task 1 erste dateien
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Carsten_Solutions/Exercise 1.ipynb
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511
Carsten_Solutions/Exercise 1.ipynb
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"cells": [
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Exercise 1"
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]
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import nltk\n",
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"from nltk import word_tokenize, pos_tag"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Classifiers\n",
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"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!__"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.feature_extraction import DictVectorizer\n",
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"from sklearn.pipeline import Pipeline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1. model1 = your POS tagger model (english)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'word': 'bims', 'length': 4, 'is_capitalized': False, 'prefix-1': 'b', 'suffix-1': 's', 'prev_word': 'i', 'next_word': 'der'}\n"
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]
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}
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],
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"source": [
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"def features(sentence, index):\n",
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" return {\n",
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" 'word': sentence[index],\n",
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" 'length': len(sentence[index]),\n",
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" 'is_capitalized': sentence[index][0].upper() == sentence[index][0],\n",
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" 'prefix-1': sentence[index][0],\n",
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" 'suffix-1': sentence[index][-1],\n",
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" 'prev_word': '' if index == 0 else sentence[index - 1],\n",
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" 'next_word': '' if index == len(sentence) - 1 else sentence[index + 1]\n",
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" }\n",
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"\n",
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"print(features(\"halli hallo i bims der Programmierer\".strip().split(\" \"), 3))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2. model2 = pre-trained POS tagger model using NLTK (maxentropy english)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 3. model3.x = rule-based classifiers (x = 1 to 5)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 4. model4 = your POS tagger model (not english)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 5. model5 = pre-trained POS tagger model using RDRPOSTagger 1 or TreeTagger 2 (not english)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Corpora\n",
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"note: data split for training/test = 0.8/0.2 (sequencial)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 1. X1 = nltk.corpus.treebank (english)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[nltk_data] Downloading package treebank to\n",
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"[nltk_data] /Users/Carsten/nltk_data...\n",
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"[nltk_data] Package treebank is already up-to-date!\n"
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]
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}
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],
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"source": [
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"nltk.download('treebank')\n",
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"x1 = nltk.corpus.treebank"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 2. X2 = nltk.corpus.brown (english)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[nltk_data] Downloading package brown to /Users/Carsten/nltk_data...\n",
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"[nltk_data] Package brown is already up-to-date!\n"
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]
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}
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],
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"source": [
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"nltk.download('brown')\n",
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"x2 = nltk.corpus.brown"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 3. X3 = other language (not english)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#nltk.download('brown')\n",
