Added tag UPenn example

nlp/4_2_Syntactic_Processing.ipynb

@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "collapsed": false
    },
@@ -109,11 +109,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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'))"
@@ -123,16 +131,213 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "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."
+    "We could have used another tagset for POS, such as UPenn."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",
@@ -156,16 +361,115 @@
    "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."
+    "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": null,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",
@@ -206,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {
     "collapsed": false
    },
@@ -229,11 +533,90 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",
@@ -257,11 +640,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",
@@ -271,11 +680,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {
     "collapsed": false
    },
-   "outputs": [],
+   "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",