Merge branch 'master' of ssh://the-cake-is-a-lie.net:20022/jonas/NLP-LAB
This commit is contained in:
		| @ -4,12 +4,18 @@ | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "# Exercise 1" | ||||
|     "# Exercise 1\n", | ||||
|     "Solution by:\n", | ||||
|     "Carsten Draschner \n", | ||||
|     "2719095\n", | ||||
|     "\n", | ||||
|     "Following Instructions: \n", | ||||
|     "https://github.com/SmartDataAnalytics/MA-INF-4222-NLP-Lab/blob/master/2018_SoSe/exercises/Task01_Instructions.ipynb" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 23, | ||||
|    "execution_count": 83, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
| @ -25,12 +31,12 @@ | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "## Classifiers\n", | ||||
|     "note: for model1 and model3 you can try different classifiers: Hidden Markov Model, Logistic Regression, Maximum Entropy Markov Models, Decision Trees, Naive Bayes, etc.. __choose one!__" | ||||
|     "**Decision Tree** import from skikit learn" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 24, | ||||
|    "execution_count": 84, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
| @ -45,19 +51,20 @@ | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "### 1. model1 = your POS tagger model (english)" | ||||
|     "### 1. model1 = your POS tagger model (english)\n", | ||||
|     "for a words defined by its in dex with the given sentences a feature vector fot this word will be determinded" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 25, | ||||
|    "execution_count": 85, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "{'word': 'bims', 'length': 4, 'is_capitalized': False, 'prefix-1': 'b', 'suffix-1': 's', 'prev_word': 'i', 'next_word': 'der', 'kindOfCamelCase': False, 'includesSpace': False}\n" | ||||
|       "defined own feature model\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
| @ -72,10 +79,13 @@ | ||||
|     "        'prev_word': '' if index == 0 else sentence[index - 1],\n", | ||||
|     "        'next_word': '' if index == len(sentence) - 1 else sentence[index + 1],\n", | ||||
|     "        'kindOfCamelCase': sentence[index][1:].lower() != sentence[index][1:],\n", | ||||
|     "        'includesSpace': True if ((' ') in sentence[index]) else False #depemds on tokenizer\n", | ||||
|     "        'includesSpace': True if ((' ') in sentence[index]) else False, #depemds on tokenizer\n", | ||||
|     "        'containsNumber': sum(str(i)  in (sentence[index]) for i in range(10))>0,\n", | ||||
|     "        'prefix-2': sentence[index][1] if len(sentence[index])>1 else \"-1\",\n", | ||||
|     "        'suffix-2': sentence[index][-2] if len(sentence[index])>1 else \"-1\"\n", | ||||
|     "    }\n", | ||||
|     "\n", | ||||
|     "print(features(\"halli hallo i bims der Programmierer\".strip().split(\" \"), 3))" | ||||
|     "print(\"defined own feature model\")\n", | ||||
|     "#print(features(\"halli hallo i bims der Programmierer\".strip().split(\" \"), 3))" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
| @ -87,7 +97,7 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 26, | ||||
|    "execution_count": 86, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
| @ -112,13 +122,24 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": null, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "execution_count": 87, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "data": { | ||||
|       "text/plain": [ | ||||
|        "\"#used from description for RegexpTagger\\npatterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*'s$', 'NN$'),               \\n             (r'.*s$', 'NNS'), (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')]\\n\\n#train taggers\\ndef_model = nltk.DefaultTagger('NN')\\nregexp_model = nltk.RegexpTagger(patterns)\\nuni_model = nltk.UnigramTagger(training_sentences_X1)\\nbi_model = nltk.BigramTagger(training_sentences_X1)\\ntri_model = nltk.TrigramTagger(training_sentences_X1)\"" | ||||
|       ] | ||||
|      }, | ||||
|      "execution_count": 87, | ||||
|      "metadata": {}, | ||||
|      "output_type": "execute_result" | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "#used from description for RegexpTagger\n", | ||||
|     "#see Task 1.3 and 1.6\n", | ||||
|     "\n", | ||||
|     "'''#used from description for RegexpTagger\n", | ||||
|     "patterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*\\'s$', 'NN$'),               \n", | ||||
|     "             (r'.*s$', 'NNS'), (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')]\n", | ||||
|     "\n", | ||||
| @ -127,7 +148,7 @@ | ||||
|     "regexp_model = nltk.RegexpTagger(patterns)\n", | ||||
|     "uni_model = nltk.UnigramTagger(training_sentences_X1)\n", | ||||
|     "bi_model = nltk.BigramTagger(training_sentences_X1)\n", | ||||
|     "tri_model = nltk.TrigramTagger(training_sentences_X1)" | ||||
