{
    "info": {
        "author": "Lovit",
        "author_email": "soy.lovit@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3.6"
        ],
        "description": "# soynlp\n\n\ud55c\uad6d\uc5b4 \ubd84\uc11d\uc744 \uc704\ud55c pure python code \uc785\ub2c8\ub2e4. \ud559\uc2b5\ub370\uc774\ud130\ub97c \uc774\uc6a9\ud558\uc9c0 \uc54a\uc73c\uba74\uc11c \ub370\uc774\ud130\uc5d0 \uc874\uc7ac\ud558\ub294 \ub2e8\uc5b4\ub97c \ucc3e\uac70\ub098, \ubb38\uc7a5\uc744 \ub2e8\uc5b4\uc5f4\ub85c \ubd84\ud574, \ud639\uc740 \ud488\uc0ac \ud310\ubcc4\uc744 \ud560 \uc218 \uc788\ub294 \ube44\uc9c0\ub3c4\ud559\uc2b5 \uc811\uadfc\ubc95\uc744 \uc9c0\ud5a5\ud569\ub2c8\ub2e4.\n\n## Guide\n\n### Usage guide\n\nsoynlp \uc5d0\uc11c \uc81c\uacf5\ud558\ub294 WordExtractor \ub098 NounExtractor \ub294 \uc5ec\ub7ec \uac1c\uc758 \ubb38\uc11c\ub85c\ubd80\ud130 \ud559\uc2b5\ud55c \ud1b5\uacc4 \uc815\ubcf4\ub97c \uc774\uc6a9\ud558\uc5ec \uc791\ub3d9\ud569\ub2c8\ub2e4.\n\ube44\uc9c0\ub3c4\ud559\uc2b5 \uae30\ubc18 \uc811\uadfc\ubc95\ub4e4\uc740 \ud1b5\uacc4\uc801 \ud328\ud134\uc744 \uc774\uc6a9\ud558\uc5ec \ub2e8\uc5b4\ub97c \ucd94\ucd9c\ud558\uae30 \ub54c\ubb38\uc5d0 \ud558\ub098\uc758 \ubb38\uc7a5 \ud639\uc740 \ubb38\uc11c\uc5d0\uc11c \ubcf4\ub2e4\ub294 \uc5b4\ub290 \uc815\ub3c4 \uaddc\ubaa8\uac00 \uc788\ub294 \ub3d9\uc77c\ud55c \uc9d1\ub2e8\uc758 \ubb38\uc11c (homogeneous documents) \uc5d0\uc11c \uc798 \uc791\ub3d9\ud569\ub2c8\ub2e4.\n\uc601\ud654 \ub313\uae00\ub4e4\uc774\ub098 \ud558\ub8e8\uc758 \ub274\uc2a4 \uae30\uc0ac\ucc98\ub7fc \uac19\uc740 \ub2e8\uc5b4\ub97c \uc774\uc6a9\ud558\ub294 \uc9d1\ud569\uc758 \ubb38\uc11c\ub9cc \ubaa8\uc544\uc11c Extractors \ub97c \ud559\uc2b5\ud558\uc2dc\uba74 \uc88b\uc2b5\ub2c8\ub2e4.\n\uc774\uc9c8\uc801\uc778 \uc9d1\ub2e8\uc758 \ubb38\uc11c\ub4e4\uc740 \ud558\ub098\ub85c \ubaa8\uc544 \ud559\uc2b5\ud558\uba74 \ub2e8\uc5b4\uac00 \uc798 \ucd94\ucd9c\ub418\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\n### Parameter naming\n\nsoynlp=0.0.46 \uae4c\uc9c0\ub294 min_score, minimum_score, l_len_min \ucc98\ub7fc \ucd5c\uc18c\uac12\uc774\ub098 \ucd5c\ub300\uac12\uc744 \uc694\uad6c\ud558\ub294 parameters \uc758 \uc774\ub984\ub4e4\uc5d0 \uaddc\uce59\uc774 \uc5c6\uc5c8\uc2b5\ub2c8\ub2e4. \uc9c0\uae08\uae4c\uc9c0 \uc791\uc5c5\ud558\uc2e0 \ucf54\ub4dc\ub4e4 \uc911\uc5d0\uc11c \uc9c1\uc811 parameters \ub97c \uc124\uc815\ud558\uc2e0 \ubd84\ub4e4\uc5d0\uac8c \ud63c\ub780\uc744 \ub4dc\ub9b4 \uc218 \uc788\uc73c\ub098, **\ub354 \ub2a6\uae30\uc804\uc5d0 \uc774\ud6c4\uc5d0 \ubc1c\uc0dd\ud560 \ubd88\ud3b8\ud568\uc744 \uc904\uc774\uae30 \uc704\ud558\uc5ec** \ubcc0\uc218 \uba85\uc744 \uc218\uc815\ud558\uc600\uc2b5\ub2c8\ub2e4.\n\n0.0.47 \uc774\ud6c4 minimum, maximum \uc758 \uc758\ubbf8\uac00 \ub4e4\uc5b4\uac00\ub294 \ubcc0\uc218\uba85\uc740 min, max \ub85c \uc904\uc5ec \uae30\uc785\ud569\ub2c8\ub2e4.\n\uadf8 \ub4a4\uc5d0 \uc5b4\ub5a4 \ud56d\ubaa9\uc758 threshold parameter \uc778\uc9c0 \uc774\ub984\uc744 \uae30\uc785\ud569\ub2c8\ub2e4. \ub2e4\uc74c\uacfc \uac19\uc740 \ud328\ud134\uc73c\ub85c parameter \uc774\ub984\uc744 \ud1b5\uc77c\ud569\ub2c8\ub2e4.\n{min, max}\\_{noun, word}\\_{score, threshold} \ub4f1\uc73c\ub85c \uc774\ub984\uc744 \ud1b5\uc77c\ud569\ub2c8\ub2e4.\n\ud56d\ubaa9\uc774 \uc790\uba85\ud55c \uacbd\uc6b0\uc5d0\ub294 \uc774\ub97c \uc0dd\ub7b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\nsoynlp \uc5d0\uc11c\ub294 substring counting \uc744 \ud558\ub294 \uacbd\uc6b0\uac00 \ub9ce\uc2b5\ub2c8\ub2e4. \ube48\ub3c4\uc218\uc640 \uad00\ub828\ub41c parameter \ub294 count \uac00 \uc544\ub2cc frequency \ub85c \ud1b5\uc77c\ud569\ub2c8\ub2e4.\n\nindex \uc640 idx \ub294 idx \ub85c \ud1b5\uc77c\ud569\ub2c8\ub2e4.\n\n\uc22b\uc790\ub97c \uc758\ubbf8\ud558\ub294 num \uacfc n \uc740 num \uc73c\ub85c \ud1b5\uc77c\ud569\ub2c8\ub2e4.\n\n## Setup\n\n```shell\n$ pip install soynlp\n```\n\n## Python version\n\n- Python 3.5+ \ub97c \uc9c0\uc6d0\ud569\ub2c8\ub2e4. 3.x \uc5d0\uc11c \uc8fc\ub85c \uc791\uc5c5\uc744 \ud558\uae30 \ub54c\ubb38\uc5d0 3.x \ub85c \uc774\uc6a9\ud558\uc2dc\uae38 \uad8c\uc7a5\ud569\ub2c8\ub2e4.\n- Python 2.x \ub294 \ubaa8\ub4e0 \uae30\ub2a5\uc5d0 \ub300\ud574\uc11c \ud14c\uc2a4\ud2b8\uac00 \ub05d\ub098\uc9c0 \uc54a\uc558\uc2b5\ub2c8\ub2e4. \n\n## Requires\n\n- numpy >= 1.12.1\n- psutil >= 5.0.1\n- scipy >= 1.1.0\n- scikit-learn >= 0.20.0\n\n## Noun Extractor\n\n\uba85\uc0ac \ucd94\ucd9c\uc744 \ud558\uae30 \uc704\ud574 \uc5ec\ub7ec \uc2dc\ub3c4\ub97c \ud55c \uacb0\uacfc, v1, news, v2 \uc138 \uac00\uc9c0 \ubc84\uc804\uc774 \ub9cc\ub4e4\uc5b4\uc84c\uc2b5\ub2c8\ub2e4. \uac00\uc7a5 \uc88b\uc740 \uc131\ub2a5\uc744 \ubcf4\uc774\ub294 \uac83\uc740 v2 \uc785\ub2c8\ub2e4.