From 1a38307a2e270777beb6992f39d80a56440f6c5b Mon Sep 17 00:00:00 2001 From: cif2cif Date: Thu, 20 Apr 2017 16:30:21 +0200 Subject: [PATCH] Corrected typo pane --- nlp/4_2_Syntactic_Processing.ipynb | 288 ++--------------------------- 1 file changed, 17 insertions(+), 271 deletions(-) diff --git a/nlp/4_2_Syntactic_Processing.ipynb b/nlp/4_2_Syntactic_Processing.ipynb index a0b8d2b..c4f9458 100644 --- a/nlp/4_2_Syntactic_Processing.ipynb +++ b/nlp/4_2_Syntactic_Processing.ipynb @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": { "collapsed": false }, @@ -109,19 +109,11 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('I', 'PRON'), ('purchased', 'VERB'), ('this', 'DET'), ('Dell', 'NOUN'), ('monitor', 'NOUN'), ('because', 'ADP'), ('of', 'ADP'), ('budgetary', 'ADJ'), ('concerns', 'NOUN'), ('.', '.'), ('This', 'DET'), ('item', 'NOUN'), ('was', 'VERB'), ('the', 'DET'), ('most', 'ADV'), ('inexpensive', 'ADJ'), ('17', 'NUM'), ('inch', 'NOUN'), ('Apple', 'NOUN'), ('monitor', 'NOUN'), ('available', 'ADJ'), ('to', 'PRT'), ('me', 'PRON'), ('at', 'ADP'), ('the', 'DET'), ('time', 'NOUN'), ('I', 'PRON'), ('made', 'VERB'), ('the', 'DET'), ('purchase', 'NOUN'), ('.', '.'), ('My', 'PRON'), ('overall', 'ADJ'), ('experience', 'NOUN'), ('with', 'ADP'), ('this', 'DET'), ('monitor', 'NOUN'), ('was', 'VERB'), ('very', 'ADV'), ('poor', 'ADJ'), ('.', '.'), ('When', 'ADV'), ('the', 'DET'), ('screen', 'NOUN'), ('was', 'VERB'), (\"n't\", 'ADV'), ('contracting', 'VERB'), ('or', 'CONJ'), ('glitching', 'VERB'), ('the', 'DET'), ('overall', 'ADJ'), ('picture', 'NOUN'), ('quality', 'NOUN'), ('was', 'VERB'), ('poor', 'ADJ'), ('to', 'PRT'), ('fair', 'VERB'), ('.', '.'), ('I', 'PRON'), (\"'ve\", 'VERB'), ('viewed', 'VERB'), ('numerous', 'ADJ'), ('different', 'ADJ'), ('monitor', 'NOUN'), ('models', 'NOUN'), ('since', 'ADP'), ('I', 'PRON'), (\"'m\", 'VERB'), ('a', 'DET'), ('college', 'NOUN'), ('student', 'NOUN'), ('at', 'ADP'), ('UPM', 'NOUN'), ('in', 'ADP'), ('Madrid', 'NOUN'), ('and', 'CONJ'), ('this', 'DET'), ('particular', 'ADJ'), ('monitor', 'NOUN'), ('had', 'VERB'), ('as', 'ADP'), ('poor', 'ADJ'), ('of', 'ADP'), ('picture', 'NOUN'), ('quality', 'NOUN'), ('as', 'ADP'), ('any', 'DET'), ('I', 'PRON'), (\"'ve\", 'VERB'), ('seen', 'VERB'), ('.', '.')]\n" - ] - } - ], + "outputs": [], "source": [ "from nltk import pos_tag, word_tokenize\n", "print (pos_tag(word_tokenize(review), tagset='universal'))" @@ -136,19 +128,11 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('I', 'PRP'), ('purchased', 'VBD'), ('this', 'DT'), ('Dell', 'NNP'), ('monitor', 'NN'), ('because', 'IN'), ('of', 'IN'), ('budgetary', 'JJ'), ('concerns', 'NNS'), ('.', '.'), ('This', 'DT'), ('item', 'NN'), ('was', 'VBD'), ('the', 'DT'), ('most', 'RBS'), ('inexpensive', 'JJ'), ('17', 'CD'), ('inch', 'NN'), ('Apple', 'NNP'), ('monitor', 'NN'), ('available', 'JJ'), ('to', 'TO'), ('me', 'PRP'), ('at', 'IN'), ('the', 'DT'), ('time', 