|     "tri_model = nltk.TrigramTagger(training_sentences_X1)'''" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
| @ -139,12 +160,14 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": null, | ||||
|    "execution_count": 88, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "source": [] | ||||
|    "source": [ | ||||
|     "#see Task 2.1" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
| @ -155,12 +178,14 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": null, | ||||
|    "execution_count": 89, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "source": [] | ||||
|    "source": [ | ||||
|     "#see Task 2.2" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
| @ -179,7 +204,7 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 27, | ||||
|    "execution_count": 90, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
| @ -188,13 +213,15 @@ | ||||
|      "text": [ | ||||
|       "[nltk_data] Downloading package treebank to\n", | ||||
|       "[nltk_data]     /Users/Carsten/nltk_data...\n", | ||||
|       "[nltk_data]   Package treebank is already up-to-date!\n" | ||||
|       "[nltk_data]   Package treebank is already up-to-date!\n", | ||||
|       "downloaded treebank\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "nltk.download('treebank')\n", | ||||
|     "x1 = nltk.corpus.treebank" | ||||
|     "x1 = nltk.corpus.treebank\n", | ||||
|     "print(\"downloaded treebank\")" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
| @ -206,7 +233,7 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 28, | ||||
|    "execution_count": 91, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
| @ -214,13 +241,15 @@ | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "[nltk_data] Downloading package brown to /Users/Carsten/nltk_data...\n", | ||||
|       "[nltk_data]   Package brown is already up-to-date!\n" | ||||
|       "[nltk_data]   Package brown is already up-to-date!\n", | ||||
|       "downloaded brown\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "nltk.download('brown')\n", | ||||
|     "x2 = nltk.corpus.brown" | ||||
|     "x2 = nltk.corpus.brown\n", | ||||
|     "print(\"downloaded brown\")" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
| @ -232,7 +261,38 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 64, | ||||
|    "execution_count": 92, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "#? nltk.corpus.ConllCorpusReader" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 93, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "downloaded german tiger corpus\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "# TODO: loading german corpus \n", | ||||
|     "X3 = nltk.corpus.ConllCorpusReader(root='/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/german/', fileids=['tiger_release_aug07.corrected.16012013.conll09'], columntypes=['ignore', 'words', 'ignore', 'ignore', 'pos'], encoding='utf-8')\n", | ||||
|     "german_tagged_sents = X3.tagged_sents()\n", | ||||
|     "print(\"downloaded german tiger corpus\")" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 94, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
| @ -241,7 +301,7 @@ | ||||
|        "'#import pandas as pd\\n#df = pd.read_table(\"/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/croatia/set.hr.conll\")\\n#df.head()\\n\\n#x3 = other language\\n#from croatia:\\n#by ZˇeljkoAgic ́,⋆NikolaLjubesˇic ́ http://www.lrec-conf.org/proceedings/lrec2014/pdf/690_Paper.pdf\\n#licenses: https://creativecommons.org/licenses/by-sa/4.0/\\ncorp = nltk.corpus.ConllCorpusReader(root=\"/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/croatia/\", fileids=[\"set.hr.conll\"], columntypes=(\\'ignore\\',\\'ignore\\',\\'pos\\',\\'ignore\\',\\'ignore\\',\\'ignore\\',\\'ignore\\',\\'ignore\\',\\'ignore\\',\\'ignore\\'))\\nprint(corp.tagged_sents[-100])\\n#from croatia:\\n#by ZˇeljkoAgic ́,⋆NikolaLjubesˇic ́ http://www.lrec-conf.org/proceedings/lrec2014/pdf/690_Paper.pdf\\n#licenses: https://creativecommons.org/licenses/by-sa/4.0/'" | ||||
|       ] | ||||
|      }, | ||||
|      "execution_count": 64, | ||||
|      "execution_count": 94, | ||||
|      "metadata": {}, | ||||
|      "output_type": "execute_result" | ||||
|     } | ||||
| @ -262,42 +322,6 @@ | ||||
|     "#licenses: https://creativecommons.org/licenses/by-sa/4.0/'''\n" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 30, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "#? nltk.corpus.ConllCorpusReader" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 52, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "<class 'nltk.collections.LazyMap'>\n", | ||||
|       "50472\n", | ||||