\n\nWordExtractor \ub294 \ud1b5\uacc4\ub97c \uc774\uc6a9\ud558\uc5ec \ub2e8\uc5b4\uc758 \uacbd\uacc4 \uc810\uc218\ub97c \ud559\uc2b5\ud558\ub294 \uac83\uc77c \ubfd0, \uac01 \ub2e8\uc5b4\uc758 \ud488\uc0ac\ub97c \ud310\ub2e8\ud558\uc9c0\ub294 \ubabb\ud569\ub2c8\ub2e4. \ub54c\ub85c\ub294 \uac01 \ub2e8\uc5b4\uc758 \ud488\uc0ac\ub97c \uc54c\uc544\uc57c \ud558\ub294 \uacbd\uc6b0\uac00 \uc788\uc2b5\ub2c8\ub2e4. \ub610\ud55c \ub2e4\ub978 \ud488\uc0ac\ubcf4\ub2e4\ub3c4 \uba85\uc0ac\uc5d0\uc11c \uc0c8\ub85c\uc6b4 \ub2e8\uc5b4\uac00 \uac00\uc7a5 \ub9ce\uc774 \ub9cc\ub4e4\uc5b4\uc9d1\ub2c8\ub2e4. \uba85\uc0ac\uc758 \uc624\ub978\ucabd\uc5d0\ub294 -\uc740, -\ub294, -\ub77c\ub294, -\ud558\ub294 \ucc98\ub7fc \ud2b9\uc815 \uae00\uc790\ub4e4\uc774 \uc790\uc8fc \ub4f1\uc7a5\ud569\ub2c8\ub2e4. \ubb38\uc11c\uc758 \uc5b4\uc808 (\ub744\uc5b4\uc4f0\uae30 \uae30\uc900 \uc720\ub2db)\uc5d0\uc11c \uc67c\ucabd\uc5d0 \uc704\uce58\ud55c substring \uc758 \uc624\ub978\ucabd\uc5d0 \uc5b4\ub5a4 \uae00\uc790\ub4e4\uc774 \ub4f1\uc7a5\ud558\ub294\uc9c0 \ubd84\ud3ec\ub97c \uc0b4\ud3b4\ubcf4\uba74 \uba85\uc0ac\uc778\uc9c0 \uc544\ub2cc\uc9c0 \ud310\ub2e8\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. soynlp \uc5d0\uc11c\ub294 \ub450 \uac00\uc9c0 \uc885\ub958\uc758 \uba85\uc0ac \ucd94\ucd9c\uae30\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub458 \ubaa8\ub450 \uac1c\ubc1c \ub2e8\uacc4\uc774\uae30 \ub54c\ubb38\uc5d0 \uc5b4\ub5a4 \uac83\uc774 \ub354 \uc6b0\uc218\ud558\ub2e4 \ub9d0\ud558\uae30\ub294 \uc5b4\ub835\uc2b5\ub2c8\ub2e4\ub9cc, NewsNounExtractor \uac00 \uc880 \ub354 \ub9ce\uc740 \uae30\ub2a5\uc744 \ud3ec\ud568\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ucd94\ud6c4, \uba85\uc0ac \ucd94\ucd9c\uae30\ub294 \ud558\ub098\uc758 \ud074\ub798\uc2a4\ub85c \uc815\ub9ac\ub420 \uc608\uc815\uc785\ub2c8\ub2e4. \n\n### Noun Extractor ver 1 & News Noun Extractor\n\n```python\nfrom soynlp.noun import LRNounExtractor\nnoun_extractor = LRNounExtractor()\nnouns = noun_extractor.train_extract(sentences) # list of str like\n\nfrom soynlp.noun import NewsNounExtractor\nnoun_extractor = NewsNounExtractor()\nnouns = noun_extractor.train_extract(sentences) # list of str like\n```\n\n2016-10-20 \uc758 \ub274\uc2a4\ub85c\ubd80\ud130 \ud559\uc2b5\ud55c \uba85\uc0ac\uc758 \uc608\uc2dc\uc785\ub2c8\ub2e4. \n\n    \ub374\ub9c8\ud06c  \uc6c3\ub3c8  \ub108\ubb34\ub108\ubb34\ub108\ubb34  \uac00\ub77d\ub3d9  \ub9e4\ub274\uc5bc  \uc9c0\ub3c4\uad50\uc218\n    \uc804\ub9dd\uce58  \uac15\uad6c  \uc5b8\ub2c8\ub4e4  \uc2e0\uc0b0\uc5c5  \uae30\ub8b0\uc804  \ub178\uc2a4\n    \ud560\ub9ac\uc6b0\ub4dc  \ud50c\ub77c\uc790  \ubd88\ubc95\uc870\uc5c5  \uc6d4\uc2a4\ud2b8\ub9ac\ud2b8\uc800\ub110  2022\ub144  \ubd88\ud5c8\n    \uace0\uc528  \uc5b4\ud50c  1987\ub144  \ubd88\uc528  \uc801\uae30  \ub808\uc2a4\n    \uc2a4\ud018\uc5b4  \ucda9\ub2f9\uae08  \uac74\ucd95\ubb3c  \ub274\uc9c8\ub79c\ub4dc  \uc0ac\uac01  \ud558\ub098\uc529\n    \uadfc\ub300  \ud22c\uc790\uc8fc\uccb4\ubcc4  4\uc704  \ud0dc\uad8c  \ub124\ud2b8\uc6cd\uc2a4  \ubaa8\ubc14\uc77c\uac8c\uc784\n    \uc5f0\ub3d9  \ub7f0\uce6d  \ub9cc\uc131  \uc190\uc9c8  \uc81c\uc791\ubc95  \ud604\uc2e4\ud654\n    \uc624\ud574\uc601  \uc2ec\uc0ac\uc704\uc6d0\ub4e4  \ub2e8\uc810  \ubd80\uc7a5\uc870\ub9ac  \ucc28\uad00\uae09  \uac8c\uc2dc\ubb3c\n    \uc778\ud130\ud3f0  \uc6d0\ud654  \ub2e8\uae30\uac04  \ud3b8\uace1  \ubb34\uc0b0  \uc678\uad6d\uc778\ub4e4\n    \uc138\ubb34\uc870\uc0ac  \uc11d\uc720\ud654\ud559  \uc6cc\ud0b9  \uc6d0\ud53c\uc2a4  \uc11c\uc7a5  \uacf5\ubc94\n\n\ub354 \uc790\uc138\ud55c \uc124\uba85\uc740 [\ud29c\ud1a0\ub9ac\uc5bc][nounextractor-v1_usage]\uc5d0 \uc788\uc2b5\ub2c8\ub2e4. \n\n### Noun Extractor ver 2\n\nsoynlp=0.0.46+ \uc5d0\uc11c\ub294 \uba85\uc0ac \ucd94\ucd9c\uae30 version 2 \ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \uc774\uc804 \ubc84\uc804\uc758 \uba85\uc0ac \ucd94\ucd9c\uc758 \uc815\ud655\uc131\uacfc \ud569\uc131\uba85\uc0ac \uc778\uc2dd \ub2a5\ub825, \ucd9c\ub825\ub418\ub294 \uc815\ubcf4\uc758 \uc624\ub958\ub97c \uc218\uc815\ud55c \ubc84\uc804\uc785\ub2c8\ub2e4. \uc0ac\uc6a9\ubc95\uc740 version 1 \uacfc \ube44\uc2b7\ud569\ub2c8\ub2e4.