'NN'), ('I', 'PRP'), ('made', 'VBD'), ('the', 'DT'), ('purchase', 'NN'), ('.', '.'), ('My', 'PRP$'), ('overall', 'JJ'), ('experience', 'NN'), ('with', 'IN'), ('this', 'DT'), ('monitor', 'NN'), ('was', 'VBD'), ('very', 'RB'), ('poor', 'JJ'), ('.', '.'), ('When', 'WRB'), ('the', 'DT'), ('screen', 'NN'), ('was', 'VBD'), (\"n't\", 'RB'), ('contracting', 'VBG'), ('or', 'CC'), ('glitching', 'VBG'), ('the', 'DT'), ('overall', 'JJ'), ('picture', 'NN'), ('quality', 'NN'), ('was', 'VBD'), ('poor', 'JJ'), ('to', 'TO'), ('fair', 'VB'), ('.', '.'), ('I', 'PRP'), (\"'ve\", 'VBP'), ('viewed', 'VBN'), ('numerous', 'JJ'), ('different', 'JJ'), ('monitor', 'NN'), ('models', 'NNS'), ('since', 'IN'), ('I', 'PRP'), (\"'m\", 'VBP'), ('a', 'DT'), ('college', 'NN'), ('student', 'NN'), ('at', 'IN'), ('UPM', 'NNP'), ('in', 'IN'), ('Madrid', 'NNP'), ('and', 'CC'), ('this', 'DT'), ('particular', 'JJ'), ('monitor', 'NN'), ('had', 'VBD'), ('as', 'IN'), ('poor', 'JJ'), ('of', 'IN'), ('picture', 'NN'), ('quality', 'NN'), ('as', 'IN'), ('any', 'DT'), ('I', 'PRP'), (\"'ve\", 'VBP'), ('seen', 'VBN'), ('.', '.')]\n" - ] - } - ], + "outputs": [], "source": [ "print (pos_tag(word_tokenize(review)))" ] @@ -181,19 +165,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['I', 'purchase', 'Dell', 'monitor', 'because', 'of', 'budgetary', 'concern', 'item', 'be', 'most', 'inexpensive', '17', 'inch', 'Apple', 'monitor', 'available', 'me', 'at', 'time', 'I', 'make', 'purchase', 'My', 'overall', 'experience', 'with', 'monitor', 'be', 'very', 'poor', 'When', 'screen', 'be', \"n't\", 'contract', 'or', 'glitching', 'overall', 'picture', 'quality', 'be', 'poor', 'fair', 'I', \"'ve\", 'view', 'numerous', 'different', 'monitor', 'model', 'since', 'I', \"'m\", 'college', 'student', 'at', 'UPM', 'in', 'Madrid', 'and', 'particular', 'monitor', 'have', 'a', 'poor', 'of', 'picture', 'quality', 'a', 'I', \"'ve\", 'see']\n" - ] - } - ], + "outputs": [], "source": [ "from nltk.stem import WordNetLemmatizer\n", "\n", @@ -222,110 +198,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(S\n", - " I/PRP\n", - " purchased/VBD\n", - " this/DT\n", - " (ORGANIZATION Dell/NNP)\n", - " monitor/NN\n", - " because/IN\n", - " of/IN\n", - " budgetary/JJ\n", - " concerns/NNS\n", - " ./.\n", - " This/DT\n", - " item/NN\n", - " was/VBD\n", - " the/DT\n", - " most/RBS\n", - " inexpensive/JJ\n", - " 17/CD\n", - " inch/NN\n", - " Apple/NNP\n", - " monitor/NN\n", - " available/JJ\n", - " to/TO\n", - " me/PRP\n", - " at/IN\n", - " the/DT\n", - " time/NN\n", - " I/PRP\n", - " made/VBD\n", - " the/DT\n", - " purchase/NN\n", - " ./.\n", - " My/PRP$\n", - " overall/JJ\n", - " experience/NN\n", - " with/IN\n", - " this/DT\n", - " monitor/NN\n", - " was/VBD\n", - " very/RB\n", - " poor/JJ\n", - " ./.\n", - " When/WRB\n", - " the/DT\n", - " screen/NN\n", - " was/VBD\n", - " n't/RB\n", - " contracting/VBG\n", - " or/CC\n", - " glitching/VBG\n", - " the/DT\n", - " overall/JJ\n", - " picture/NN\n", - " quality/NN\n", - " was/VBD\n", - " poor/JJ\n", - " to/TO\n", - " fair/VB\n", - " ./.