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"#x3 = other language"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Task 1\n",
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"* get results for english (plot a graph with all classifiers x results)\n",
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" * performance 1.1 = model1 in X1"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### Generate Training and Testdata\n",
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"1. split annotaed sentences into training and testdata\n",
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"2. split trainingdata into input data and teacherdata\n",
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" *input is the feature vector of each word\n",
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" *output is a list of POS tags for each word and sentences"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"got 3131 training sentences and 783 test sentences\n"
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]
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}
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],
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"source": [
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"#to generate trainingsdata, delete the assigned tags as a function\n",
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"def untag(tagged_sentence):\n",
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" return [w for w, t in tagged_sentence]\n",
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"\n",
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"#object including the annotated sentences\n",
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"annotated_sent = nltk.corpus.treebank.tagged_sents()\n",
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"\n",
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"#to split the data, calculate the borders for ratio\n",
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"cutoff = int(.8 * len(annotated_sent))\n",
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"training_sentences = annotated_sent[:cutoff]\n",
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"test_sentences = annotated_sent[cutoff:]\n",
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"\n",
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"#show the amount of sentences\n",
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"print(\"got \",len(training_sentences),\" training sentences and \", len(test_sentences), \" test sentences\")\n",
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"\n",
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"#for training split sentences with its tags into y (for a sentences its resulting tags for each word) and transform sentences and x as a list of the features extracet for echt word in the sentences\n",
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"def transform_to_dataset(tagged_sentences):\n",
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" X, y = [], []\n",
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" for tagged_sentence in tagged_sentences:\n",
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" for index in range(len(tagged_sentence)):\n",
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" X.append(features(untag(tagged_sentence), index))\n",
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" y.append(tagged_sentence[index][1]) \n",
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" return X, y\n",
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"\n",
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"#trainings inputset X and training teacher set y\n",
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"X, y = transform_to_dataset(training_sentences)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"#### Implementing a classifier\n",
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"relevant imports\n",
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"* decision tree as the AI for classfing\n",
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"* dict vercorizer transforms the feature dictionary into a vector as the input for the tree"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.feature_extraction import DictVectorizer\n",
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"from sklearn.pipeline import Pipeline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Pipeline manages vectorizer and classifier"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"clf = Pipeline([\n",
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" ('vectorizer', DictVectorizer(sparse=False)),\n",
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" ('classifier', DecisionTreeClassifier(criterion='entropy'))\n",
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"])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### Calculate performance 1.1 \n",