\n\n```python\nfrom soynlp.utils import DoublespaceLineCorpus\nfrom soynlp.noun import LRNounExtractor_v2\n\ncorpus_path = '2016-10-20-news'\nsents = DoublespaceLineCorpus(corpus_path, iter_sent=True)\n\nnoun_extractor = LRNounExtractor_v2(verbose=True)\nnouns = noun_extractor.train_extract(sents)\n```\n\n\ucd94\ucd9c\ub41c nouns \ub294 {str:namedtuple} \ud615\uc2dd\uc785\ub2c8\ub2e4. \n\n```python\nprint(nouns['\ub274\uc2a4']) # NounScore(frequency=4319, score=1.0)\n```\n\n_compounds_components \uc5d0\ub294 \ubcf5\ud569\uba85\uc0ac\ub97c \uad6c\uc131\ud558\ub294 \ub2e8\uc77c\uba85\uc0ac\ub4e4\uc758 \uc815\ubcf4\uac00 \uc800\uc7a5\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. '\ub300\ud55c\ubbfc\uad6d', '\ub179\uc0c9\uc131\uc7a5'\uacfc \uac19\uc774 \uc2e4\uc81c\ub85c\ub294 \ubcf5\ud569\ud615\ud0dc\uc18c\uc774\uc9c0\ub9cc, \ub2e8\uc77c \uba85\uc0ac\ub85c \uc774\uc6a9\ub418\ub294 \uacbd\uc6b0\ub294 \ub2e8\uc77c \uba85\uc0ac\ub85c \uc778\uc2dd\ud569\ub2c8\ub2e4.\n\n```python\nlist(noun_extractor._compounds_components.items())[:5]\n\n# [('\uc7a0\uc218\ud568\ubc1c\uc0ac\ud0c4\ub3c4\ubbf8\uc0ac\uc77c', ('\uc7a0\uc218\ud568', '\ubc1c\uc0ac', '\ud0c4\ub3c4\ubbf8\uc0ac\uc77c')),\n#  ('\ubbf8\uc0ac\uc77c\ub300\uc751\ub2a5\ub825\uc704\uc6d0\ud68c', ('\ubbf8\uc0ac\uc77c', '\ub300\uc751', '\ub2a5\ub825', '\uc704\uc6d0\ud68c')),\n#  ('\uae00\ub85c\ubc8c\ub179\uc0c9\uc131\uc7a5\uc5f0\uad6c\uc18c', ('\uae00\ub85c\ubc8c', '\ub179\uc0c9\uc131\uc7a5', '\uc5f0\uad6c\uc18c')),\n#  ('\uc2dc\uce74\uace0\uc635\uc158\uac70\ub798\uc18c', ('\uc2dc\uce74\uace0', '\uc635\uc158', '\uac70\ub798\uc18c')),\n#  ('\ub300\ud55c\ubbfc\uad6d\ud2b9\uc218\uc784\ubb34\uc720\uacf5', ('\ub300\ud55c\ubbfc\uad6d', '\ud2b9\uc218', '\uc784\ubb34', '\uc720\uacf5')),\n```\n\nLRGraph \ub294 \ud559\uc2b5\ub41c corpus \uc5d0 \ub4f1\uc7a5\ud55c \uc5b4\uc808\uc758 L-R \uad6c\uc870\ub97c \uc800\uc7a5\ud558\uace0 \uc788\uc2b5\ub2c8\ub2e4. get_r \uacfc get_l \uc744 \uc774\uc6a9\ud558\uc5ec \uc774\ub97c \ud655\uc778\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n```python\nnoun_extractor.lrgraph.get_r('\uc544\uc774\uc624\uc544\uc774')\n\n# [('', 123),\n#  ('\uc758', 47),\n#  ('\ub294', 40),\n#  ('\uc640', 18),\n#  ('\uac00', 18),\n#  ('\uc5d0', 7),\n#  ('\uc5d0\uac8c', 6),\n#  ('\uae4c\uc9c0', 2),\n#  ('\ub791', 2),\n#  ('\ubd80\ud130', 1)]\n```\n\n\ub354 \uc790\uc138\ud55c \uc124\uba85\uc740 [\ud29c\ud1a0\ub9ac\uc5bc 2][nounextractor-v2_usage]\uc5d0 \uc788\uc2b5\ub2c8\ub2e4.\n\n## Word Extraction \n\n2016 \ub144 10\uc6d4\uc758 \uc5f0\uc608\uae30\uc0ac \ub274\uc2a4\uc5d0\ub294 '\ud2b8\uc640\uc774\uc2a4', '\uc544\uc774\uc624\uc544\uc774' \uc640 \uac19\uc740 \ub2e8\uc5b4\uac00 \uc874\uc7ac\ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \ub9d0\ubb49\uce58\ub97c \uae30\ubc18\uc73c\ub85c \ud559\uc2b5\ub41c \ud488\uc0ac \ud310\ubcc4\uae30 / \ud615\ud0dc\uc18c \ubd84\uc11d\uae30\ub294 \uc774\ub7f0 \ub2e8\uc5b4\ub97c \ubcf8 \uc801\uc774 \uc5c6\uc2b5\ub2c8\ub2e4. \ub298 \uc0c8\ub85c\uc6b4 \ub2e8\uc5b4\uac00 \ub9cc\ub4e4\uc5b4\uc9c0\uae30 \ub54c\ubb38\uc5d0 \ud559\uc2b5\ud558\uc9c0 \ubabb\ud55c \ub2e8\uc5b4\ub97c \uc81c\ub300\ub85c \uc778\uc2dd\ud558\uc9c0 \ubabb\ud558\ub294 \ubbf8\ub4f1\ub85d\ub2e8\uc5b4 \ubb38\uc81c (out of vocabulry, OOV) \uac00 \ubc1c\uc0dd\ud569\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc774 \uc2dc\uae30\uc5d0 \uc791\uc131\ub41c \uc5ec\ub7ec \uac1c\uc758 \uc5f0\uc608 \ub274\uc2a4 \uae30\uc0ac\ub97c \uc77d\ub2e4\ubcf4\uba74 '\ud2b8\uc640\uc774\uc2a4', '\uc544\uc774\uc624\uc544\uc774' \uac19\uc740 \ub2e8\uc5b4\uac00 \ub4f1\uc7a5\ud568\uc744 \uc54c \uc218 \uc788\uace0, \uc0ac\ub78c\uc740 \uc774\ub97c \ud559\uc2b5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubb38\uc11c\uc9d1\ud569\uc5d0\uc11c \uc790\uc8fc \ub4f1\uc7a5\ud558\ub294 \uc5f0\uc18d\ub41c \ub2e8\uc5b4\uc5f4\uc744 \ub2e8\uc5b4\ub77c \uc815\uc758\ud55c\ub2e4\uba74, \uc6b0\ub9ac\ub294 \ud1b5\uacc4\ub97c \uc774\uc6a9\ud558\uc5ec \uc774\ub97c \ucd94\ucd9c\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud1b5\uacc4 \uae30\ubc18\uc73c\ub85c \ub2e8\uc5b4(\uc758 \uacbd\uacc4)\ub97c \ud559\uc2b5\ud558\ub294 \ubc29\ubc95\uc740 \ub2e4\uc591\ud569\ub2c8\ub2e4. soynlp\ub294 \uadf8 \uc911, Cohesion score, Branching Entropy, Accessor Variety \ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \n\n```python\nfrom soynlp.word import WordExtractor\n\nword_extractor = WordExtractor(min_frequency=100,\n    min_cohesion_forward=0.05, \n    min_right_branching_entropy=0.0\n)\nword_extractor.train(sentences) # list of str or like\nwords = word_extractor.extract()\n```\n\nwords \ub294 Scores \ub77c\ub294 namedtuple \uc744 value \ub85c \uc9c0\ub2c8\ub294 dict \uc785\ub2c8\ub2e4. \n\n```python\nwords['\uc544\uc774\uc624\uc544\uc774']\n\nScores(cohesion_forward=0.30063636035733476,\n        