├── notebooks ├── conference_notebooks │ └── pycon_nlp │ │ ├── images │ │ ├── displacy_title.png │ │ ├── example_output.png │ │ ├── example_output_full.png │ │ └── syntax-dependencies-oliver.png │ │ ├── data │ │ └── article.txt │ │ ├── 00_spacy_intro.ipynb │ │ ├── 01_pride_and_predjudice.ipynb │ │ └── 02_rand_dataset.ipynb └── lightning_tour.ipynb ├── LICENSE └── README.md /notebooks/conference_notebooks/pycon_nlp/images/displacy_title.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/explosion/spacy-notebooks/HEAD/notebooks/conference_notebooks/pycon_nlp/images/displacy_title.png -------------------------------------------------------------------------------- /notebooks/conference_notebooks/pycon_nlp/images/example_output.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/explosion/spacy-notebooks/HEAD/notebooks/conference_notebooks/pycon_nlp/images/example_output.png -------------------------------------------------------------------------------- /notebooks/conference_notebooks/pycon_nlp/images/example_output_full.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/explosion/spacy-notebooks/HEAD/notebooks/conference_notebooks/pycon_nlp/images/example_output_full.png -------------------------------------------------------------------------------- /notebooks/conference_notebooks/pycon_nlp/images/syntax-dependencies-oliver.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/explosion/spacy-notebooks/HEAD/notebooks/conference_notebooks/pycon_nlp/images/syntax-dependencies-oliver.png -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2016 Explosion AI UG (haftungsbeschränkt) 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | # spaCy Jupyter notebooks 4 | 5 | An ongoing collection of [Jupyter](http://jupyter.org/) notebooks (formerly iPython notebooks) of easy-to-run [spaCy](https://github.com/explosion/spaCy) examples and tutorials. To get started, simply fork or clone this repository, navigate to the new directory and [run the notebook server](https://jupyter.readthedocs.io/en/latest/running.html): 6 | 7 | ```bash 8 | git clone https://github.com/explosion/spacy-notebooks 9 | cd spacy-notebooks 10 | jupyter notebook 11 | ``` 12 | 13 | ## Table of contents 14 | 15 | | Notebook | Content by | Notebook by | Description | 16 | | --- | --- | --- | --- | 17 | | [`lightning_tour`](notebooks/lightning_tour.ipynb) | [@explosion](https://github.com/explosion) | [@bhargavvader](https://github.com/bhargavvader) | An overview of spaCy's functionality and its usage. 18 | | [`pycon_nlp`](notebooks/conference_notebooks/pycon_nlp) | [@CytoraTech](https://github.com/cytora) | [@CytoraTech](https://github.com/cytora/pycon-nlp-in-10-lines) | spaCy introduction, and using spaCy to analyse Pride and Prejudice and the RAND dataset. Presented at PyCon UK 2016. 19 | | [`pydata_nlp`](notebooks/conference_notebooks/modern_nlp_in_python.ipynb) | [@skipgram](https://github.com/skipgram) | [@skipgram](https://github.com/skipgram/modern-nlp-in-python) | Introduction to NLP in python using spaCy and gensim. Presented at PyData DC 2016. 20 | | [`advanced_text_analysis`](notebooks/conference_notebooks/advanced_text_analysis.ipynb) | [@JonathanReeve](https://github.com/JonathanReeve) | [@JonathanReeve](https://github.com/JonathanReeve/advanced-text-analysis-workshop-2017) | Advanced Text Analysis with spaCy and Scikit-Learn. Presented at NYU during NYCDH Week 2017. 21 | 22 | ## Contribute a notebook 23 | 24 | Have you created a Jupyter notebook featuring spaCy, or have you converted one of the existing [examples](https://github.com/explosion/spaCy/tree/master/examples) or [tutorials](https://spacy.io/docs/usage)? We always appreciate [pull requests](https://github.com/explosion/spacy-notebooks) to this repository! 