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"* fit the decision tree for a limited amount (size) of training \n",
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"* test data and compare with score function on testdata"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"training OK\n",
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"Accuracy: 0.880832376865\n"
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]
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}
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],
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"source": [
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"size=10000\n",
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"clf.fit(X[:size], y[:size])\n",
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" \n",
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"print('training OK')\n",
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" \n",
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"X_test, y_test = transform_to_dataset(test_sentences)\n",
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"\n",
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"performance1_1 = clf.score(X_test, y_test)\n",
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"\n",
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"print(\"Accuracy:\", performance1_1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"##### Calculate other performances"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"performance1_2 = 0\n",
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"performance1_3 = 0\n",
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"performance1_4 = 0\n",
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"performance1_5 = 0\n",
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"performance1_6 = 0"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Using the classifier\n",
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"for results the link of pos_tags:\n",
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"https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3.6.3\n",
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"checking...\n",
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"[('Hello', 'NNP'), ('world', 'VBD'), (',', ','), ('lets', 'NNS'), ('do', 'VB'), ('something', 'VBG'), ('awesome', 'NN'), ('today', 'NN'), ('!', 'CD')]\n"
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]
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}
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],
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"source": [
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"def pos_tag(sentence):\n",
|
||||||
|
" print('checking...')\n",
|
||||||
|
" tagged_sentence = []\n",
|
||||||
|
" tags = clf.predict([features(sentence, index) for index in range(len(sentence))])\n",
|
||||||
|
" return zip(sentence, tags)\n",
|
||||||
|
"\n",
|
||||||
|
"import platform\n",
|
||||||
|
"print(platform.python_version())\n",
|
||||||
|
"\n",
|
||||||
|
"print(list(pos_tag(word_tokenize('Hello world, lets do something awesome today!'))))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"### Results for Task 1\n",
|
||||||
|
"* get results for english (plot a graph with all classifiers x results)\n",
|
||||||
|
" * performance 1.1 = model1 in X1\n",
|
||||||
|
" * performance 1.2 = model2 in X1\n",
|
||||||
|
" * performance 1.3.x = model3.x in X1\n",
|
||||||
|
" * performance 1.4 = model1 in X2\n",
|
||||||
|
" * performance 1.5 = model2 in X2\n",
|
||||||
|
" * performance 1.6.x = model3.x in X2"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 22,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~carsten95/0 or inside your plot.ly account where it is named 'basic-bar'\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"data": {
|
||||||
|
"text/html": [
|
||||||
|
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~carsten95/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
|
||||||
|
],
|
||||||
|
"text/plain": [
|
||||||
|
"<plotly.tools.PlotlyDisplay object>"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
"execution_count": 22,
|
||||||
|
"metadata": {},
|
||||||
|
"output_type": "execute_result"
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"import plotly\n",
|
||||||
|
"plotly.tools.set_credentials_file(username='carsten95', api_key='vElf5IOxiFheQdjTxjXW')\n",
|
||||||
|
"plotly.__version__\n",
|
||||||
|
"import plotly.plotly as py\n",
|
||||||
|
"import plotly.graph_objs as go\n",
|
||||||
|
"\n",
|
||||||
|
"data = [go.Bar(\n",
|
||||||
|
" x=['performance 1.1', 'performance 1.2', 'performance 1.3', 'performance 1.4', 'performance 1.5' , 'performance 1.6'],\n",
|
||||||
|
" y=[performance1_1, performance1_2, performance1_3, performance1_4, performance1_5, performance1_6]\n",