cohesion_backward=0,\n        left_branching_entropy=0,\n        right_branching_entropy=0,\n        left_accessor_variety=0,\n        right_accessor_variety=0,\n        leftside_frequency=270,\n        rightside_frequency=0\n)\n```\n\n2016-10-26 \uc758 \ub274\uc2a4 \uae30\uc0ac\ub85c\ubd80\ud130 \ud559\uc2b5\ud55c \ub2e8\uc5b4 \uc810\uc218 (cohesion * branching entropy) \uae30\uc900\uc73c\ub85c \uc815\ub82c\ud55c \uc608\uc2dc\uc785\ub2c8\ub2e4. \n\n    \ub2e8\uc5b4   (\ube48\ub3c4\uc218, cohesion, branching entropy)\n\n    \ucd2c\uc601     (2222, 1.000, 1.823)\n    \uc11c\uc6b8     (25507, 0.657, 2.241)\n    \ub4e4\uc5b4     (3906, 0.534, 2.262)\n    \ub86f\ub370     (1973, 0.999, 1.542)\n    \ud55c\uad6d     (9904, 0.286, 2.729)\n    \ubd81\ud55c     (4954, 0.766, 1.729)\n    \ud22c\uc790     (4549, 0.630, 1.889)\n    \ub5a8\uc5b4     (1453, 0.817, 1.515)\n    \uc9c4\ud589     (8123, 0.516, 1.970)\n    \uc598\uae30     (1157, 0.970, 1.328)\n    \uc6b4\uc601     (4537, 0.592, 1.768)\n    \ud504\ub85c\uadf8\ub7a8  (2738, 0.719, 1.527)\n    \ud074\ub9b0\ud134   (2361, 0.751, 1.420)\n    \ub6f0\uc5b4     (927, 0.831, 1.298)\n    \ub4dc\ub77c\ub9c8   (2375, 0.609, 1.606)\n    \uc6b0\ub9ac     (7458, 0.470, 1.827)\n    \uc900\ube44     (1736, 0.639, 1.513)\n    \ub8e8\uc774     (1284, 0.743, 1.354)\n    \ud2b8\ub7fc\ud504   (3565, 0.712, 1.355)\n    \uc0dd\uac01     (3963, 0.335, 2.024)\n    \ud32c\ub4e4     (999, 0.626, 1.341)\n    \uc0b0\uc5c5     (2203, 0.403, 1.769)\n    10      (18164, 0.256, 2.210)\n    \ud655\uc778     (3575, 0.306, 2.016)\n    \ud544\uc694     (3428, 0.635, 1.279)\n    \ubb38\uc81c     (4737, 0.364, 1.808)\n    \ud610\uc758     (2357, 0.962, 0.830)\n    \ud3c9\uac00     (2749, 0.362, 1.787)\n    20      (59317, 0.667, 1.171)\n    \uc2a4\ud3ec\uce20    (3422, 0.428, 1.604)\n\n\uc790\uc138\ud55c \ub0b4\uc6a9\uc740 [word extraction tutorial][wordextraction_lecture] \uc5d0 \uc788\uc2b5\ub2c8\ub2e4. \n\ud604\uc7ac \ubc84\uc804\uc5d0\uc11c \uc81c\uacf5\ud558\ub294 \uae30\ub2a5\uc740 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4. \n\n## Tokenizer\n\nWordExtractor \ub85c\ubd80\ud130 \ub2e8\uc5b4 \uc810\uc218\ub97c \ud559\uc2b5\ud558\uc600\ub2e4\uba74, \uc774\ub97c \uc774\uc6a9\ud558\uc5ec \ub2e8\uc5b4\uc758 \uacbd\uacc4\ub97c \ub530\ub77c \ubb38\uc7a5\uc744 \ub2e8\uc5b4\uc5f4\ub85c \ubd84\ud574\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. soynlp \ub294 \uc138 \uac00\uc9c0 \ud1a0\ud06c\ub098\uc774\uc800\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \ub744\uc5b4\uc4f0\uae30\uac00 \uc798 \ub418\uc5b4 \uc788\ub2e4\uba74 LTokenizer \ub97c \uc774\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud55c\uad6d\uc5b4 \uc5b4\uc808\uc758 \uad6c\uc870\ub97c \"\uba85\uc0ac + \uc870\uc0ac\" \ucc98\ub7fc \"L + [R]\" \ub85c \uc0dd\uac01\ud569\ub2c8\ub2e4. \n\n### LTokenizer\n\nL parts \uc5d0\ub294 \uba85\uc0ac/\ub3d9\uc0ac/\ud615\uc6a9\uc0ac/\ubd80\uc0ac\uac00 \uc704\uce58\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5b4\uc808\uc5d0\uc11c L \ub9cc \uc798 \uc778\uc2dd\ud55c\ub2e4\uba74 \ub098\uba38\uc9c0 \ubd80\ubd84\uc774 R parts \uac00 \ub429\ub2c8\ub2e4. LTokenizer \uc5d0\ub294 L parts \uc758 \ub2e8\uc5b4 \uc810\uc218\ub97c \uc785\ub825\ud569\ub2c8\ub2e4. \n\n```python\nfrom soynlp.tokenizer import LTokenizer\n\nscores = {'\ub370\uc774':0.5, '\ub370\uc774\ud130':0.5, '\ub370\uc774\ud130\ub9c8\uc774\ub2dd':0.5, '\uacf5\ubd80':0.5, '\uacf5\ubd80\uc911':0.45}\ntokenizer = LTokenizer(scores=scores)\n\nsent = '\ub370\uc774\ud130\ub9c8\uc774\ub2dd\uc744 \uacf5\ubd80\ud55c\ub2e4'\n\nprint(tokenizer.tokenize(sent, flatten=False))\n#[['\ub370\uc774\ud130\ub9c8\uc774\ub2dd', '\uc744'], ['\uacf5\ubd80', '\uc911\uc774\ub2e4']]\n\nprint(tokenizer.tokenize(sent))\n# ['\ub370\uc774\ud130\ub9c8\uc774\ub2dd', '\uc744', '\uacf5\ubd80', '\uc911\uc774\ub2e4']\n```\n\n\ub9cc\uc57d WordExtractor \ub97c \uc774\uc6a9\ud558\uc5ec \ub2e8\uc5b4 \uc810\uc218\ub97c \uacc4\uc0b0\ud558\uc600\ub2e4\uba74, \ub2e8\uc5b4 \uc810\uc218 \uc911 \ud558\ub098\ub97c \ud0dd\ud558\uc5ec scores \ub97c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 Forward cohesion \uc758 \uc810\uc218\ub9cc\uc744 \uc774\uc6a9\ud558\ub294 \uacbd\uc6b0\uc785\ub2c8\ub2e4. \uadf8 \uc678\uc5d0\ub3c4 \ub2e4\uc591\ud558\uac8c \ub2e8\uc5b4 \uc810\uc218\ub97c \uc815\uc758\ud558\uc5ec \uc774\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n```python\nfrom soynlp.word import WordExtractor\nfrom soynlp.utils import DoublespaceLineCorpus\n\nfile_path = 'your file path'\ncorpus = DoublespaceLineCorpus(file_path, iter_sent=True)\n\nword_extractor = WordExtractor(\n    min_frequency=100, # example\n    min_cohesion_forward=0.05,\n    min_right_branching_entropy=0.0\n)\n\nword_extractor.train(sentences)\nwords = word_extractor.extract()\n\ncohesion_score = {word:score.cohesion_forward for word, score in words.items()}\ntokenizer = LTokenizer(scores=cohesion_score)\n```\n\n\uba85\uc0ac \ucd94\ucd9c\uae30\uc758 \uba85\uc0ac \uc810\uc218\uc640 Cohesion \uc744 \ud568\uaed8 \uc774\uc6a9\ud560 \uc218\ub3c4 \uc788\uc2b5\ub2c8\ub2e4. \ud55c \uc608\ub85c, \"Cohesion \uc810\uc218 + \uba85\uc0ac \uc810\uc218\"\ub97c \ub2e8\uc5b4 \uc810\uc218\ub85c \uc774\uc6a9\ud558\ub824\uba74 \uc544\ub798\ucc98\ub7fc \uc791\uc5c5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.\n\n```python\nfrom soynlp.noun import LRNounExtractor_2\nnoun_extractor = LRNounExtractor_v2()\nnouns = noun_extractor.train_extract(corpus) # list of str like\n\nnoun_scores = {noun:score.score for noun, score in nouns.items()}\ncombined_scores = {noun:score + cohesion_score.get(noun, 0)\n    for noun, score in noun_scores.items()}\ncombined_scores = combined_scores.update(\n    {subword:cohesion for subword, cohesion in cohesion_score.items()\n    if not (subword in combine_scores)}\n)\n\ntokenizer = LTokenizer(scores=combined_scores)\n```\n\n### MaxScoreTokenizer\n\n\ub744\uc5b4\uc4f0\uae30\uac00 \uc81c\ub300\ub85c \uc9c0\ucf1c\uc9c0\uc9c0 \uc54a\uc740 \ub370\uc774\ud130\ub77c\uba74, \ubb38\uc7a5\uc758 \ub744\uc5b4\uc4f0\uae30 \uae30\uc900\uc73c\ub85c \ub098\ub258\uc5b4\uc9c4 \ub2e8\uc704\uac00 L + [R] \uad6c\uc870\ub77c \uac00\uc815\ud560 \uc218 \uc5c6\uc2b5\ub2c8\ub2e4. \ud558\uc9c0\ub9cc \uc0ac\ub78c\uc740 \ub744\uc5b4\uc4f0\uae30\uac00 \uc9c0\ucf1c\uc9c0\uc9c0 \uc54a\uc740 \ubb38\uc7a5\uc5d0\uc11c \uc775\uc219\ud55c \ub2e8\uc5b4\ubd80\ud130 \ub208\uc5d0 \ub4e4\uc5b4\uc635\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc744 \ubaa8\ub378\ub85c \uc62e\uae34 MaxScoreTokenizer \uc5ed\uc2dc \ub2e8\uc5b4 \uc810\uc218\ub97c \uc774\uc6a9\ud569\ub2c8\ub2e4. \n\n```python\nfrom soynlp.tokenizer import MaxScoreTokenizer\n\nscores = {'\ud30c\uc2a4': 0.3, '\ud30c\uc2a4\ud0c0': 0.7, '\uc88b\uc544\uc694': 0.2, '\uc88b\uc544':0.5}\ntokenizer = MaxScoreTokenizer(scores=scores)\n\nprint(tokenizer.tokenize('\ub09c\ud30c\uc2a4\ud0c0\uac00\uc88b\uc544\uc694'))\n# ['\ub09c', '\ud30c\uc2a4\ud0c0', '\uac00', '\uc88b\uc544', '\uc694']\n\nprint(tokenizer.tokenize('\ub09c\ud30c\uc2a4\ud0c0\uac00 \uc88b\uc544\uc694'), flatten=False)\n# [[('\ub09c', 0, 1, 0.0, 1), ('\ud30c\uc2a4\ud0c0', 1, 4, 0.7, 3),  ('\uac00', 4, 5, 0.0, 1)],\n#  [('\uc88b\uc544', 0, 2, 0.5, 2), ('\uc694', 2, 3, 0.0, 1)]]\n```\n\nMaxScoreTokenizer \uc5ed\uc2dc WordExtractor \uc758 \uacb0\uacfc\ub97c \uc774\uc6a9\ud558\uc2e4 \ub54c\uc5d0\ub294 \uc704\uc758 \uc608\uc2dc\ucc98\ub7fc \uc801\uc808\ud788 scores \ub97c \ub9cc\ub4e4\uc5b4 \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774\ubbf8 \uc54c\ub824\uc9c4 \ub2e8\uc5b4 \uc0ac\uc804\uc774 \uc788\ub2e4\uba74 \uc774 \ub2e8\uc5b4\ub4e4\uc740 \ub2e4\ub978 \uc5b4\ub5a4 \ub2e8\uc5b4\ubcf4\ub2e4\ub3c4 \ub354 \ud070 \uc810\uc218\ub97c \ubd80\uc5ec\ud558\uba74 \uadf8 \ub2e8\uc5b4\ub294 \ud1a0\ud06c\ub098\uc774\uc800\uac00 \ud558\ub098\uc758 \ub2e8\uc5b4\ub85c \uc798\ub77c\ub0c5\ub2c8\ub2e4. \n\n### RegexTokenizer\n\n\uaddc\uce59 \uae30\ubc18\uc73c\ub85c\ub3c4 \ub2e8\uc5b4\uc5f4\uc744 \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc5b8\uc5b4\uac00 \ubc14\ub00c\ub294 \ubd80\ubd84\uc5d0\uc11c \uc6b0\ub9ac\ub294 \ub2e8\uc5b4\uc758 \uacbd\uacc4\ub97c \uc778\uc2dd\ud569\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4 \"\uc544\uc774\uace0\u314b\u314b\u315c\u315c\uc9c4\uc9dc?\" \ub294 [\uc544\uc774\uace0, \u314b\u314b, \u315c\u315c, \uc9c4\uc9dc, ?]\ub85c \uc27d\uac8c \ub2e8\uc5b4\uc5f4\uc744 \ub098\ub215\ub2c8\ub2e4. \n\n```python\nfrom soynlp.tokenizer import RegexTokenizer\n\ntokenizer = RegexTokenizer()\n\nprint(tokenizer.tokenize('\uc774\ub807\uac8c\uc5f0\uc18d\ub41c\ubb38\uc7a5\uc740\uc798\ub9ac\uc9c0\uc54a\uc2b5\ub2c8\ub2e4\ub9cc'))\n# ['\uc774\ub807\uac8c\uc5f0\uc18d\ub41c\ubb38\uc7a5\uc740\uc798\ub9ac\uc9c0\uc54a\uc2b5\ub2c8\ub2e4\ub9cc']\n\nprint(tokenizer.tokenize('\uc22b\uc790123\uc774\uc601\uc5b4abc\uc5d0\uc11e\uc5ec\uc788\uc73c\uba74\u314b\u314b\uc798\ub9ac\uaca0\uc8e0'))\n# ['\uc22b\uc790', '123', '\uc774\uc601\uc5b4', 'abc', '\uc5d0\uc11e\uc5ec\uc788\uc73c\uba74', '\u314b\u314b', '\uc798\ub9ac\uaca0\uc8e0']\n```\n\n## Part of Speech