\n", - " I/PRP\n", - " 've/VBP\n", - " viewed/VBN\n", - " numerous/JJ\n", - " different/JJ\n", - " monitor/NN\n", - " models/NNS\n", - " since/IN\n", - " I/PRP\n", - " 'm/VBP\n", - " a/DT\n", - " college/NN\n", - " student/NN\n", - " at/IN\n", - " (ORGANIZATION UPM/NNP)\n", - " in/IN\n", - " (GPE Madrid/NNP)\n", - " and/CC\n", - " this/DT\n", - " particular/JJ\n", - " monitor/NN\n", - " had/VBD\n", - " as/IN\n", - " poor/JJ\n", - " of/IN\n", - " picture/NN\n", - " quality/NN\n", - " as/IN\n", - " any/DT\n", - " I/PRP\n", - " 've/VBP\n", - " seen/VBN\n", - " ./.)\n" - ] - } - ], + "outputs": [], "source": [ "from nltk import ne_chunk, pos_tag, word_tokenize\n", "ne_tagged = ne_chunk(pos_tag(word_tokenize(review)), binary=False)\n", @@ -357,7 +234,7 @@ "We can use the StandfordParser that is integrated in NLTK, but it requires to configure the CLASSPATH, which can be a bit annoying. Instead, we are going to see some demos to understand how grammars work. In case you are interested, you can consult the [manual](http://www.nltk.org/api/nltk.parse.html) to run it.\n", "\n", "In the following example, you will run an interactive context-free parser, called [shift-reduce parser](http://www.nltk.org/book/ch08.html).\n", - "The pane on the left shows the grammar as a list of production rules. The pane on the right contains the stack and the remaining input.\n", + "The panel on the left shows the grammar as a list of production rules. The panel on the right contains the stack and the remaining input.\n", "\n", "You should:\n", "* Run pressing 'step' until the sentence is fully analyzed. With each step, the parser either shifts one word onto the stack or reduces two subtrees of the stack into a new subtree.\n", @@ -366,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "collapsed": false }, @@ -389,90 +266,11 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(S\n", - " I/PRON\n", - " purchased/VERB\n", - " (NP this/DET Dell/NOUN monitor/NOUN)\n", - " because/ADP\n", - " of/ADP\n", - " (NP budgetary/ADJ concerns/NOUN)\n", - " ./.\n", - " (NP This/DET item/NOUN)\n", - " was/VERB\n", - " (NP\n", - " the/DET\n", - " most/ADV\n", - " inexpensive/ADJ\n", - " 17/NUM\n", - " inch/NOUN\n", - " Apple/NOUN\n", - " monitor/NOUN)\n", - " available/ADJ\n", - " to/PRT\n", - " me/PRON\n", - " at/ADP\n", - " (NP the/DET time/NOUN)\n", - " I/PRON\n", - " made/VERB\n", - " (NP the/DET purchase/NOUN)\n", - " ./.\n", - " (NP My/PRON overall/ADJ experience/NOUN)\n", - " with/ADP\n", - " (NP this/DET monitor/NOUN)\n", - " was/VERB\n", - " very/ADV\n", - " poor/ADJ\n", - " ./.\n", - " When/ADV\n", - " (NP the/DET screen/NOUN)\n", - " was/VERB\n", - " n't/ADV\n", - " contracting/VERB\n", - " or/CONJ\n", - " glitching/VERB\n", - " (NP the/DET overall/ADJ picture/NOUN quality/NOUN)\n", - " was/VERB\n", - " poor/ADJ\n", - " to/PRT\n", - " fair/VERB\n", - " ./.