|       "[('So', 'ADV'), ('kann', 'VMFIN'), ('man', 'PIS'), ('Marsilius', 'NE'), ('von', 'APPR'), ('Padua', 'NE'), ('so', 'ADV'), ('wenig', 'ADV'), ('zu', 'APPR'), ('einem', 'ART'), ('Vorläufer', 'NN'), ('moderner', 'ADJA'), ('Volkssouveränität', 'NN'), ('machen', 'VVINF'), ('wie', 'KOKOM'), ('Rousseau', 'NE'), ('zum', 'APPRART'), ('Verkünder', 'NN'), ('eines', 'ART'), ('``', '$('), ('Zurück', 'NN'), ('zur', 'APPRART'), ('Natur', 'NN'), (\"''\", '$('), ('.', '$.')]\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "# TODO: loading german corpus \n", | ||||
|     "X3 = nltk.corpus.ConllCorpusReader(root='/Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions/sets/german/', fileids=['tiger_release_aug07.corrected.16012013.conll09'], columntypes=['ignore', 'words', 'ignore', 'ignore', 'pos'], encoding='utf-8')\n", | ||||
|     "german_tagged_sents = X3.tagged_sents()\n", | ||||
|     "print(type(german_tagged_sents))\n", | ||||
|     "print(len(german_tagged_sents))\n", | ||||
|     "\n", | ||||
|     "print (german_tagged_sents[-100])" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
| @ -312,6 +336,13 @@ | ||||
|     "    * performance 1.6.x = model3.x in X2" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "#### Generating Testdata" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
| @ -325,27 +356,26 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 32, | ||||
|    "execution_count": 95, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "#to generate trainingsdata, delete the assigned tags as a function\n", | ||||
|     "#to generate trainingsdata, ignore the assigned tags as a function\n", | ||||
|     "def untag(tagged_sentence):\n", | ||||
|     "    return [w for w, t in tagged_sentence]" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 65, | ||||
|    "execution_count": 96, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "<class 'nltk.corpus.reader.util.ConcatenatedCorpusView'>\n", | ||||
|       "got  3131  training sentences and  783  test sentences\n" | ||||
|      ] | ||||
|     } | ||||
| @ -365,9 +395,16 @@ | ||||
|     "print(\"got \",len(training_sentences_X1),\" training sentences and \", len(test_sentences_X1), \" test sentences\")" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "**transform_to_dataset** generates the input X as a list of feature dictinionaries and an output y as a list of pos tags. " | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 34, | ||||
|    "execution_count": 97, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
| @ -385,14 +422,21 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 35, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "execution_count": 98, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "generated X1 (feature sets) and y1 set of teacher tags\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "#trainings inputset X and training teacher set y\n", | ||||
|     "X1, y1 = transform_to_dataset(training_sentences_X1)" | ||||
|     "X1, y1 = transform_to_dataset(training_sentences_X1)\n", | ||||
|     "print(\"generated X1 (feature sets) and y1 set of teacher tags\")" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
| @ -408,7 +452,7 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 36, | ||||
|    "execution_count": 99, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
| @ -434,14 +478,21 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 37, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "execution_count": 100, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "generated X2 (feature sets) and y2 set of teacher tags\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "#trainings inputset X and training teacher set y\n", | ||||
|     "X2, y2 = transform_to_dataset(training_sentences_X2)" | ||||
|     "X2, y2 = transform_to_dataset(training_sentences_X2)\n", | ||||
|     "print(\"generated X2 (feature sets) and y2 set of teacher tags\")#(X3[:3], y3[:3])" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
| @ -457,14 +508,13 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 63, | ||||
|    "execution_count": 101, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "<class 'nltk.collections.LazyMap'>\n", | ||||
|       "got  40377  training sentences and  10095  test sentences\n" | ||||
|      ] | ||||
|     } | ||||
| @ -486,12 +536,21 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 66, | ||||
|    "execution_count": 102, | ||||
|    "metadata": {}, | ||||
|    "outputs": [], | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "generated X3 (feature sets) and y3 set of teacher tags\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "#trainings inputset X and training teacher set y\n", | ||||
|     "X3, y3 = transform_to_dataset(training_sentences_X3)" | ||||
|     "X3, y3 = transform_to_dataset(training_sentences_X3)\n", | ||||
|     "print(\"generated X3 (feature sets) and y3 set of teacher tags\")#(X3[:3], y3[:3])" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
