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sitc/nlp/4_2_Syntactic_Processing.ipynb
cif2cif c55a1c077b Added tag UPenn example
2017-04-20 15:56:32 +02:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"![](images/EscUpmPolit_p.gif \"UPM\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Course Notes for Learning Intelligent Systems"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Department of Telematic Engineering Systems, Universidad Politécnica de Madrid, © 2016 Carlos A. Iglesias"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Syntactic Processing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Table of Contents\n",
"\n",
"* [Objectives](#Objectives)\n",
"* [POS Tagging](#POS-Tagging)\n",
"* [NER](#NER)\n",
"* [Parsing and Chunking](#Parsing-and-Chunking)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Objectives"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this session we are going to learn how to analyse the syntax of text. In particular, we will learn\n",
"* Understand and perform POS (Part of Speech) tagging\n",
"* Understand and perform NER (Named Entity Recognition)\n",
"* Understand and parse texts\n",
"\n",
"We will use the same examples than in the previous notebook, slightly modified for learning purposes."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"review = \"\"\"I purchased this Dell monitor because of budgetary concerns. This item was the most inexpensive 17 inch Apple monitor \n",
"available to me at the time I made the purchase. My overall experience with this monitor was very poor. When the \n",
"screen wasn't contracting or glitching the overall picture quality was poor to fair. I've viewed numerous different \n",
"monitor models since I 'm a college student at UPM in Madrid and this particular monitor had as poor of picture quality as \n",
"any I 've seen.\"\"\"\n",
"\n",
"tweet = \"\"\"@concert Lady Gaga is actually at the Britney Spears Femme Fatale Concert tonight!!! She still listens to \n",
" her music!!!! WOW!!! #ladygaga #britney\"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# POS Tagging"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"POS Tagging is the process of assigning a grammatical category (known as *part of speech*, POS) to a word. For this purpose, the most common approach is using an annotated corpus such as Penn Treebank. The tag set (categories) depends on the corpus annotation. Fortunately, nltk defines a [universal tagset](http://www.nltk.org/book/ch05.html):\n",
"\n",
"\n",
"Tag\t| Meaning | English Examples\n",
"----|---------|------------------\n",
"ADJ\t| adjective | new, good, high, special, big, local\n",
"ADP\t| adposition | on, of, at, with, by, into, under\n",
"ADV\t| adverb | really, already, still, early, now\n",
"CONJ| conjunction | and, or, but, if, while, although\n",
"DET | determiner, article | the, a, some, most, every, no, which\n",
"NOUN | noun\t | year, home, costs, time, Africa\n",
"NUM\t| numeral | twenty-four, fourth, 1991, 14:24\n",
"PRT | particle | at, on, out, over per, that, up, with\n",
"PRON | pronoun | he, their, her, its, my, I, us\n",
"VERB | verb\t| is, say, told, given, playing, would\n",
". | punctuation marks | . , ; !\n",
"X | other | ersatz, esprit, dunno, gr8, univeristy"
]
},
{
"cell_type": "code",
"execution_count": 30,
"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"
]
}
],
"source": [
"from nltk import pos_tag, word_tokenize\n",
"print (pos_tag(word_tokenize(review), tagset='universal'))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We could have used another tagset for POS, such as UPenn."
]
},
{
"cell_type": "code",
"execution_count": 28,
"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$'), ('overall', 'JJ'), ('experience', 'NN'), ('with', 'IN'), ('this', 'DT'), ('monitor', 'NN'), ('was', 'VBD'), ('very', 'RB'), ('poor', 'JJ'), ('.', '.'), ('When', 'WRB'), ('the', 'DT'), ('screen', 'NN'), ('was', 'VBD'), (\"n't\", 'RB'), ('contracting', 'VBG'), ('or', 'CC'), ('glitching', 'VBG'), ('the', 'DT'), ('overall', 'JJ'), ('picture', 'NN'), ('quality', 'NN'), ('was', 'VBD'), ('poor', 'JJ'), ('to', 'TO'), ('fair', 'VB'), ('.', '.'), ('I', 'PRP'), (\"'ve\", 'VBP'), ('viewed', 'VBN'), ('numerous', 'JJ'), ('different', 'JJ'), ('monitor', 'NN'), ('models', 'NNS'), ('since', 'IN'), ('I', 'PRP'), (\"'m\", 'VBP'), ('a', 'DT'), ('college', 'NN'), ('student', 'NN'), ('at', 'IN'), ('UPM', 'NNP'), ('in', 'IN'), ('Madrid', 'NNP'), ('and', 'CC'), ('this', 'DT'), ('particular', 'JJ'), ('monitor', 'NN'), ('had', 'VBD'), ('as', 'IN'), ('poor', 'JJ'), ('of', 'IN'), ('picture', 'NN'), ('quality', 'NN'), ('as', 'IN'), ('any', 'DT'), ('I', 'PRP'), (\"'ve\", 'VBP'), ('seen', 'VBN'), ('.', '.')]\n"
]
}
],
"source": [
"print (pos_tag(word_tokenize(review)))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The meaning of these tags can be obtained here:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"$: 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"
]
}
],
"source": [
"nltk.help.upenn_tagset()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are going to use the Univeral tagset in this example. Based on this POS info, we could use correctly now the WordNetLemmatizer. The WordNetLemmatizer only is interesting for 4 POS categories: ADJ, ADV, NOUN, and VERB. This is because WordNet lemmatizer will only lemmatize adjectives, adverbs, nouns and verbs, and it needs that all the provided tags are in [n, a, r, v]."