Tagger\n\n\ub2e8\uc5b4 \uc0ac\uc804\uc774 \uc798 \uad6c\ucd95\ub418\uc5b4 \uc788\ub2e4\uba74, \uc774\ub97c \uc774\uc6a9\ud558\uc5ec \uc0ac\uc804 \uae30\ubc18 \ud488\uc0ac \ud310\ubcc4\uae30\ub97c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e8, \ud615\ud0dc\uc18c\ubd84\uc11d\uc744 \ud558\ub294 \uac83\uc774 \uc544\ub2c8\uae30 \ub54c\ubb38\uc5d0 '\ud558\ub294', '\ud558\ub2e4', '\ud558\uace0'\ub294 \ubaa8\ub450 \ub3d9\uc0ac\uc5d0 \ud574\ub2f9\ud569\ub2c8\ub2e4. Lemmatizer \ub294 \ud604\uc7ac \uac1c\ubc1c/\uc815\ub9ac \uc911\uc785\ub2c8\ub2e4. \n\n```python\npos_dict = {\n    'Adverb': {'\ub108\ubb34', '\ub9e4\uc6b0'}, \n    'Noun': {'\ub108\ubb34\ub108\ubb34\ub108\ubb34', '\uc544\uc774\uc624\uc544\uc774', '\uc544\uc774', '\ub178\ub798', '\uc624', '\uc774', '\uace0\uc591'},\n    'Josa': {'\ub294', '\uc758', '\uc774\ub2e4', '\uc785\ub2c8\ub2e4', '\uc774', '\uc774\ub294', '\ub97c', '\ub77c', '\ub77c\ub294'},\n    'Verb': {'\ud558\ub294', '\ud558\ub2e4', '\ud558\uace0'},\n    'Adjective': {'\uc608\uc05c', '\uc608\uc058\ub2e4'},\n    'Exclamation': {'\uc6b0\uc640'}    \n}\n\nfrom soynlp.postagger import Dictionary\nfrom soynlp.postagger import LRTemplateMatcher\nfrom soynlp.postagger import LREvaluator\nfrom soynlp.postagger import SimpleTagger\nfrom soynlp.postagger import UnknowLRPostprocessor\n\ndictionary = Dictionary(pos_dict)\ngenerator = LRTemplateMatcher(dictionary)    \nevaluator = LREvaluator()\npostprocessor = UnknowLRPostprocessor()\ntagger = SimpleTagger(generator, evaluator, postprocessor)\n\nsent = '\ub108\ubb34\ub108\ubb34\ub108\ubb34\ub294\uc544\uc774\uc624\uc544\uc774\uc758\ub178\ub798\uc785\ub2c8\ub2e4!!'\nprint(tagger.tag(sent))\n# [('\ub108\ubb34\ub108\ubb34\ub108\ubb34', 'Noun'),\n#  ('\ub294', 'Josa'),\n#  ('\uc544\uc774\uc624\uc544\uc774', 'Noun'),\n#  ('\uc758', 'Josa'),\n#  ('\ub178\ub798', 'Noun'),\n#  ('\uc785\ub2c8\ub2e4', 'Josa'),\n#  ('!!', None)]\n```\n\n\ub354 \uc790\uc138\ud55c \uc0ac\uc6a9\ubc95\uc740 [\uc0ac\uc6a9\ubc95 \ud29c\ud1a0\ub9ac\uc5bc][tagger_usage] \uc5d0 \uae30\uc220\ub418\uc5b4 \uc788\uc73c\uba70, [\uac1c\ubc1c\uacfc\uc815 \ub178\ud2b8][tagger_lecture]\ub294 \uc5ec\uae30\uc5d0 \uae30\uc220\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \n\n## Vetorizer\n\n\ud1a0\ud06c\ub098\uc774\uc800\ub97c \ud559\uc2b5\ud558\uac70\ub098, \ud639\uc740 \ud559\uc2b5\ub41c \ud1a0\ud06c\ub098\uc774\uc800\ub97c \uc774\uc6a9\ud558\uc5ec \ubb38\uc11c\ub97c sparse matrix \ub85c \ub9cc\ub4ed\ub2c8\ub2e4. minimum / maximum of term frequency / document frequency \ub97c \uc870\uc808\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. Verbose mode \uc5d0\uc11c\ub294 \ud604\uc7ac\uc758 \ubca1\ud130\ub77c\uc774\uc9d5 \uc0c1\ud669\uc744 print \ud569\ub2c8\ub2e4. \n\n```python\nvectorizer = BaseVectorizer(\n    tokenizer=tokenizer,\n    min_tf=0,\n    max_tf=10000,\n    min_df=0,\n    max_df=1.0,\n    stopwords=None,\n    lowercase=True,\n    verbose=True\n)\n\ncorpus.iter_sent = False\nx = vectorizer.fit_transform(corpus)\n```\n\n\ubb38\uc11c\uc758 \ud06c\uae30\uac00 \ud06c\uac70\ub098, \uace7\ubc14\ub85c sparse matrix \ub97c \uc774\uc6a9\ud560 \uac83\uc774 \uc544\ub2c8\ub77c\uba74 \uc774\ub97c \uba54\ubaa8\ub9ac\uc5d0 \uc62c\ub9ac\uc9c0 \uc54a\uace0 \uadf8\ub300\ub85c \ud30c\uc77c\ub85c \uc800\uc7a5\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. fit_to_file() \ud639\uc740 to_file() \ud568\uc218\ub294 \ud558\ub098\uc758 \ubb38\uc11c\uc5d0 \ub300\ud55c term frequency vector \ub97c \uc5bb\ub294\ub300\ub85c \ud30c\uc77c\uc5d0 \uae30\ub85d\ud569\ub2c8\ub2e4. BaseVectorizer \uc5d0\uc11c \uc774\uc6a9\ud560 \uc218 \uc788\ub294 parameters \ub294 \ub3d9\uc77c\ud569\ub2c8\ub2e4.\n\n```python\nvectorizer = BaseVectorizer(min_tf=1, tokenizer=tokenizer)\ncorpus.iter_sent = False\n\nmatrix_path = 'YOURS'\nvectorizer.fit_to_file(corpus, matrix_path)\n```\n\n\ud558\ub098\uc758 \ubb38\uc11c\ub97c sparse matrix \uac00 \uc544\ub2cc list of int \ub85c \ucd9c\ub825\uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4. \uc774 \ub54c vectorizer.vocabulary_ \uc5d0 \ud559\uc2b5\ub418\uc9c0 \uc54a\uc740 \ub2e8\uc5b4\ub294 encoding \uc774 \ub418\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.\n\n```python\nvectorizer.encode_a_doc_to_bow('\uc624\ub298 \ub274\uc2a4\ub294 \uc774\uac83\uc774 \uc804\ubd80\ub2e4')\n# {3: 1, 258: 1, 428: 1, 1814: 1}\n```\n\nlist of int \ub294 list of str \ub85c decoding \uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.