| @ -508,15 +567,22 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 40, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "execution_count": 103, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "imported sktree, DictVectorizer, Pipeline\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "from sklearn.tree import DecisionTreeClassifier\n", | ||||
|     "from sklearn.feature_extraction import DictVectorizer\n", | ||||
|     "from sklearn.pipeline import Pipeline" | ||||
|     "from sklearn.pipeline import Pipeline\n", | ||||
|     "print(\"imported sktree, DictVectorizer, Pipeline\")" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
| @ -528,30 +594,44 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 41, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "execution_count": 104, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "Initialized classifier\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "clf = Pipeline([\n", | ||||
|     "    ('vectorizer', DictVectorizer(sparse=False)),\n", | ||||
|     "    ('classifier', DecisionTreeClassifier(criterion='entropy'))\n", | ||||
|     "])" | ||||
|     "])\n", | ||||
|     "print(\"Initialized classifier\")" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "##### Calculate performance 1.1 \n", | ||||
|     "#### Calculating performances" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "##### Calculate performance 1.1 - own POS tagger model with X1 = treebank\n", | ||||
|     "* fit the decision tree for a limited amount (size) of training \n", | ||||
|     "* test data and compare with score function on testdata" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 42, | ||||
|    "execution_count": 105, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
| @ -559,7 +639,7 @@ | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "training OK\n", | ||||
|       "Accuracy: 0.880632766106\n" | ||||
|       "Accuracy: 0.883077997904\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
| @ -580,12 +660,12 @@ | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "##### Calculate performance 1.2" | ||||
|     "##### Calculate performance 1.2 - pre-trained POS tagger model using NLTK (maxentropy english) with X1 = treebank" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 43, | ||||
|    "execution_count": 106, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
| @ -617,7 +697,7 @@ | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "##### Calculate performance 1.3\n", | ||||
|     "##### Calculate performance 1.3 - rule-based classifiers with X1 = treebank\n", | ||||
|     "1. DefaultTagger that simply tags everything with the same tag\n", | ||||
|     "2. RegexpTagger that applies tags according to a set of regular expressions\n", | ||||
|     "3. N-Gram (n-gram tagger is a generalization of a unigram tagger whose context is the current word together with the part-of-speech tags of the n-1 preceding token)\n", | ||||
| @ -628,7 +708,7 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 44, | ||||
|    "execution_count": 107, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
| @ -644,6 +724,17 @@ | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "#used from description for RegexpTagger\n", | ||||
|     "patterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*\\'s$', 'NN$'),               \n", | ||||
|     "             (r'.*s$', 'NNS'), (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')]\n", | ||||
|     "\n", | ||||
|     "#train taggers\n", | ||||
|     "def_model = nltk.DefaultTagger('NN')\n", | ||||
|     "regexp_model = nltk.RegexpTagger(patterns)\n", | ||||
|     "uni_model = nltk.UnigramTagger(training_sentences_X1)\n", | ||||
|     "bi_model = nltk.BigramTagger(training_sentences_X1)\n", | ||||
|     "tri_model = nltk.TrigramTagger(training_sentences_X1)\n", | ||||
|     "\n", | ||||
|     "#evaluate taggers\n", | ||||
|     "# performance of Default Tagger\n", | ||||
|     "performance1_3_1 = def_model.evaluate(test_sentences_X1)\n", | ||||
| @ -670,19 +761,19 @@ | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "##### Calculate performance 1.4" | ||||
|     "##### Calculate performance 1.4 - own POS tagger model with X2 = brown" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 45, | ||||
|    "execution_count": 108, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "calculated perfomance 1.4=  0.75680543774\n" | ||||
|       "calculated perfomance 1.4=  0.772156918908\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
| @ -698,12 +789,12 @@ | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "##### Calculate performance 1.5" | ||||
|     "##### Calculate performance 1.5 - pre-trained POS tagger model using NLTK (maxentropy english) with X2 = brown" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 46, | ||||
|    "execution_count": 109, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