]
},
{
"cell_type": "code",
"execution_count": 3,
"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"
]
}
],
"source": [
"from nltk.stem import WordNetLemmatizer\n",
"\n",
"review_postagged = pos_tag(word_tokenize(review), tagset='universal')\n",
"pos_mapping = {'NOUN': 'n', 'ADJ': 'a', 'VERB': 'v', 'ADV': 'r', 'ADP': 'n', 'CONJ': 'n', \n",
" 'PRON': 'n', 'NUM': 'n', 'X': 'n' }\n",
"\n",
"wordnet = WordNetLemmatizer()\n",
"lemmas = [wordnet.lemmatize(w, pos=pos_mapping[tag]) for (w,tag) in review_postagged if tag in pos_mapping.keys()]\n",
"print(lemmas)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NER"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Named Entity Recognition (NER) is an information retrieval for identifying named entities of places, organisation of persons. NER usually relies in a tagged corpus. NER algorithms can be trained for new corpora. Here we are using the Brown tagset (http://www.comp.leeds.ac.uk/ccalas/tagsets/brown.html)."
]
},
{
"cell_type": "code",
"execution_count": 4,
"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"
]
}
],
"source": [
"from nltk import ne_chunk, pos_tag, word_tokenize\n",
"ne_tagged = ne_chunk(pos_tag(word_tokenize(review)), binary=False)\n",
"print(ne_tagged) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"NLTK comes with other NER implementations. We can also use online services, such as [OpenCalais](http://www.opencalais.com/), [DBpedia Spotlight](https://github.com/dbpedia-spotlight/dbpedia-spotlight/wiki/Web-service) or [TagME](http://tagme.di.unipi.it/)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Parsing and Chunking"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Parsing** is the process of obtaining a parsing tree given a grammar. It which can be very useful to understand the relationship among the words.\n",
"\n",
"As we have seen in class, we can follow a traditional approach and obtain a full parsing tree or shallow parsing (chunking) and obtain a partial tree.\n",
"\n",
"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",
"\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",
"* Try to act as the parser. Instead of pressing 'step', press 'shift' and 'reduce'. Follow the 'always shift before reduce' rule. It is likely you will reach a state where the parser cannot proceed. You can go back with 'Undo'."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from nltk.app import srparser_app\n",
"srparser_app.app()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Chunking** o **shallow parsing** aims at extracting relevant parts of the sentence. There are (two main approaches)[http://www.nltk.org/book/ch07.html] to chunking: using regular expressions based on POS tags, or training a chunk parser.\n",
"\n",
"We are going to illustrate the first technique for extracting NP chunks.\n",
"\n",
"We define regular expressions for the chunks we want to get."
]
},
{
"cell_type": "code",
"execution_count": 6,
"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"
]
}
],
"source": [
"from nltk.chunk.regexp import *\n",
"pattern = \"\"\"NP: {<PRON><ADJ><NOUN>+} \n",
" {<DET>?<ADV>?<ADJ|NUM>*?<NOUN>+}\n",
" \"\"\"\n",
"NPChunker = RegexpParser(pattern)\n",
"\n",
"reviews_pos = (pos_tag(word_tokenize(review), tagset='universal'))\n",
"\n",
"chunks_np = NPChunker.parse(reviews_pos)\n",
"print(chunks_np)\n",
"chunks_np.draw()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can traverse the trees and obtain the strings as follows."
]
},
{
"cell_type": "code",
"execution_count": 7,
"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"
}
],
"source": [
"def extractTrees(parsed_tree, category='NP'):\n",
" return list(parsed_tree.subtrees(filter=lambda x: x.label()==category))\n",
"\n",
"extractTrees(chunks_np, 'NP')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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"
}
],
"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",
" \n",
"extractStrings(chunks_np)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## References\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* [NLTK Book. Natural Language Processing with Python. Steven Bird, Ewan Klein, and Edward Loper. O'Reilly Media, 2009 ](http://www.nltk.org/book_1ed/)\n",
"* [NLTK Essentials, Nitin Hardeniya, Packt Publishing, 2015](http://proquest.safaribooksonline.com/search?q=NLTK%20Essentials)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Licence"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The notebook is freely licensed under under the [Creative Commons Attribution Share-Alike license](https://creativecommons.org/licenses/by/2.0/). \n",
"\n",
"© 2016 Carlos A. Iglesias, Universidad Politécnica de Madrid."
]
}
],
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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