\n\n```python\nvectorizer.decode_from_bow({3: 1, 258: 1, 428: 1, 1814: 1})\n# {'\ub274\uc2a4': 1, '\ub294': 1, '\uc624\ub298': 1, '\uc774\uac83\uc774': 1}\n```\n\ndict \ud615\uc2dd\uc758 bag of words \ub85c\ub3c4 encoding \uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4. \n\n```python\nvectorizer.encode_a_doc_to_list('\uc624\ub298\uc758 \ub274\uc2a4\ub294 \ub9e4\uc6b0 \uc2ec\uac01\ud569\ub2c8\ub2e4')\n# [258, 4, 428, 3, 333]\n```\n\ndict \ud615\uc2dd\uc758 bag of words \ub294 decoding \uc774 \uac00\ub2a5\ud569\ub2c8\ub2e4.\n\n```python\nvectorizer.decode_from_list([258, 4, 428, 3, 333])\n['\uc624\ub298', '\uc758', '\ub274\uc2a4', '\ub294', '\ub9e4\uc6b0']\n```\n\n## Normalizer\n\n\ub300\ud654 \ub370\uc774\ud130, \ub313\uae00 \ub370\uc774\ud130\uc5d0 \ub4f1\uc7a5\ud558\ub294 \ubc18\ubcf5\ub418\ub294 \uc774\ubaa8\ud2f0\ucf58\uc758 \uc815\ub9ac \ubc0f \ud55c\uae00, \ud639\uc740 \ud14d\uc2a4\ud2b8\ub9cc \ub0a8\uae30\uae30 \uc704\ud55c \ud568\uc218\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. \n\n```python\nfrom soynlp.normalizer import *\n\nemoticon_normalize('\u314b\u314b\u314b\u314b\u314b\u314b\u314b\u314b\u314b\u314b\u314b\u314b\u314b\ucfe0\u315c\u315c\u315c\u315c\u315c\u315c', num_repeats=3)\n# '\u314b\u314b\u314b\u315c\u315c\u315c'\n\nrepeat_normalize('\uc640\ud558\ud558\ud558\ud558\ud558\ud558\ud558\ud558\ud558\ud56b', num_repeats=2)\n# '\uc640\ud558\ud558\ud56b'\n\nonly_hangle('\uac00\ub098\ub2e4\u314f\u3151\u3153\u314b\u314b\ucfe0\u315c\u315c\u315cabcd123!!\uc544\ud56b')\n# '\uac00\ub098\ub2e4\u314f\u3151\u3153\u314b\u314b\ucfe0\u315c\u315c\u315c \uc544\ud56b'\n\nonly_hangle_number('\uac00\ub098\ub2e4\u314f\u3151\u3153\u314b\u314b\ucfe0\u315c\u315c\u315cabcd123!!\uc544\ud56b')\n# '\uac00\ub098\ub2e4\u314f\u3151\u3153\u314b\u314b\ucfe0\u315c\u315c\u315c 123 \uc544\ud56b'\n\nonly_text('\uac00\ub098\ub2e4\u314f\u3151\u3153\u314b\u314b\ucfe0\u315c\u315c\u315cabcd123!!\uc544\ud56b')\n# '\uac00\ub098\ub2e4\u314f\u3151\u3153\u314b\u314b\ucfe0\u315c\u315c\u315cabcd123!!\uc544\ud56b'\n```\n\n\ub354 \uc790\uc138\ud55c \uc124\uba85\uc740 [\ud29c\ud1a0\ub9ac\uc5bc][normalizer_tutorial]\uc5d0 \uc788\uc2b5\ub2c8\ub2e4.\n\n## Point-wise Mutual Information (PMI)\n\n\uc5f0\uad00\uc5b4 \ubd84\uc11d\uc744 \uc704\ud55c co-occurrence matrix \uacc4\uc0b0\uacfc \uc774\ub97c \uc774\uc6a9\ud55c Point-wise Mutual Information (PMI) \uacc4\uc0b0\uc744 \uc704\ud55c \ud568\uc218\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\n\uc544\ub798 sent_to_word_contexts_matrix \ud568\uc218\ub97c \uc774\uc6a9\ud558\uc5ec (word, context words) matrix \ub97c \ub9cc\ub4e4 \uc218 \uc788\uc2b5\ub2c8\ub2e4. x \ub294 scipy.sparse.csr_matrix \uc774\uba70, (n_vocabs, n_vocabs) \ud06c\uae30\uc785\ub2c8\ub2e4. idx2vocab \uc740 x \uc758 \uac01 row, column \uc5d0 \ud574\ub2f9\ud558\ub294 \ub2e8\uc5b4\uac00 \ud3ec\ud568\ub41c list of str \uc785\ub2c8\ub2e4. \ubb38\uc7a5\uc758 \uc55e/\ub4a4 windows \ub2e8\uc5b4\ub97c context \ub85c \uc778\uc2dd\ud558\uba70, min_tf \uc774\uc0c1\uc758 \ube48\ub3c4\uc218\ub85c \ub4f1\uc7a5\ud55c \ub2e8\uc5b4\uc5d0 \ub300\ud574\uc11c\ub9cc \uacc4\uc0b0\uc744 \ud569\ub2c8\ub2e4. dynamic_weight \ub294 context \uae38\uc774\uc5d0 \ubc18\ube44\ub840\ud558\uc5ec weighting \uc744 \ud569\ub2c8\ub2e4. windows \uac00 3 \uc77c \uacbd\uc6b0, 1, 2, 3 \uce78 \ub5a8\uc5b4\uc9c4 \ub2e8\uc5b4\uc758 co-occurrence \ub294 1, 2/3, 1/3 \uc73c\ub85c \uacc4\uc0b0\ub429\ub2c8\ub2e4.\n\n```python\nfrom soynlp.vectorizer import sent_to_word_contexts_matrix\n\nx, idx2vocab = sent_to_word_contexts_matrix(\n    corpus,\n    windows=3,\n    min_tf=10,\n    tokenizer=tokenizer, # (default) lambda x:x.split(),\n    dynamic_weight=False,\n    verbose=True\n)\n```\n\nCo-occurrence matrix \uc778 x \ub97c pmi \uc5d0 \uc785\ub825\ud558\uba74 row \uc640 column \uc744 \uac01 \ucd95\uc73c\ub85c PMI \uac00 \uacc4\uc0b0\ub429\ub2c8\ub2e4. pmi_dok \uc740 scipy.sparse.dok_matrix \ud615\uc2dd\uc785\ub2c8\ub2e4. min_pmi \uc774\uc0c1\uc758 \uac12\ub9cc \uc800\uc7a5\ub418\uba70, default \ub294 min_pmi = 0 \uc774\uae30 \ub54c\ubb38\uc5d0 Positive PMI (PPMI) \uc785\ub2c8\ub2e4. alpha \ub294 PMI(x,y) = p(x,y) / ( p(x) * ( p(y) + alpha ) ) \uc5d0 \uc785\ub825\ub418\ub294 smoothing parameter \uc785\ub2c8\ub2e4. \uacc4\uc0b0 \uacfc\uc815\uc774 \uc624\ub798 \uac78\ub9ac\uae30 \ub54c\ubb38\uc5d0 verbose = True \ub85c \uc124\uc815\ud558\uba74 \ud604\uc7ac\uc758 \uc9c4\ud589 \uc0c1\ud669\uc744 \ucd9c\ub825\ud569\ub2c8\ub2e4.\n\n```python\nfrom soynlp.word import pmi\n\npmi_dok = pmi(\n    x,\n    min_pmi=0,\n    alpha=0.0001,\n    verbose=True\n)\n```\n\n\ub354 \uc790\uc138\ud55c \uc124\uba85\uc740 [\ud29c\ud1a0\ub9ac\uc5bc][pmi_tutorial]\uc5d0 \uc788\uc2b5\ub2c8\ub2e4.