| @ -735,12 +826,12 @@ | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "##### Calculate performance 1.6" | ||||
|     "##### Calculate performance 1.6 - rule-based classifiers with X2 = brown" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 47, | ||||
|    "execution_count": 110, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
| @ -793,16 +884,14 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 48, | ||||
|    "execution_count": 111, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "3.6.3\n", | ||||
|       "checking...\n", | ||||
|       "[('Hello', 'VBD-HL'), ('world', 'VBD'), (',', ','), ('lets', 'NNS'), ('do', 'DO'), ('something', 'PN'), ('awesome', 'NN'), ('today', 'NR'), ('!', 'CD')]\n" | ||||
|       "3.6.3\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
| @ -816,7 +905,7 @@ | ||||
|     "import platform\n", | ||||
|     "print(platform.python_version())\n", | ||||
|     "\n", | ||||
|     "print(list(pos_tag(word_tokenize('Hello world, lets do something awesome today!'))))" | ||||
|     "#print(list(pos_tag(word_tokenize('Hello world, lets do something awesome today!'))))" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
| @ -835,7 +924,7 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 49, | ||||
|    "execution_count": 112, | ||||
|    "metadata": { | ||||
|     "scrolled": true | ||||
|    }, | ||||
| @ -856,7 +945,7 @@ | ||||
|        "<plotly.tools.PlotlyDisplay object>" | ||||
|       ] | ||||
|      }, | ||||
|      "execution_count": 49, | ||||
|      "execution_count": 112, | ||||
|      "metadata": {}, | ||||
|      "output_type": "execute_result" | ||||
|     } | ||||
| @ -890,9 +979,25 @@ | ||||
|     "        * pre-trained POS tagger model using RDRPOSTagger 1 or TreeTagger 2 (not english)" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "##### Calculate Performance 2.1" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "again building a pipeline:\n", | ||||
|     "* first vectorizing the dictionary based on feature dict\n", | ||||
|     "* second, initializing and training the max entropy classifier decision tree" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 70, | ||||
|    "execution_count": 113, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
| @ -906,15 +1011,15 @@ | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 76, | ||||
|    "execution_count": 114, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "training OK\n", | ||||
|       "Accuracy: 0.836976962858\n" | ||||
|       "training done\n", | ||||
|       "Accuracy: 0.838839915374\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
| @ -922,7 +1027,7 @@ | ||||
|     "size=10000\n", | ||||
|     "clf.fit(X3[:size], y3[:size])\n", | ||||
|     " \n", | ||||
|     "print('training OK')\n", | ||||
|     "print('training done')\n", | ||||
|     " \n", | ||||
|     "X3_test, y3_test = transform_to_dataset(test_sentences_X3)\n", | ||||
|     "\n", | ||||
| @ -931,9 +1036,217 @@ | ||||
|     "print(\"Accuracy:\", performance2_1)" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "markdown", | ||||
|    "metadata": {}, | ||||
|    "source": [ | ||||
|     "##### Calculate Performance 2.2\n", | ||||
|     "* using RDRPOS Taggger in a python 3 port rom https://github.com/jacopofar/RDRPOSTagger-python-3" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 77, | ||||
|    "execution_count": 141, | ||||
|    "metadata": {}, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "#RDRPOSTagger port python 3 from https://github.com/jacopofar/RDRPOSTagger-python-3" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 131, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "wrote file de_text.tx in cwd with each word of the sentence seperated by a space\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "#generate a german txt text file:\n", | ||||
|     "f = open(\"de_text.txt\", 'w')\n", | ||||
|     "for sentence in test_sentences_X3:\n", | ||||
|     "    for word, tag in sentence:\n", | ||||
|     "        f.write(word + \" \")\n", | ||||
|     "    f.write(\"\\n\")\n", | ||||
|     "f.close()\n", | ||||
|     "\n", | ||||
|     "print(\"wrote file de_text.tx in cwd with each word of the sentence seperated by a space\")" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 132, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "stored:  /Users/Carsten/GitRepos/NLP-LAB/Carsten_Solutions\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "#to use RDRPOSTagger we have to store the path where we are working currently and where the donwnloaded  RDRPOSTagger is stored\n", | ||||
|     "import sys, os\n", | ||||
|     "\n", | ||||
|     "#current working directory to restore it later\n", | ||||
|     "dir_path = os.getcwd()\n", | ||||
