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Corrected typo pane

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cif2cif 2017-04-20 16:30:21 +02:00
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commit 1a38307a2e
1 changed files with 17 additions and 271 deletions
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nlp/4_2_Syntactic_Processing.ipynb
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@ -61,7 +61,7 @@
},
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"cell_type": "code",
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@ -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: {<PRON><ADJ><NOUN>+} \n",
@ -496,37 +294,11 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {
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"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",