|
||||||
|
" )]\n",
|
||||||
|
"\n",
|
||||||
|
"py.iplot(data, filename='basic-bar')"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"collapsed": true
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"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": "3.6.3"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
304
Carsten_Solutions/NLP - Test 01.ipynb
Normal file
304
Carsten_Solutions/NLP - Test 01.ipynb
Normal file
File diff suppressed because one or more lines are too long
@ -0,0 +1,545 @@
|
|||||||
|
{
|
||||||
|
"cells": [
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"# NLP Lab Task 1 SoSe 18\n",
|
||||||
|
"\n",
|
||||||
|
"## POS Tagger\n",
|
||||||
|
"due to 08.05.2018\n"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"collapsed": true
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 1,
|
||||||
|
"metadata": {
|
||||||
|
"collapsed": true
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"import nltk\n",
|
||||||
|
"from nltk import word_tokenize, pos_tag"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 2,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"[('Hi', 'NNP'), (',', ','), ('welcome', 'NN'), ('to', 'TO'), ('the', 'DT'), ('NLP', 'NNP'), ('lab', 'NN'), ('!', '.')]\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"tokens = word_tokenize(\"Hi, welcome to the NLP lab!\")\n",
|
||||||
|
"print(pos_tag(tokens))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Exploring the Penn TreeBank (PTB) Corpus"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 3,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"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",
|
||||||
|
"[('Pierre', 'NNP'), ('Vinken', 'NNP'), (',', ','), ('61', 'CD'), ('years', 'NNS'), ('old', 'JJ'), (',', ','), ('will', 'MD'), ('join', 'VB'), ('the', 'DT'), ('board', 'NN'), ('as', 'IN'), ('a', 'DT'), ('nonexecutive', 'JJ'), ('director', 'NN'), ('Nov.', 'NNP'), ('29', 'CD'), ('.', '.')]\n",
|
||||||
|
"Tagged sentences: 3914\n",
|
||||||
|
"Tagged words: 100676\n",
|
||||||
|
"[nltk_data] Downloading package tagsets to /Users/Carsten/nltk_data...\n",
|
||||||
|
"[nltk_data] Package tagsets is already up-to-date!\n",
|
||||||
|
"$: dollar\n",
|
||||||
|
" $ -$ --$ A$ C$ HK$ M$ NZ$ S$ U.S.$ US$\n",
|
||||||
|
"'': closing quotation mark\n",
|
||||||
|
" ' ''\n",
|
||||||
|
"(: opening parenthesis\n",
|
||||||
|
" ( [ {\n",
|
||||||
|
"): closing parenthesis\n",
|
||||||
|
" ) ] }\n",
|
||||||
|
",: comma\n",
|
||||||
|
" ,\n",
|
||||||
|
"--: dash\n",
|
||||||
|
" --\n",
|
||||||
|
".: sentence terminator\n",
|
||||||
|
" . ! ?\n",
|
||||||
|
":: colon or ellipsis\n",
|
||||||
|
" : ; ...\n",
|
||||||
|
"CC: conjunction, coordinating\n",
|
||||||
|
" & 'n and both but either et for less minus neither nor or plus so\n",
|
||||||
|
" therefore times v. versus vs. whether yet\n",
|
||||||
|
"CD: numeral, cardinal\n",
|
||||||
|
" mid-1890 nine-thirty forty-two one-tenth ten million 0.5 one forty-\n",
|
||||||
|
" seven 1987 twenty '79 zero two 78-degrees eighty-four IX '60s .025\n",
|
||||||
|
" fifteen 271,124 dozen quintillion DM2,000 ...\n",
|
||||||
|
"DT: determiner\n",
|
||||||
|
" all an another any both del each either every half la many much nary\n",
|
||||||
|
" neither no some such that the them these this those\n",
|
||||||
|
"EX: existential there\n",
|
||||||
|
" there\n",
|
||||||
|
"FW: foreign word\n",
|
||||||
|
" gemeinschaft hund ich jeux habeas Haementeria Herr K'ang-si vous\n",
|
||||||
|
" lutihaw alai je jour objets salutaris fille quibusdam pas trop Monte\n",
|
||||||
|
" terram fiche oui corporis ...\n",
|
||||||
|
"IN: preposition or conjunction, subordinating\n",
|
||||||
|
" astride among uppon whether out inside pro despite on by throughout\n",
|
||||||
|
" below within for towards near behind atop around if like until below\n",
|
||||||
|
" next into if beside ...\n",
|
||||||
|
"JJ: adjective or numeral, ordinal\n",
|
||||||
|
" third ill-mannered pre-war regrettable oiled calamitous first separable\n",
|
||||||
|
" ectoplasmic battery-powered participatory fourth still-to-be-named\n",
|
||||||
|
" multilingual multi-disciplinary ...\n",
|
||||||
|
"JJR: adjective, comparative\n",
|
||||||
|
" bleaker braver breezier briefer brighter brisker broader bumper busier\n",
|
||||||
|
" calmer cheaper choosier cleaner clearer closer colder commoner costlier\n",
|
||||||
|
" cozier creamier crunchier cuter ...\n",
|
||||||
|
"JJS: adjective, superlative\n",
|
||||||
|
" calmest cheapest choicest classiest cleanest clearest closest commonest\n",
|
||||||
|
" corniest costliest crassest creepiest crudest cutest darkest deadliest\n",
|
||||||
|
" dearest deepest densest dinkiest ...\n",
|
||||||
|
"LS: list item marker\n",
|
||||||
|
" A A. B B. C C. D E F First G H I J K One SP-44001 SP-44002 SP-44005\n",
|
||||||
|
" SP-44007 Second Third Three Two * a b c d first five four one six three\n",
