\n", "72 (melanggar)\n", "\n", "\n", "\n", "70->72\n", "\n", "\n", "xcomp\n", "\n", "\n", "\n", "71\n", "71 (selepas)\n", "\n", "\n", "\n", "72->71\n", "\n", "\n", "case\n", "\n", "\n", "\n", "73\n", "73 (SOP)\n", "\n", "\n", "\n", "72->73\n", "\n", "\n", "obj\n", "\n", "\n", "\n", "76\n", "76 (mengambil)\n", "\n", "\n", "\n", "72->76\n", "\n", "\n", "advcl\n", "\n", "\n", "\n", "74\n", "74 (kerana)\n", "\n", "\n", "\n", "76->74\n", "\n", "\n", "mark\n", "\n", "\n", "\n", "75\n", "75 (tidak)\n", "\n", "\n", "\n", "76->75\n", "\n", "\n", "advmod\n", "\n", "\n", "\n", "77\n", "77 (suhu)\n", "\n", "\n", "\n", "76->77\n", "\n", "\n", "obj\n", "\n", "\n", "\n", "80\n", "80 (masuk)\n", "\n", "\n", "\n", "76->80\n", "\n", "\n", "advcl\n", "\n", "\n", "\n", "78\n", "78 (badan)\n", "\n", "\n", "\n", "77->78\n", "\n", "\n", "compound\n", "\n", "\n", "\n", "79\n", "79 (ketika)\n", "\n", "\n", "\n", "80->79\n", "\n", "\n", "mark\n", "\n", "\n", "\n", "83\n", "83 (surau)\n", "\n", "\n", "\n", "80->83\n", "\n", "\n", "obl\n", "\n", "\n", "\n", "85\n", "85 (Langkawi)\n", "\n", "\n", "\n", "80->85\n", "\n", "\n", "obl\n", "\n", "\n", "\n", "87\n", "87 (Sabtu)\n", "\n", "\n", "\n", "80->87\n", "\n", "\n", "obl\n", "\n", "\n", "\n", "81\n", "81 (ke)\n", "\n", "\n", "\n", "83->81\n", "\n", "\n", "case\n", "\n", "\n", "\n", "82\n", "82 (sebuah)\n", "\n", "\n", "\n", "83->82\n", "\n", "\n", "det\n", "\n", "\n", "\n", "84\n", "84 (di)\n", "\n", "\n", "\n", "85->84\n", "\n", "\n", "case\n", "\n", "\n", "\n", "86\n", "86 (pada)\n", "\n", "\n", "\n", "87->86\n", "\n", "\n", "case\n", "\n", "\n", "\n", "88\n", "88 (lalu)\n", "\n", "\n", "\n", "87->88\n", "\n", "\n", "amod\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph.to_graphvis()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Get nodes" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "defaultdict(.()>,\n", " {0: {'address': 0,\n", " 'word': None,\n", " 'lemma': None,\n", " 'ctag': 'TOP',\n", " 'tag': 'TOP',\n", " 'feats': None,\n", " 'head': None,\n", " 'deps': defaultdict(list, {'root': [11]}),\n", " 'rel': None},\n", " 1: {'address': 1,\n", " 'word': 'KUALA',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 11,\n", " 'deps': defaultdict(list,\n", " {'flat': [2], 'obl': [5], 'punct': [7]}),\n", " 'rel': 'nsubj'},\n", " 11: {'address': 11,\n", " 'word': 'melihat',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 0,\n", " 'deps': defaultdict(list,\n", " {'nsubj': [1],\n", " 'advmod': [8, 9],\n", " 'case': [10],\n", " 'advcl': [29],\n", " 'dep': [42]}),\n", " 'rel': 'root'},\n", " 2: {'address': 2,\n", " 'word': 'LUMPUR',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 1,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'flat'},\n", " 3: {'address': 3,\n", " 'word': ':',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 5,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'punct'},\n", " 5: {'address': 5,\n", " 'word': 'hal',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 1,\n", " 'deps': defaultdict(list,\n", " {'punct': [3], 'case': [4], 'compound': [6]}),\n", " 'rel': 'obl'},\n", " 4: {'address': 4,\n", " 'word': 'Dalam',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 5,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'case'},\n", " 6: {'address': 6,\n", " 'word': 'politik',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 5,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'compound'},\n", " 7: {'address': 7,\n", " 'word': ',',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 1,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'punct'},\n", " 8: {'address': 8,\n", " 'word': 