|     "print(\"stored: \", dir_path)" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 133, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "switched to path: /Users/Carsten/Downloads/RDRPOSTagger-python-3-master/pSCRDRtagger\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "#set the rdrpos as path to work in lownloaded api\n", | ||||
|     "RDRPOS_TAGGER_PATH = \"/Users/Carsten/Downloads/RDRPOSTagger-python-3-master/pSCRDRtagger\"\n", | ||||
|     "sys.path.insert(0, RDRPOS_TAGGER_PATH)\n", | ||||
|     "os.chdir(RDRPOS_TAGGER_PATH)\n", | ||||
|     "print(\"switched to path:\", RDRPOS_TAGGER_PATH)" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 134, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "['Node', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', 'tabStr']\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "# import and rename for easier use\n", | ||||
|     "import RDRPOSTagger\n", | ||||
|     "r = RDRPOSTagger.RDRPOSTagger()\n", | ||||
|     "\n", | ||||
|     "#load files\n", | ||||
|     "r.constructSCRDRtreeFromRDRfile(\"../Models/POS/German.RDR\")\n", | ||||
|     "DICT = RDRPOSTagger.readDictionary(\"../Models/POS/German.DICT\")" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 135, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "('\\nOutput file:', 'de_text.txt.TAGGED')\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "#switch back to dir in which we worked at the start\n", | ||||
|     "os.chdir(dir_path)\n", | ||||
|     "\n", | ||||
|     "# generate file with tags after each word with the delimiter /\n", | ||||
|     "r.tagRawCorpus(DICT, \"de_text.txt\")" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 142, | ||||
|    "metadata": { | ||||
|     "collapsed": true | ||||
|    }, | ||||
|    "outputs": [], | ||||
|    "source": [ | ||||
|     "#from generated textfile above, seperate the word and tags\n", | ||||
|     "tagged_words = []\n", | ||||
|     "f = open(\"de_text.txt.TAGGED\", 'r')\n", | ||||
|     "for line in f:\n", | ||||
|     "    for splits in line.split():\n", | ||||
|     "        cmp = splits.rsplit('/',1)\n", | ||||
|     "        if len(cmp) != 2:\n", | ||||
|     "            print(\"error parsing: \", cmp)\n", | ||||
|     "        else:\n", | ||||
|     "            w,t = cmp\n", | ||||
|     "            tagged_words.append((w,t))" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 143, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "data": { | ||||
|       "text/plain": [ | ||||
|        "[('CUPERTINO', 'NE'), ('(', '$('), ('rtr', 'NE'), ('/', '$('), ('whp', 'XY'), (')', '$('), ('.', '$.'), ('Der', 'ART'), ('Chef', 'NN'), ('des', 'ART')]" | ||||
|       ] | ||||
|      }, | ||||
|      "metadata": {}, | ||||
|      "output_type": "display_data" | ||||
|     }, | ||||
|     { | ||||
|      "data": { | ||||
|       "text/plain": [ | ||||
|        "[[('CUPERTINO', 'NE')], [('(', '$('), ('rtr', 'NE'), ('/', '$('), ('whp', 'XY'), (')', '$('), ('.', '$.')], ...]" | ||||
|       ] | ||||
|      }, | ||||
|      "metadata": {}, | ||||
|      "output_type": "display_data" | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "display(tagged_words[:10])\n", | ||||
|     "display(test_sentences_X3[:10])" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 144, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
|      "name": "stdout", | ||||
|      "output_type": "stream", | ||||
|      "text": [ | ||||
|       "Accuracy 2.2 =  0.9754407616361072\n" | ||||
|      ] | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "performance2_2 = 0 # for test \n", | ||||
|     "\n", | ||||
|     "#counter for the words\n", | ||||
|     "i = 0\n", | ||||
|     "\n", | ||||
|     "#evaluate accuracy\n", | ||||
|     "for sent in test_sentences_X3:\n", | ||||
|     "    for tagged_w in sent:\n", | ||||
|     "        if tagged_w[1] == tagged_words[i][1]:\n", | ||||
|     "            performance2_2 += 1\n", | ||||
|     "        i += 1\n", | ||||
|     "performance2_2 = performance2_2 / len(tagged_words)\n", | ||||
|     "print(\"Accuracy 2.2 = \",performance2_2)" | ||||
|    ] | ||||
|   }, | ||||
|   { | ||||
|    "cell_type": "code", | ||||
|    "execution_count": 145, | ||||
|    "metadata": {}, | ||||
|    "outputs": [ | ||||
|     { | ||||
| @ -952,14 +1265,13 @@ | ||||
|        "<plotly.tools.PlotlyDisplay object>" | ||||
|       ] | ||||
|      }, | ||||
|      "execution_count": 77, | ||||
|      "execution_count": 145, | ||||
|      "metadata": {}, | ||||
|      "output_type": "execute_result" | ||||
|     } | ||||
|    ], | ||||
|    "source": [ | ||||
|     "performance2_2 = 0\n", | ||||
|     "\n", | ||||
|     "#visualize results with plotly\n", | ||||
|     "data = [go.Bar(\n", | ||||
|     "            x=['performance 2.1', 'performance 2.2'],\n", | ||||
|     "            y=[performance2_1, performance2_2]\n", | ||||
|  | ||||
		Reference in New Issue
	
	Block a user