|
||||||
|
" two\n",
|
||||||
|
"MD: modal auxiliary\n",
|
||||||
|
" can cannot could couldn't dare may might must need ought shall should\n",
|
||||||
|
" shouldn't will would\n",
|
||||||
|
"NN: noun, common, singular or mass\n",
|
||||||
|
" common-carrier cabbage knuckle-duster Casino afghan shed thermostat\n",
|
||||||
|
" investment slide humour falloff slick wind hyena override subhumanity\n",
|
||||||
|
" machinist ...\n",
|
||||||
|
"NNP: noun, proper, singular\n",
|
||||||
|
" Motown Venneboerger Czestochwa Ranzer Conchita Trumplane Christos\n",
|
||||||
|
" Oceanside Escobar Kreisler Sawyer Cougar Yvette Ervin ODI Darryl CTCA\n",
|
||||||
|
" Shannon A.K.C. Meltex Liverpool ...\n",
|
||||||
|
"NNPS: noun, proper, plural\n",
|
||||||
|
" Americans Americas Amharas Amityvilles Amusements Anarcho-Syndicalists\n",
|
||||||
|
" Andalusians Andes Andruses Angels Animals Anthony Antilles Antiques\n",
|
||||||
|
" Apache Apaches Apocrypha ...\n",
|
||||||
|
"NNS: noun, common, plural\n",
|
||||||
|
" undergraduates scotches bric-a-brac products bodyguards facets coasts\n",
|
||||||
|
" divestitures storehouses designs clubs fragrances averages\n",
|
||||||
|
" subjectivists apprehensions muses factory-jobs ...\n",
|
||||||
|
"PDT: pre-determiner\n",
|
||||||
|
" all both half many quite such sure this\n",
|
||||||
|
"POS: genitive marker\n",
|
||||||
|
" ' 's\n",
|
||||||
|
"PRP: pronoun, personal\n",
|
||||||
|
" hers herself him himself hisself it itself me myself one oneself ours\n",
|
||||||
|
" ourselves ownself self she thee theirs them themselves they thou thy us\n",
|
||||||
|
"PRP$: pronoun, possessive\n",
|
||||||
|
" her his mine my our ours their thy your\n",
|
||||||
|
"RB: adverb\n",
|
||||||
|
" occasionally unabatingly maddeningly adventurously professedly\n",
|
||||||
|
" stirringly prominently technologically magisterially predominately\n",
|
||||||
|
" swiftly fiscally pitilessly ...\n",
|
||||||
|
"RBR: adverb, comparative\n",
|
||||||
|
" further gloomier grander graver greater grimmer harder harsher\n",
|
||||||
|
" healthier heavier higher however larger later leaner lengthier less-\n",
|
||||||
|
" perfectly lesser lonelier longer louder lower more ...\n",
|
||||||
|
"RBS: adverb, superlative\n",
|
||||||
|
" best biggest bluntest earliest farthest first furthest hardest\n",
|
||||||
|
" heartiest highest largest least less most nearest second tightest worst\n",
|
||||||
|
"RP: particle\n",
|
||||||
|
" aboard about across along apart around aside at away back before behind\n",
|
||||||
|
" by crop down ever fast for forth from go high i.e. in into just later\n",
|
||||||
|
" low more off on open out over per pie raising start teeth that through\n",
|
||||||
|
" under unto up up-pp upon whole with you\n",
|
||||||
|
"SYM: symbol\n",
|
||||||
|
" % & ' '' ''. ) ). * + ,. < = > @ A[fj] U.S U.S.S.R * ** ***\n",
|
||||||
|
"TO: \"to\" as preposition or infinitive marker\n",
|
||||||
|
" to\n",
|
||||||
|
"UH: interjection\n",
|
||||||
|
" Goodbye Goody Gosh Wow Jeepers Jee-sus Hubba Hey Kee-reist Oops amen\n",
|
||||||
|
" huh howdy uh dammit whammo shucks heck anyways whodunnit honey golly\n",
|
||||||
|
" man baby diddle hush sonuvabitch ...\n",
|
||||||
|
"VB: verb, base form\n",
|
||||||
|
" ask assemble assess assign assume atone attention avoid bake balkanize\n",
|
||||||
|
" bank begin behold believe bend benefit bevel beware bless boil bomb\n",
|
||||||
|
" boost brace break bring broil brush build ...\n",
|
||||||
|
"VBD: verb, past tense\n",
|
||||||
|
" dipped pleaded swiped regummed soaked tidied convened halted registered\n",
|
||||||
|
" cushioned exacted snubbed strode aimed adopted belied figgered\n",
|
||||||
|
" speculated wore appreciated contemplated ...\n",
|
||||||
|
"VBG: verb, present participle or gerund\n",
|
||||||
|
" telegraphing stirring focusing angering judging stalling lactating\n",
|
||||||
|
" hankerin' alleging veering capping approaching traveling besieging\n",
|
||||||
|
" encrypting interrupting erasing wincing ...\n",
|
||||||
|
"VBN: verb, past participle\n",
|
||||||
|
" multihulled dilapidated aerosolized chaired languished panelized used\n",
|
||||||
|
" experimented flourished imitated reunifed factored condensed sheared\n",
|
||||||
|
" unsettled primed dubbed desired ...\n",
|
||||||
|
"VBP: verb, present tense, not 3rd person singular\n",
|
||||||
|
" predominate wrap resort sue twist spill cure lengthen brush terminate\n",
|
||||||
|
" appear tend stray glisten obtain comprise detest tease attract\n",
|
||||||
|
" emphasize mold postpone sever return wag ...\n",
|
||||||
|
"VBZ: verb, present tense, 3rd person singular\n",
|
||||||
|
" bases reconstructs marks mixes displeases seals carps weaves snatches\n",
|
||||||
|
" slumps stretches authorizes smolders pictures emerges stockpiles\n",
|
||||||
|