'jarang',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 11,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'advmod'},\n", " 9: {'address': 9,\n", " 'word': 'sekali',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 11,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'advmod'},\n", " 10: {'address': 10,\n", " 'word': 'untuk',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 11,\n", " 'deps': 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defaultdict(list, {}),\n", " 'rel': 'flat'},\n", " 30: {'address': 30,\n", " 'word': \"'\",\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 31,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'punct'},\n", " 31: {'address': 31,\n", " 'word': 'pandangan',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 29,\n", " 'deps': defaultdict(list, {'punct': [30], 'amod': [33]}),\n", " 'rel': 'obj'},\n", " 32: {'address': 32,\n", " 'word': 'yang',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 36,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'nsubj'},\n", " 36: {'address': 36,\n", " 'word': 'sekapal',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 33,\n", " 'deps': defaultdict(list,\n", " {'nsubj': [32], 'punct': [34], 'cc': [35]}),\n", " 'rel': 'conj'},\n", " 33: {'address': 33,\n", " 'word': 'sama',\n", " 'lemma': 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" 'head': 44,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'mark'},\n", " 44: {'address': 44,\n", " 'word': 'melibatkan',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 42,\n", " 'deps': defaultdict(list, {'mark': [43], 'obj': [45]}),\n", " 'rel': 'advcl'},\n", " 45: {'address': 45,\n", " 'word': 'isu',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 44,\n", " 'deps': defaultdict(list, {'compound': [46], 'nmod': [48]}),\n", " 'rel': 'obj'},\n", " 46: {'address': 46,\n", " 'word': 'ketidakpatuhan',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 45,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'compound'},\n", " 47: {'address': 47,\n", " 'word': 'terhadap',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 48,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'case'},\n", " 48: {'address': 48,\n", " 'word': 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defaultdict(list,\n", " {'mark': [74],\n", " 'advmod': [75],\n", " 'obj': [77],\n", " 'advcl': [80]}),\n", " 'rel': 'advcl'},\n", " 75: {'address': 75,\n", " 'word': 'tidak',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 76,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'advmod'},\n", " 77: {'address': 77,\n", " 'word': 'suhu',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 76,\n", " 'deps': defaultdict(list, {'compound': [78]}),\n", " 'rel': 'obj'},\n", " 78: {'address': 78,\n", " 'word': 'badan',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 77,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'compound'},\n", " 79: {'address': 79,\n", " 'word': 'ketika',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 80,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'mark'},\n", " 80: {'address': 80,\n", " 'word': 'masuk',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 76,\n", " 'deps': defaultdict(list, {'mark': [79], 'obl': [83, 85, 87]}),\n", " 'rel': 'advcl'},\n", " 81: {'address': 81,\n", " 'word': 'ke',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 83,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'case'},\n", " 83: {'address': 83,\n", " 'word': 'surau',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 80,\n", " 'deps': defaultdict(list, {'case': [81], 'det': [82]}),\n", " 'rel': 'obl'},\n", " 82: {'address': 82,\n", " 'word': 'sebuah',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 83,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'det'},\n", " 84: {'address': 84,\n", " 'word': 'di',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 85,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'case'},\n", " 85: {'address': 85,\n", " 'word': 'Langkawi',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 80,\n", " 'deps': defaultdict(list, {'case': [84]}),\n", " 'rel': 'obl'},\n", " 86: {'address': 86,\n", " 'word': 'pada',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 87,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'case'},\n", " 87: {'address': 87,\n", " 'word': 'Sabtu',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 80,\n", " 'deps': defaultdict(list, {'case': [86], 'amod': [88]}),\n", " 'rel': 'obl'},\n", " 88: {'address': 88,\n", " 'word': 'lalu',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 87,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'amod'},\n", " 89: {'address': 89,\n", " 'word': '.',\n", " 'lemma': '_',\n", " 'ctag': '_',\n", " 'tag': '_',\n", " 'feats': '_',\n", " 'head': 42,\n", " 'deps': defaultdict(list, {}),\n", " 'rel': 'punct'}})" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph.nodes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Flat the graph" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[(('melihat', '_'), 'nsubj', ('KUALA', '_')),\n", " (('KUALA', '_'), 'flat', ('LUMPUR', '_')),\n", " (('KUALA', '_'), 'obl', ('hal', '_')),\n", " (('hal', '_'), 'punct', (':', '_')),\n", " (('hal', '_'), 'case', ('Dalam', '_')),\n", " (('hal', '_'), 'compound', ('politik', '_')),\n", " (('KUALA', '_'), 'punct', (',', '_')),\n", " (('melihat', '_'), 'advmod', ('jarang', '_')),\n", " (('melihat', '_'), 'advmod', ('sekali', '_')),\n", " (('melihat', '_'), 'case', ('untuk', '_')),\n", " (('melihat', '_'), 'advcl', ('mempunyai', '_')),\n", " (('mempunyai', '_'), 'obj', ('figura', '_')),\n", " (('figura', '_'), 'nummod', ('dua', '_')),\n", " (('figura', '_'), 'punct', ('-', '_')),\n", " (('figura', '_'), 'compound:plur', ('bekas', '_')),\n", " (('figura', '_'), 'flat', ('Perdana', '_')),\n", " (('Perdana', '_'), 'det', ('ini', '_')),\n", " (('Perdana', '_'), 'flat', ('Menteri', '_')),\n", " (('Perdana', '_'), 'punct', (',', '_')),\n", " (('Perdana', '_'), 'appos', ('Datuk', '_')),\n", " (('Datuk', '_'), 'flat', ('Seri', '_')),\n", " (('Seri', '_'), 'flat', ('Najib', '_')),\n", " (('Najib', '_'), 'flat', ('Razak', '_')),\n", " (('Perdana', '_'), 'conj', ('Tun', '_')),\n", " (('Tun', '_'), 'cc', ('dan', '_')),\n", " (('Tun', '_'), 'flat', ('Dr', '_')),\n", " (('Dr', '_'), 'flat', ('Mahathir', '_')),\n", " (('Mahathir', '_'), 'flat', ('Mohamad', '_')),\n", " (('mempunyai', '_'), 'obj', ('pandangan', '_')),\n", " (('pandangan', '_'), 'punct', (\"'\", '_')),\n", " (('pandangan', '_'), 'amod', ('sama', '_')),\n", " (('sama', '_'), 'conj', ('sekapal', '_')),\n", " (('sekapal', '_'), 'nsubj', ('yang', '_')),\n", " (('sekapal', '_'), 'punct', (\"'\", '_')),\n", " (('sekapal', '_'), 'cc', ('atau', '_')),\n", " (('mempunyai', '_'), 'punct', ('.', '_')),\n", " (('mempunyai', '_'), 'mark', ('Namun', '_')),\n", " (('melihat', '_'), 'dep', ('berbeza', '_')),\n", " (('berbeza', '_'), 'punct', (',', '_')),\n", " (('berbeza', '_'), 'nsubj', ('situasi', '_')),\n", " (('situasi', '_'), 'det', ('itu', '_')),\n", " (('berbeza', '_'), 'advcl', ('melibatkan', '_')),\n", " (('melibatkan', '_'), 'mark', ('apabila', '_')),\n", " (('melibatkan', '_'), 'obj', ('isu', '_')),\n", " (('isu', '_'), 'compound', ('ketidakpatuhan', '_')),\n", " (('isu', '_'), 