\n\n## notes\n\n### Slides\n\n- [slide files][unkornlp_pdf]\uc5d0 \uc54c\uace0\ub9ac\uc998\ub4e4\uc758 \uc6d0\ub9ac \ubc0f \uc124\uba85\uc744 \uc801\uc5b4\ub480\uc2b5\ub2c8\ub2e4. \ub370\uc774\ud130\uc57c\ub180\uc790\uc5d0\uc11c \ubc1c\ud45c\ud588\ub358 \uc790\ub8cc\uc785\ub2c8\ub2e4.\n- [textmining tutorial][textmining-tutorial] \uc744 \ub9cc\ub4e4\uace0 \uc788\uc2b5\ub2c8\ub2e4. soynlp project \uc5d0\uc11c \uad6c\ud604 \uc911\uc778 \uc54c\uace0\ub9ac\uc998\ub4e4\uc758 \uc124\uba85 \ubc0f \ud14d\uc2a4\ud2b8 \ub9c8\uc774\ub2dd\uc5d0 \uc774\uc6a9\ub418\ub294 \uba38\uc2e0 \ub7ec\ub2dd \ubc29\ubc95\ub4e4\uc744 \uc124\uba85\ud558\ub294 slides \uc785\ub2c8\ub2e4. \n\n### Blogs\n\n- [github io blog][lovitio] \uc5d0\uc11c [slides][textmining-tutorial] \uc5d0 \uc788\ub294 \ub0b4\uc6a9\ub4e4\uc758 \ud14d\uc2a4\ud2b8 \uc124\uba85 \uae00\ub4e4\uc744 \uc62c\ub9ac\uace0 \uc788\uc2b5\ub2c8\ub2e4. Slides \uc758 \ub0b4\uc6a9\uc5d0 \ub300\ud574 \ub354 \uc790\uc138\ud558\uac8c \ubcf4\uace0 \uc2f6\uc73c\uc2e4 \ub54c \uc77d\uc73c\uc2dc\uae38 \uad8c\ud569\ub2c8\ub2e4. \n\n\n## \ud568\uaed8 \uc774\uc6a9\ud558\uba74 \uc88b\uc740 \ub77c\uc774\ube0c\ub7ec\ub9ac\ub4e4\n\n### \uc138\uc885 \ub9d0\ubb49\uce58 \uc815\uc81c\ub97c \uc704\ud55c utils\n\n\uc790\uc5f0\uc5b4\ucc98\ub9ac \ubaa8\ub378 \ud559\uc2b5\uc744 \uc704\ud558\uc5ec \uc138\uc885 \ub9d0\ubb49\uce58 \ub370\uc774\ud130\ub97c \uc815\uc81c\ud558\uae30 \uc704\ud55c \ud568\uc218\ub4e4\uc744 \uc81c\uacf5\ud569\ub2c8\ub2e4. \ud615\ud0dc\uc18c/\ud488\uc0ac \ud615\ud0dc\ub85c \uc815\uc81c\ub41c \ud559\uc2b5\uc6a9 \ub370\uc774\ud130\ub97c \ub9cc\ub4dc\ub294 \ud568\uc218, \uc6a9\uc5b8\uc758 \ud65c\uc6a9 \ud615\ud0dc\ub97c \uc815\ub9ac\ud558\uc5ec \ud14c\uc774\ube14\ub85c \ub9cc\ub4dc\ub294 \ud568\uc218, \uc138\uc885 \ub9d0\ubb49\uce58\uc758 \ud488\uc0ac \uccb4\uacc4\ub97c \ub2e8\uc21c\ud654 \uc2dc\ud0a4\ub294 \ud568\uc218\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4.\n\n- https://github.com/lovit/sejong_corpus_cleaner\n\n### soyspacing\n\n\ub744\uc5b4\uc4f0\uae30 \uc624\ub958\uac00 \uc788\uc744 \uacbd\uc6b0 \uc774\ub97c \uc81c\uac70\ud558\uba74 \ud14d\uc2a4\ud2b8 \ubd84\uc11d\uc774 \uc26c\uc6cc\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ubd84\uc11d\ud558\ub824\ub294 \ub370\uc774\ud130\ub97c \uae30\ubc18\uc73c\ub85c \ub744\uc5b4\uc4f0\uae30 \uc5d4\uc9c4\uc744 \ud559\uc2b5\ud558\uace0, \uc774\ub97c \uc774\uc6a9\ud558\uc5ec \ub744\uc5b4\uc4f0\uae30 \uc624\ub958\ub97c \uad50\uc815\ud569\ub2c8\ub2e4. \n\n- https://github.com/lovit/soyspacing\n- pip install soyspacing\n\n### KR-WordRank\n\n\ud1a0\ud06c\ub098\uc774\uc800\ub098 \ub2e8\uc5b4 \ucd94\ucd9c\uae30\ub97c \ud559\uc2b5\ud560 \ud544\uc694\uc5c6\uc774, HITS algorithm \uc744 \uc774\uc6a9\ud558\uc5ec substring graph \uc5d0\uc11c \ud0a4\uc6cc\ub4dc\ub97c \ucd94\ucd9c\ud569\ub2c8\ub2e4. \n\n- https://github.com/lovit/KR-WordRank\n- pip install krwordrank\n\n### soykeyword\n\n\ud0a4\uc6cc\ub4dc \ucd94\ucd9c\uae30\uc785\ub2c8\ub2e4. Logistic Regression \uc744 \uc774\uc6a9\ud558\ub294 \ubaa8\ub378\uacfc \ud1b5\uacc4 \uae30\ubc18 \ubaa8\ub378, \ub450 \uc885\ub958\uc758 \ud0a4\uc6cc\ub4dc \ucd94\ucd9c\uae30\ub97c \uc81c\uacf5\ud569\ub2c8\ub2e4. scipy.sparse \uc758 sparse matrix \ud615\uc2dd\uacfc \ud14d\uc2a4\ud2b8 \ud30c\uc77c \ud615\uc2dd\uc744 \uc9c0\uc6d0\ud569\ub2c8\ub2e4. \n\n- https://github.com/lovit/soykeyword\n- pip install soykeyword\n\n[![Analytics](https://ga-beacon.appspot.com/UA-129549627-2/soynlp/readme)](https://github.com/lovit/soynlp)\n\n[wordextraction_lecture]: https://github.com/lovit/soynlp/blob/master/tutorials/wordextractor_lecture.ipynb\n[nounextractor-v1_usage]: https://github.com/lovit/soynlp/blob/master/tutorials/nounextractor-v1_usage.ipynb\n[nounextractor-v2_usage]: https://github.com/lovit/soynlp/blob/master/tutorials/nounextractor-v2_usage.ipynb\n[tagger_usage]: https://github.com/lovit/soynlp/blob/master/tutorials/tagger_usage.ipynb\n[tagger_lecture]: https://github.com/lovit/soynlp/blob/master/tutorials/tagger_lecture.ipynb\n[normalizer_tutorial]: https://github.com/lovit/soynlp/blob/master/tutorials/normalizer_usage.ipynb\n[pmi_tutorial]: https://github.com/lovit/soynlp/blob/master/tutorials/pmi_usage.ipynb\n[unkornlp_pdf]: https://github.com/lovit/soynlp/blob/master/notes/unskonlp.pdf\n[textmining-tutorial]: https://github.com/lovit/textmining-tutorial\n[lovitio]: https://lovit.github.io/\n\n\n",
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