" seduces fizzes uses bolsters slaps speaks pleads ...\n",
|
||||||
|
"WDT: WH-determiner\n",
|
||||||
|
" that what whatever which whichever\n",
|
||||||
|
"WP: WH-pronoun\n",
|
||||||
|
" that what whatever whatsoever which who whom whosoever\n",
|
||||||
|
"WP$: WH-pronoun, possessive\n",
|
||||||
|
" whose\n",
|
||||||
|
"WRB: Wh-adverb\n",
|
||||||
|
" how however whence whenever where whereby whereever wherein whereof why\n",
|
||||||
|
"``: opening quotation mark\n",
|
||||||
|
" ` ``\n",
|
||||||
|
"None\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"nltk.download('treebank')\n",
|
||||||
|
"annotated_sent = nltk.corpus.treebank.tagged_sents()\n",
|
||||||
|
" \n",
|
||||||
|
"print(annotated_sent[0])\n",
|
||||||
|
"print(\"Tagged sentences: \", len(annotated_sent))\n",
|
||||||
|
"print(\"Tagged words:\", len(nltk.corpus.treebank.tagged_words()))\n",
|
||||||
|
"\n",
|
||||||
|
"# tagsets\n",
|
||||||
|
"nltk.download('tagsets')\n",
|
||||||
|
"print(nltk.help.upenn_tagset())"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Training"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 4,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"{'is_capitalized': False,\n",
|
||||||
|
" 'next_word': 'sentence',\n",
|
||||||
|
" 'prefix-1': 'a',\n",
|
||||||
|
" 'prev_word': 'is',\n",
|
||||||
|
" 'suffix-1': 'a',\n",
|
||||||
|
" 'word': 'a'}\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"# TODO: improve this feature extraction function\n",
|
||||||
|
" \n",
|
||||||
|
"def features(sentence, index):\n",
|
||||||
|
" return {\n",
|
||||||
|
" 'word': sentence[index],\n",
|
||||||
|
" 'is_capitalized': sentence[index][0].upper() == sentence[index][0],\n",
|
||||||
|
" 'prefix-1': sentence[index][0],\n",
|
||||||
|
" 'suffix-1': sentence[index][-1],\n",
|
||||||
|
" 'prev_word': '' if index == 0 else sentence[index - 1],\n",
|
||||||
|
" 'next_word': '' if index == len(sentence) - 1 else sentence[index + 1]\n",
|
||||||
|
" }\n",
|
||||||
|
"import pprint \n",
|
||||||
|
"pprint.pprint(features(['This', 'is', 'a', 'sentence'], 2))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 5,
|
||||||
|
"metadata": {
|
||||||
|
"collapsed": true
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": [
|
||||||
|
"def untag(tagged_sentence):\n",
|
||||||
|
" return [w for w, t in tagged_sentence]"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 6,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"2935\n",
|
||||||
|
"979\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"cutoff = int(.75 * len(annotated_sent))\n",
|
||||||
|
"training_sentences = annotated_sent[:cutoff]\n",
|
||||||
|
"test_sentences = annotated_sent[cutoff:]\n",
|
||||||
|
" \n",
|
||||||
|
"print(len(training_sentences))\n",
|
||||||
|
"print(len(test_sentences))\n",
|
||||||
|
" \n",
|
||||||
|
"def transform_to_dataset(tagged_sentences):\n",
|
||||||
|
" X, y = [], []\n",
|
||||||
|
" for tagged in tagged_sentences:\n",
|
||||||
|
" for index in range(len(tagged)):\n",
|
||||||
|
" X.append(features(untag(tagged), index))\n",
|
||||||
|
" y.append(tagged[index][1])\n",
|
||||||
|
" \n",
|
||||||
|
" return X, y\n",
|
||||||
|
" \n",
|
||||||
|
"X, y = transform_to_dataset(training_sentences)"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Implementing a classifier"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 7,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"training OK\n",
|
||||||
|
"Accuracy: 0.878515185602\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"from sklearn.tree import DecisionTreeClassifier\n",
|
||||||
|
"from sklearn.feature_extraction import DictVectorizer\n",
|
||||||
|
"from sklearn.pipeline import Pipeline\n",
|
||||||
|
"\n",
|
||||||
|
"size=10000\n",
|
||||||
|
"\n",
|
||||||
|
"clf = Pipeline([\n",
|
||||||
|
" ('vectorizer', DictVectorizer(sparse=False)),\n",
|
||||||
|
" ('classifier', DecisionTreeClassifier(criterion='entropy'))\n",
|
||||||
|
"])\n",
|
||||||
|
"clf.fit(X[:size], y[:size])\n",
|
||||||
|
" \n",
|
||||||
|
"print('training OK')\n",
|
||||||
|
" \n",
|
||||||
|
"X_test, y_test = transform_to_dataset(test_sentences)\n",
|
||||||
|
" \n",
|
||||||
|
"print(\"Accuracy:\", clf.score(X_test, y_test))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Using the classifier"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 8,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"name": "stdout",
|
||||||
|
"output_type": "stream",
|
||||||
|
"text": [
|
||||||
|
"3.6.3\n",
|
||||||
|
"checking...\n",
|
||||||
|
"[('Hello', 'NN'), ('world', 'NN'), (',', ','), ('lets', 'NNS'), ('do', 'VB'), ('something', 'VBG'), ('awesome', 'NN'), ('today', 'NN'), ('!', 'NNP')]\n"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"def pos_tag(sentence):\n",
|
||||||
|
" print('checking...')\n",
|
||||||
|
" tagged_sentence = []\n",
|
||||||
|
" tags = clf.predict([features(sentence, index) for index in range(len(sentence))])\n",