👍 25 | 26 | Please keep in mind that this repository is MIT-licensed, so we'll only be able to publish notebooks that are available under MIT or a more permissive license. 27 | -------------------------------------------------------------------------------- /notebooks/conference_notebooks/pycon_nlp/data/article.txt: -------------------------------------------------------------------------------- 1 | Saudi Arabia has suffered a string of deadly shootings and bomb attacks in recent months, many of which the Daesh (so-called IS) terrorist organization have claimed responsibility for. In July, suicide bombers struck three cities across Saudi Arabia, killing at least four security officers. The apparently coordinated attacks came on the penultimate day of the Muslim holy month of Ramadan. Recently, the Ministry of Interior foiled a planned suicide attack on a mosque in the Qatif region, in eastern Saudi Arabia, on Aug. 24. Indeed, the Ministry of Interior has successfully repelled hundreds of terrorist operations since 2003, displaying security expertise that is admired worldwide. Many countries have benefited from this experience in counter terrorism. However, success at security level does not correspond to a counter terrorist ideology that has seeped into our communities through social media or by any other means. 2 | 3 | The discourse and ideology of any terrorist organization, be it Al-Qaeda, Daesh or any other, is almost identical regardless of the form it takes or the time in which it exists. This discourse is characterized in three ways: 4 | 5 | Firstly, it is dogmatic; identified by intolerance and rejection of any idea of ‘the other’. Dogmatic personalities refuse to enter into discussion, even if their ideas are proved to be faulty, as they think their own ideas are prescribed by God and thus have the overtones of holiness, which makes them categorically incontestable as ‘the absolute truth’. Hence, this is an advanced case of intellectual recession and the absence of scientific critique. 6 | 7 | Secondly, it is utopian; evoking concepts of ‘Ideal City’ similar to ‘Caliphate State’ or ‘Rightly Guided Caliphate’, forms a cultural and religious component of the Arab and Islamic mindset. Ever since the fall of the Ottoman Empire, ‘restoration of the Islamic Caliphate’ has been behind the emergence of many hard-line Islamic organizations. These organizations claim that they deserve to raise the flag of the Islamic Caliphate; governed by a single Caliph who rules Muslims in the East and in the West, abolishing all positive ‘nation states’ set out according to the Sykes-Picot Agreement. Hard-line Islamic organizations include the Muslim Brotherhood in Egypt, the Gulen Movement in Turkey, Jamaat-e-Islami in India, Al-Qaeda, and Daesh. 8 | 9 | Thirdly, it is dystopian; it rejects contemporary reality as it perceives all communities as ignorant and all regimes as infidels governed by tyrants who impose laws that have nothing to do with religion. In the aftermath of the 9/11 attacks, Al-Qaeda raised the banner of excommunication of communities, regimes, and scholars. This organization caused damage to many countries, especially Saudi Arabia. However, the power of the Saudi state security system eliminated Al-Qaeda, dramatically undermined its capabilities, and forced it to retreat to Yemen, where a political and institutional vacuum prevails. Today, Daesh is adopting the same discourse by escalating excommunication, terrorism, and violence, which seem to be interconnected. 