'nmod', ('prosedur', '_')),\n", " (('prosedur', '_'), 'case', ('terhadap', '_')),\n", " (('prosedur', '_'), 'compound', ('operasi', '_')),\n", " (('prosedur', '_'), 'amod', ('standard', '_')),\n", " (('prosedur', '_'), 'appos', ('SOP', '_')),\n", " (('SOP', '_'), 'punct', ('(', '_')),\n", " (('SOP', '_'), 'punct', (')', '_')),\n", " (('berbeza', '_'), 'punct', ('.', '_')),\n", " (('berbeza', '_'), 'dep', ('Najib', '_')),\n", " (('Najib', '_'), 'punct', (',', '_')),\n", " (('Najib', '_'), 'nsubj', ('Ahli', '_')),\n", " (('Ahli', '_'), 'nsubj', ('yang', '_')),\n", " (('Ahli', '_'), 'advmod', ('juga', '_')),\n", " (('Ahli', '_'), 'flat', ('Parlimen', '_')),\n", " (('Parlimen', '_'), 'flat', ('Pekan', '_')),\n", " (('Najib', '_'), 'acl', ('memuji', '_')),\n", " (('memuji', '_'), 'obj', ('sikap', '_')),\n", " (('sikap', '_'), 'flat', ('Ahli', '_')),\n", " (('Ahli', '_'), 'flat', ('Parlimen', '_')),\n", " (('Parlimen', '_'), 'flat', ('Langkawi', '_')),\n", " (('Langkawi', '_'), 'det', ('itu', '_')),\n", " (('sikap', '_'), 'acl', ('mengaku', '_')),\n", " (('mengaku', '_'), 'nsubj', ('yang', '_')),\n", " (('mengaku', '_'), 'xcomp', ('bersalah', '_')),\n", " (('bersalah', '_'), 'xcomp', ('melanggar', '_')),\n", " (('melanggar', '_'), 'case', ('selepas', '_')),\n", " (('melanggar', '_'), 'obj', ('SOP', '_')),\n", " (('melanggar', '_'), 'advcl', ('mengambil', '_')),\n", " (('mengambil', '_'), 'mark', ('kerana', '_')),\n", " (('mengambil', '_'), 'advmod', ('tidak', '_')),\n", " (('mengambil', '_'), 'obj', ('suhu', '_')),\n", " (('suhu', '_'), 'compound', ('badan', '_')),\n", " (('mengambil', '_'), 'advcl', ('masuk', '_')),\n", " (('masuk', '_'), 'mark', ('ketika', '_')),\n", " (('masuk', '_'), 'obl', ('surau', '_')),\n", " (('surau', '_'), 'case', ('ke', '_')),\n", " (('surau', '_'), 'det', ('sebuah', '_')),\n", " (('masuk', '_'), 'obl', ('Langkawi', '_')),\n", " (('Langkawi', '_'), 'case', ('di', '_')),\n", " (('masuk', '_'), 'obl', ('Sabtu', '_')),\n", " (('Sabtu', '_'), 'case', ('pada', '_')),\n", " (('Sabtu', '_'), 'amod', ('lalu', '_')),\n", " (('berbeza', '_'), 'punct', ('.', '_'))]" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list(graph.triples())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Check the graph contains cycles" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "graph.contains_cycle()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generate networkx\n", "\n", "Make sure you already installed networkx, \n", "\n", "```bash\n", "pip install networkx\n", "```" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digraph = graph.to_networkx()\n", "digraph" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import networkx as nx\n", "import matplotlib.pyplot as plt\n", "nx.draw_networkx(digraph)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "OutMultiEdgeDataView([(1, 11), (2, 1), (3, 5), (4, 5), (5, 1), (6, 5), (7, 1), (8, 11), (9, 11), (10, 11), (12, 13), (13, 29), (14, 17), (15, 13), (16, 13), (17, 13), (18, 17), (19, 17), (20, 17), (21, 20), (22, 21), (23, 22), (24, 25), (25, 17), (26, 25), (27, 26), (28, 27), (29, 11), (30, 31), (31, 29), (32, 36), (33, 31), (34, 36), (35, 36), (36, 33), (37, 29), (38, 29), (39, 42), (40, 42), (41, 40), (42, 11), (43, 44), (44, 42), (45, 44), (46, 45), (47, 48), (48, 45), (49, 48), (50, 48), (51, 52), (52, 48), (53, 52), (54, 42), (55, 42), (56, 55), (57, 59), (58, 59), (59, 55), (60, 59), (61, 60), (62, 55), (63, 62), (64, 63), (65, 64), (66, 65), (67, 66), (68, 69), (69, 63), (70, 69), (71, 72), (72, 70), (73, 72), (74, 76), (75, 76), (76, 72), (77, 76), (78, 77), (79, 80), (80, 76), (81, 83), (82, 