|
||||||
|
" return zip(sentence, tags)\n",
|
||||||
|
"\n",
|
||||||
|
"import platform\n",
|
||||||
|
"print(platform.python_version())\n",
|
||||||
|
"\n",
|
||||||
|
"print(list(pos_tag(word_tokenize('Hello world, lets do something awesome today!'))))"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Rule-based POS taggers\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",
|
||||||
|
" * UnigramTagger\n",
|
||||||
|
" * BigramTagger\n",
|
||||||
|
" * TrigramTagger"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": 9,
|
||||||
|
"metadata": {},
|
||||||
|
"outputs": [
|
||||||
|
{
|
||||||
|
"ename": "NameError",
|
||||||
|
"evalue": "name 'brown_tagged_sents' is not defined",
|
||||||
|
"output_type": "error",
|
||||||
|
"traceback": [
|
||||||
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||||
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
||||||
|
"\u001b[0;32m<ipython-input-9-cac1441958dc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mnltk\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTrigramTagger\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mtg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbrown_tagged_sents\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m0.9\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m patterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*\\'s$', 'NN$'), \n",
|
||||||
|
"\u001b[0;31mNameError\u001b[0m: name 'brown_tagged_sents' is not defined"
|
||||||
|
]
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"source": [
|
||||||
|
"#nltk.download('brown')\n",
|
||||||
|
"\n",
|
||||||
|
"from nltk.corpus import brown\n",
|
||||||
|
"from nltk import DefaultTagger as df\n",
|
||||||
|
"from nltk import UnigramTagger as ut\n",
|
||||||
|
"from nltk import BigramTagger as bt\n",
|
||||||
|
"from nltk import TrigramTagger as tg\n",
|
||||||
|
"\n",
|
||||||
|
"size = int(len(brown_tagged_sents) * 0.9)\n",
|
||||||
|
"\n",
|
||||||
|
"patterns = [(r'.*ing$', '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",
|
||||||
|
"brown_tagged_sents = brown.tagged_sents(categories='news')\n",
|
||||||
|
"brown_sents = brown.sents(categories='news')\n",
|
||||||
|
"\n",
|
||||||
|
"train_sents = brown_tagged_sents[:size]\n",
|
||||||
|
"test_sents = brown_tagged_sents[size:]\n",
|
||||||
|
"\n",
|
||||||
|
"def_model = nltk.DefaultTagger('NN')\n",
|
||||||
|
"uni_model = nltk.UnigramTagger(train_sents)\n",
|
||||||
|
"bi_model = nltk.BigramTagger(train_sents)\n",
|
||||||
|
"tri_model = nltk.TrigramTagger(train_sents)\n",
|
||||||
|
"regexp_model = nltk.RegexpTagger(patterns)\n",
|
||||||
|
"\n",
|
||||||
|
"# performance of Default Tagger\n",
|
||||||
|
"print(def_model.evaluate(train_sents))\n",
|
||||||
|
"print(def_model.evaluate(test_sents))\n",
|
||||||
|
"print()\n",
|
||||||
|
"# performance of Unigram Tagger\n",
|
||||||
|
"print(uni_model.evaluate(train_sents))\n",
|
||||||
|
"print(uni_model.evaluate(test_sents))\n",
|
||||||
|
"print()\n",
|
||||||
|
"# performance of Bigram Tagger\n",
|
||||||
|
"print(bi_model.evaluate(train_sents))\n",
|
||||||
|
"print(bi_model.evaluate(test_sents))\n",
|
||||||
|
"print()\n",
|
||||||
|
"# performance of Trigram Tagger\n",
|
||||||
|
"print(tri_model.evaluate(train_sents))\n",
|
||||||
|
"print(tri_model.evaluate(test_sents))\n",
|
||||||
|
"print()\n",
|
||||||
|
"# performance of Regex Tagger\n",
|
||||||
|
"print(regexp_model.evaluate(train_sents))\n",
|
||||||
|
"print(regexp_model.evaluate(test_sents))\n",
|
||||||
|
"print()"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "markdown",
|
||||||
|
"metadata": {},
|
||||||
|
"source": [
|
||||||
|
"## Exercise 01\n",
|
||||||
|
"### In this lab you will learn how to train your own POS tagger classifier and test it against some pre-trained models\n",
|
||||||
|
"__Pleases implement your code and upload it to git using (jupyter notebook format)__\n",
|
||||||
|
"#### Classifiers\n",
|
||||||
|
"1. model1 = your POS tagger model (english)\n",
|
||||||
|
"2. model2 = pre-trained POS tagger model using NLTK (maxentropy english)\n",
|
||||||
|
"3. model3.x = rule-based classifiers (x = 1 to 5)\n",
|
||||||
|
"4. model4 = your POS tagger model (not english)\n",
|
||||||
|
"5. model5 = pre-trained POS tagger model using RDRPOSTagger 1 or TreeTagger 2 (not english)\n",
|
||||||
|
"\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!__\n",
|
||||||
|
"#### Corpora\n",
|
||||||
|
"1. X1 = nltk.corpus.treebank (english)\n",
|
||||||
|
"2. X2 = nltk.corpus.brown (english)\n",
|
||||||
|
"3. X3 = other language (not english)\n",
|
||||||
|
"note: data split for training/test = 0.8/0.2 (sequencial)\n",
|
||||||
|
"#### Task 1\n",
|
||||||
|
"* get results for english (plot a graph with all classifiers x results)\n",
|
||||||
|
" * performance 1.1 = model1 in X1\n",
|
||||||
|