10 | 11 | It is necessary to deconstruct terrorist discourse, refute its suspicions at an intellectual level, and clarify their deviated citation of religious texts to promote their ideology. Official and non-official religious institutions must take the responsibility for refuting such suspicions by various means and on all platforms. They must also take the responsibility for working on the establishment of moderate centric thought. It is also necessary to contain the youth through modern religious discourse to correct misconceptions of restoring a utopian Islamic Caliphate, and promote national pertinence in education, the media, and social media. There are ongoing efforts, worthy of mention that are working well in deconstructing such a discourse including the Mohammad Bin Naif Counseling and Care Centre, the Assakina campaign for dialogue, the Hedaya Centre, and the Sawab Centre in the UAE. However, all Gulf, Arab, and even international efforts must be concerted to counter terrorism, as it is a job that has to be undertaken collectively because terrorism represents a renewed international threat that recognizes no place, religion, nationality, color, or ethnicity. -------------------------------------------------------------------------------- /notebooks/lightning_tour.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Lightning Tour\n", 8 | "\n", 9 | "This is a tutorial notebook of the [Lightning Tour](https://spacy.io/docs/usage/lightning-tour) page.\n", 10 | "\n", 11 | "The following examples and code snippets give you an overview of spaCy's functionality and its usage.\n", 12 | "\n", 13 | "### Load resources and process text" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 1, 19 | "metadata": { 20 | "collapsed": false 21 | }, 22 | "outputs": [], 23 | "source": [ 24 | "import spacy\n", 25 | "# this is to make sure we get no unicode based errors\n", 26 | "from __future__ import unicode_literals\n", 27 | "\n", 28 | "en_nlp = spacy.load('en')\n", 29 | "de_nlp = spacy.load('de')\n", 30 | "en_doc = en_nlp(u'Hello, world. Here are two sentences.')\n", 31 | "de_doc = de_nlp(u'ich bin ein Berliner.')" 32 | ] 33 | }, 34 | { 35 | "cell_type": "markdown", 36 | "metadata": {}, 37 | "source": [ 38 | "### Multi-threaded generator" 39 | ] 40 | }, 41 | { 42 | "cell_type": "code", 43 | "execution_count": 2, 44 | "metadata": { 45 | "collapsed": false 46 | }, 47 | "outputs": [], 48 | "source": [ 49 | "texts = [u'One document.', u'...', u'Lots of documents']\n", 50 | "# .pipe streams input, and produces streaming output\n", 51 | "iter_texts = (texts[i % 3] for i in xrange(100000000))\n", 52 | "for i, doc in enumerate(en_nlp.pipe(iter_texts, batch_size=50, n_threads=4)):\n", 53 | " assert doc.is_parsed\n", 54 | " if i == 100:\n", 55 | " break" 56 | ] 57 | }, 58 | { 59 | "cell_type": "markdown", 60 | "metadata": {}, 61 | "source": [ 62 | "### Get tokens and sentences" 63 | ] 64 | }, 65 | { 66 | "cell_type": "code", 67 | "execution_count": 3, 68 | "metadata": { 69 | "collapsed": false 70 | }, 71 | "outputs": [], 72 | "source": [ 73 | "token = en_doc[0]\n", 74 | "sentence = next(en_doc.sents)\n", 75 | "assert token is sentence[0]\n", 76 | "assert sentence.text == 'Hello, world.'" 