83), (83, 80), (84, 85), (85, 80), (86, 87), (87, 80), (88, 87), (89, 42)])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digraph.edges()" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "NodeView((1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89))" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digraph.nodes()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1: 'KUALA',\n", " 2: 'LUMPUR',\n", " 3: ':',\n", " 4: 'Dalam',\n", " 5: 'hal',\n", " 6: 'politik',\n", " 7: ',',\n", " 8: 'jarang',\n", " 9: 'sekali',\n", " 10: 'untuk',\n", " 11: 'melihat',\n", " 12: 'dua',\n", " 13: 'figura',\n", " 14: 'ini',\n", " 15: '-',\n", " 16: 'bekas',\n", " 17: 'Perdana',\n", " 18: 'Menteri',\n", " 19: ',',\n", " 20: 'Datuk',\n", " 21: 'Seri',\n", " 22: 'Najib',\n", " 23: 'Razak',\n", " 24: 'dan',\n", " 25: 'Tun',\n", " 26: 'Dr',\n", " 27: 'Mahathir',\n", " 28: 'Mohamad',\n", " 29: 'mempunyai',\n", " 30: \"'\",\n", " 31: 'pandangan',\n", " 32: 'yang',\n", " 33: 'sama',\n", " 34: \"'\",\n", " 35: 'atau',\n", " 36: 'sekapal',\n", " 37: '.',\n", " 38: 'Namun',\n", " 39: ',',\n", " 40: 'situasi',\n", " 41: 'itu',\n", " 42: 'berbeza',\n", " 43: 'apabila',\n", " 44: 'melibatkan',\n", " 45: 'isu',\n", " 46: 'ketidakpatuhan',\n", " 47: 'terhadap',\n", " 48: 'prosedur',\n", " 49: 'operasi',\n", " 50: 'standard',\n", " 51: '(',\n", " 52: 'SOP',\n", " 53: ')',\n", " 54: '.',\n", " 55: 'Najib',\n", " 56: ',',\n", " 57: 'yang',\n", " 58: 'juga',\n", " 59: 'Ahli',\n", " 60: 'Parlimen',\n", " 61: 'Pekan',\n", " 62: 'memuji',\n", " 63: 'sikap',\n", " 64: 'Ahli',\n", " 65: 'Parlimen',\n", " 66: 'Langkawi',\n", " 67: 'itu',\n", " 68: 'yang',\n", " 69: 'mengaku',\n", " 70: 'bersalah',\n", " 71: 'selepas',\n", " 72: 'melanggar',\n", " 73: 'SOP',\n", " 74: 'kerana',\n", " 75: 'tidak',\n", " 76: 'mengambil',\n", " 77: 'suhu',\n", " 78: 'badan',\n", " 79: 'ketika',\n", " 80: 'masuk',\n", " 81: 'ke',\n", " 82: 'sebuah',\n", " 83: 'surau',\n", " 84: 'di',\n", " 85: 'Langkawi',\n", " 86: 'pada',\n", " 87: 'Sabtu',\n", " 88: 'lalu',\n", " 89: '.'}" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "labels = {i:graph.get_by_address(i)['word'] for i in digraph.nodes()}\n", "labels" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(15,5))\n", "nx.draw_networkx(digraph,labels=labels)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Vectorize\n", "\n", "Let say you want to visualize word level in lower dimension, you can use `model.vectorize`,\n", "\n", "```python\n", "def vectorize(self, string: str):\n", " \"\"\"\n", " vectorize a string.\n", "\n", " Parameters\n", " ----------\n", " string: List[str]\n", "\n", " Returns\n", " -------\n", " result: np.array\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [], "source": [ "r = quantized_model.vectorize(s)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "x = [i[0] for i in r]\n", "y = [i[1] for i in r]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(89, 2)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.manifold import TSNE\n", "import matplotlib.pyplot as plt\n", "\n", "tsne = TSNE().fit_transform(y)\n", "tsne.shape" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (7, 7))\n", "plt.scatter(tsne[:, 0], tsne[:, 1])\n", "labels = x\n", "for label, x, y in zip(\n", " labels, tsne[:, 0], tsne[:, 1]\n", "):\n", " label = (\n", " '%s, %.3f' % (label[0], label[1])\n", " if isinstance(label, list)\n", " else label\n", " )\n", " plt.annotate(\n", " label,\n", " xy = (x, y),\n", " xytext = (0, 0),\n", " textcoords = 'offset points',\n", " )" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" } }, "nbformat": 4, "nbformat_minor": 2 }