" * performance 1.2 = model2 in X1\n",
|
||||||
|
" * performance 1.3.x = model3.x in X1\n",
|
||||||
|
" * performance 1.4 = model1 in X2\n",
|
||||||
|
" * performance 1.5 = model2 in X2\n",
|
||||||
|
" * performance 1.6.x = model3.x in X2\n",
|
||||||
|
"#### Task 2\n",
|
||||||
|
"* train your model with standard features (plot a graph with all classifiers x results)\n",
|
||||||
|
" * performance 2.1 = model4 in X3\n",
|
||||||
|
" * performance 2.2 = model5 in X3\n",
|
||||||
|
"### notes:\n",
|
||||||
|
"1. you can save your trained models using pickle (import pickle)\n",
|
||||||
|
"2. please upload your jupyter file to git\n",
|
||||||
|
"3. this script just gives a general idea, please organize and comment your code accordingly\n",
|
||||||
|
"4. you have to make sure the language you choose is supported for one of the classifiers suggested (see above) AND you are able to find a corpus in that language (example: Tiger Corpus for German). You can also search the Web in order to try to find a pre-trained classifier in your language. If that is not possible, just choose one existing. Please also make sure the language you have choosen does not overlap with other students.\n",
|
||||||
|
"5. If you are able to find an annotated corpus and format is CoNLL, you can easly read it using the following method in NLTK:\n",
|
||||||
|
"corp = nltk.corpus.ConllCorpusReader()\n",
|
||||||
|
"6. a nice library to create charts: https://plot.ly/python/bar-charts/"
|
||||||
|
]
|
||||||
|
},
|
||||||
|
{
|
||||||
|
"cell_type": "code",
|
||||||
|
"execution_count": null,
|
||||||
|
"metadata": {
|
||||||
|
"collapsed": true
|
||||||
|
},
|
||||||
|
"outputs": [],
|
||||||
|
"source": []
|
||||||
|
}
|
||||||
|
],
|
||||||
|
"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": "3.6.3"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"nbformat": 4,
|
||||||
|
"nbformat_minor": 2
|
||||||
|
}
|
||||||
@ -2,8 +2,10 @@
|
|||||||
"cells": [
|
"cells": [
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 18,
|
"execution_count": 1,
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"collapsed": true
|
||||||
|
},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"import nltk"
|
"import nltk"
|
||||||
@ -18,15 +20,15 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 13,
|
"execution_count": 2,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"[nltk_data] Downloading package punkt to /home/jonas/nltk_data...\n",
|
"[nltk_data] Downloading package punkt to /Users/Carsten/nltk_data...\n",
|
||||||
"[nltk_data] Package punkt is already up-to-date!\n"
|
"[nltk_data] Unzipping tokenizers/punkt.zip.\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@ -35,7 +37,7 @@
|
|||||||
"True"
|
"True"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 13,
|
"execution_count": 2,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"output_type": "execute_result"
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
@ -46,7 +48,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 19,
|
"execution_count": 3,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
@ -54,9 +56,8 @@
|
|||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"[nltk_data] Downloading package averaged_perceptron_tagger to\n",
|
"[nltk_data] Downloading package averaged_perceptron_tagger to\n",
|
||||||
"[nltk_data] /home/jonas/nltk_data...\n",
|
"[nltk_data] /Users/Carsten/nltk_data...\n",
|
||||||
"[nltk_data] Package averaged_perceptron_tagger is already up-to-\n",
|
"[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.\n"
|
||||||
"[nltk_data] date!\n"
|
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
@ -65,7 +66,7 @@
|
|||||||
"True"
|
"True"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 19,
|
"execution_count": 3,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"output_type": "execute_result"
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
@ -83,8 +84,10 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 6,
|
"execution_count": 4,
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"collapsed": true
|
||||||
|
},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": [
|
"source": [
|
||||||
"sentence = \"This is a test sentence.\""
|
"sentence = \"This is a test sentence.\""
|
||||||
@ -92,7 +95,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 16,
|
"execution_count": 5,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
@ -112,7 +115,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 17,
|
"execution_count": 6,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
{
|
{
|
||||||
@ -138,7 +141,9 @@
|
|||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": null,
|
"execution_count": null,
|
||||||
"metadata": {},
|
"metadata": {
|
||||||
|
"collapsed": true
|
||||||
|
},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
"source": []
|
"source": []
|
||||||
}
|
}
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user