77 | ] 78 | }, 79 | { 80 | "cell_type": "markdown", 81 | "metadata": {}, 82 | "source": [ 83 | "### Use integer IDs for any string" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 4, 89 | "metadata": { 90 | "collapsed": false 91 | }, 92 | "outputs": [], 93 | "source": [ 94 | "hello_id = en_nlp.vocab.strings['Hello']\n", 95 | "hello_str = en_nlp.vocab.strings[hello_id]\n", 96 | "\n", 97 | "assert token.orth == hello_id == 6747\n", 98 | "assert token.orth_ == hello_str == 'Hello'" 99 | ] 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "metadata": {}, 104 | "source": [ 105 | "### Get and set string views and flags" 106 | ] 107 | }, 108 | { 109 | "cell_type": "code", 110 | "execution_count": 5, 111 | "metadata": { 112 | "collapsed": false 113 | }, 114 | "outputs": [], 115 | "source": [ 116 | "assert token.shape_ == u\"Xxxxx\"\n", 117 | "for lexeme in en_nlp.vocab:\n", 118 | " if lexeme.is_alpha:\n", 119 | " lexeme.shape_ = 'W'\n", 120 | " elif lexeme.is_digit:\n", 121 | " lexeme.shape_ = 'D'\n", 122 | " elif lexeme.is_punct:\n", 123 | " lexeme.shape_ = 'P'\n", 124 | " else:\n", 125 | " lexeme.shape_ = 'M'\n", 126 | "assert token.shape_ == 'W'" 127 | ] 128 | }, 129 | { 130 | "cell_type": "markdown", 131 | "metadata": {}, 132 | "source": [ 133 | "### Export to numpy arrays" 134 | ] 135 | }, 136 | { 137 | "cell_type": "code", 138 | "execution_count": 6, 139 | "metadata": { 140 | "collapsed": true 141 | }, 142 | "outputs": [], 143 | "source": [ 144 | "from spacy.attrs import ORTH, LIKE_URL, IS_OOV\n", 145 | "\n", 146 | "attr_ids = [ORTH, LIKE_URL, IS_OOV]\n", 147 | "doc_array = en_doc.to_array(attr_ids)\n", 148 | "assert doc_array.shape == (len(en_doc), len(attr_ids))\n", 149 | "assert en_doc[0].orth == doc_array[0, 0]\n", 150 | "assert en_doc[1].orth == doc_array[1, 0]\n", 151 | "assert en_doc[0].like_url == doc_array[0, 1]\n", 152 | "assert list(doc_array[:, 1]) == [t.like_url for t in en_doc]" 153 | ] 154 | }, 155 | { 156 | "cell_type": "markdown", 157 | "metadata": {}, 158 | "source": [ 159 | "### Word vectors" 160 | ] 161 | }, 162 | { 163 | "cell_type": "code", 164 | "execution_count": 7, 165 | "metadata": { 166 | "collapsed": false 167 | }, 168 | "outputs": [], 169 | "source": [ 170 | "doc = en_nlp(u\"Apples and oranges are similar. Boots and hippos aren't.\")\n", 171 | "\n", 172 | "apples = doc[0]\n", 173 | "oranges = doc[2]\n", 174 | "boots = doc[6]\n", 175 | "hippos = doc[8]\n", 176 | "\n", 177 | "assert apples.similarity(oranges) > boots.similarity(hippos)" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": {}, 183 | "source": [ 184 | "### Part-of-speech tags" 185 | ] 186 | }, 187 | { 188 | "cell_type": "code", 189 | "execution_count": 8, 190 | "metadata": { 191 | "collapsed": false 192 | }, 193 | "outputs": [], 194 | "source": [ 195 | "from spacy.parts_of_speech import ADV\n", 196 | "\n", 197 | "def is_adverb(token):\n", 198 | " return token.pos == spacy.parts_of_speech.ADV\n", 199 | "\n", 200 | "# These are data-specific, so no constants are provided. You have to look\n", 201 | "# up the IDs from the StringStore.\n", 202 | "NNS = en_nlp.vocab.strings['NNS']\n", 203 | "NNPS = en_nlp.vocab.strings['NNPS']\n", 204 | "def is_plural_noun(token):\n", 205 | " return token.tag == NNS or token.tag == NNPS\n", 206 | "\n", 207 | "def print_coarse_pos(token):\n", 208 | " print(token.pos_)\n", 209 | "\n", 210 | "def print_fine_pos(token):\n", 211 | " print(token.tag_)" 212 | ] 213 | }, 214 | { 215 | "cell_type": "markdown", 216 | "metadata": {}, 217 | "source": [ 218 | "### Syntactic dependencies" 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": 9, 224 | "metadata": { 225 | "collapsed": true 226 | }, 227 | "outputs": [], 228 | "source": [ 229 | "def dependency_labels_to_root(token):\n", 230 | " '''Walk up the syntactic tree, collecting the arc labels.'''