\n", - " I/PRON\n", - " 've/VERB\n", - " viewed/VERB\n", - " (NP numerous/ADJ different/ADJ monitor/NOUN models/NOUN)\n", - " since/ADP\n", - " I/PRON\n", - " 'm/VERB\n", - " (NP a/DET college/NOUN student/NOUN)\n", - " at/ADP\n", - " (NP UPM/NOUN)\n", - " in/ADP\n", - " (NP Madrid/NOUN)\n", - " and/CONJ\n", - " (NP this/DET particular/ADJ monitor/NOUN)\n", - " had/VERB\n", - " as/ADP\n", - " poor/ADJ\n", - " of/ADP\n", - " (NP picture/NOUN quality/NOUN)\n", - " as/ADP\n", - " any/DET\n", - " I/PRON\n", - " 've/VERB\n", - " seen/VERB\n", - " ./.)\n" - ] - } - ], + "outputs": [], "source": [ "from nltk.chunk.regexp import *\n", "pattern = \"\"\"NP: {+} \n", @@ -496,37 +294,11 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "[Tree('NP', [('this', 'DET'), ('Dell', 'NOUN'), ('monitor', 'NOUN')]),\n", - " Tree('NP', [('budgetary', 'ADJ'), ('concerns', 'NOUN')]),\n", - " Tree('NP', [('This', 'DET'), ('item', 'NOUN')]),\n", - " Tree('NP', [('the', 'DET'), ('most', 'ADV'), ('inexpensive', 'ADJ'), ('17', 'NUM'), ('inch', 'NOUN'), ('Apple', 'NOUN'), ('monitor', 'NOUN')]),\n", - " Tree('NP', [('the', 'DET'), ('time', 'NOUN')]),\n", - " Tree('NP', [('the', 'DET'), ('purchase', 'NOUN')]),\n", - " Tree('NP', [('My', 'PRON'), ('overall', 'ADJ'), ('experience', 'NOUN')]),\n", - " Tree('NP', [('this', 'DET'), ('monitor', 'NOUN')]),\n", - " Tree('NP', [('the', 'DET'), ('screen', 'NOUN')]),\n", - " Tree('NP', [('the', 'DET'), ('overall', 'ADJ'), ('picture', 'NOUN'), ('quality', 'NOUN')]),\n", - " Tree('NP', [('numerous', 'ADJ'), ('different', 'ADJ'), ('monitor', 'NOUN'), ('models', 'NOUN')]),\n", - " Tree('NP', [('a', 'DET'), ('college', 'NOUN'), ('student', 'NOUN')]),\n", - " Tree('NP', [('UPM', 'NOUN')]),\n", - " Tree('NP', [('Madrid', 'NOUN')]),\n", - " Tree('NP', [('this', 'DET'), ('particular', 'ADJ'), ('monitor', 'NOUN')]),\n", - " Tree('NP', [('picture', 'NOUN'), ('quality', 'NOUN')])]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def extractTrees(parsed_tree, category='NP'):\n", " return list(parsed_tree.subtrees(filter=lambda x: x.label()==category))\n", @@ -536,37 +308,11 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "text/plain": [ - "['this Dell monitor',\n", - " 'budgetary concerns',\n", - " 'This item',\n", - " 'the most inexpensive 17 inch Apple monitor',\n", - " 'the time',\n", - " 'the purchase',\n", - " 'My overall experience',\n", - " 'this monitor',\n", - " 'the screen',\n", - " 'the overall picture quality',\n", - " 'numerous different monitor models',\n", - " 'a college student',\n", - " 'UPM',\n", - " 'Madrid',\n", - " 'this particular monitor',\n", - " 'picture quality']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "def extractStrings(parsed_tree, category='NP'):\n", " return [\" \".join(word for word, pos in vp.leaves()) for vp in extractTrees(parsed_tree, category)]\n",