\n", 231 | " dep_labels = []\n", 232 | " while token.head is not token:\n", 233 | " dep_labels.append(token.dep)\n", 234 | " token = token.head\n", 235 | " return dep_labels" 236 | ] 237 | }, 238 | { 239 | "cell_type": "markdown", 240 | "metadata": {}, 241 | "source": [ 242 | "### Named entities" 243 | ] 244 | }, 245 | { 246 | "cell_type": "code", 247 | "execution_count": 10, 248 | "metadata": { 249 | "collapsed": true 250 | }, 251 | "outputs": [], 252 | "source": [ 253 | "def iter_products(docs):\n", 254 | " for doc in docs:\n", 255 | " for ent in doc.ents:\n", 256 | " if ent.label_ == 'PRODUCT':\n", 257 | " yield ent\n", 258 | "\n", 259 | "def word_is_in_entity(word):\n", 260 | " return word.ent_type != 0\n", 261 | "\n", 262 | "def count_parent_verb_by_person(docs):\n", 263 | " counts = defaultdict(lambda: defaultdict(int))\n", 264 | " for doc in docs:\n", 265 | " for ent in doc.ents:\n", 266 | " if ent.label_ == 'PERSON' and ent.root.head.pos == VERB:\n", 267 | " counts[ent.orth_][ent.root.head.lemma_] += 1\n", 268 | " return counts" 269 | ] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "metadata": {}, 274 | "source": [ 275 | "### Calculate inline mark-up on original string" 276 | ] 277 | }, 278 | { 279 | "cell_type": "code", 280 | "execution_count": 11, 281 | "metadata": { 282 | "collapsed": true 283 | }, 284 | "outputs": [], 285 | "source": [ 286 | "def put_spans_around_tokens(doc, get_classes):\n", 287 | " '''Given some function to compute class names, put each token in a\n", 288 | " span element, with the appropriate classes computed.\n", 289 | "\n", 290 | " All whitespace is preserved, outside of the spans. (Yes, I know HTML\n", 291 | " won't display it. But the point is no information is lost, so you can\n", 292 | " calculate what you need, e.g. tags, tags, etc.)\n", 293 | " '''\n", 294 | " output = []\n", 295 | " template = '{word}{space}'\n", 296 | " for token in doc:\n", 297 | " if token.is_space:\n", 298 | " output.append(token.orth_)\n", 299 | " else:\n", 300 | " output.append(\n", 301 | " template.format(\n", 302 | " classes=' '.join(get_classes(token)),\n", 303 | " word=token.orth_,\n", 304 | " space=token.whitespace_))\n", 305 | " string = ''.join(output)\n", 306 | " string = string.replace('\\n', '')\n", 307 | " string = string.replace('\\t', ' ')\n", 308 | " return string" 309 | ] 310 | }, 311 | { 312 | "cell_type": "markdown", 313 | "metadata": {}, 314 | "source": [ 315 | "### Efficient binary serialization" 316 | ] 317 | }, 318 | { 319 | "cell_type": "code", 320 | "execution_count": 12, 321 | "metadata": { 322 | "collapsed": true 323 | }, 324 | "outputs": [], 325 | "source": [ 326 | "from spacy.tokens.doc import Doc\n", 327 | "\n", 328 | "byte_string = doc.to_bytes()\n", 329 | "open('moby_dick.bin', 'wb').write(byte_string)\n", 330 | "\n", 331 | "nlp = spacy.load('en')\n", 332 | "for byte_string in Doc.read_bytes(open('moby_dick.bin', 'rb')):\n", 333 | " doc = Doc(nlp.vocab)\n", 334 | " doc.from_bytes(byte_string)" 335 | ] 336 | } 337 | ], 338 | "metadata": { 339 | "kernelspec": { 340 | "display_name": "Python 2", 341 | "language": "python", 342 | "name": "python2" 343 | }, 344 | "language_info": { 345 | "codemirror_mode": { 346 | "name": "ipython", 347 | "version": 2 348 | }, 349 | "file_extension": ".py", 350 | "mimetype": "text/x-python", 351 | "name": "python", 352 | "nbconvert_exporter": "python", 353 | "pygments_lexer": "ipython2", 354 | "version": "2.7.12" 355 | } 356 | }, 357 | "nbformat": 4, 358 | "nbformat_minor": 2 359 | } 360 | -------------------------------------------------------------------------------- /notebooks/conference_notebooks/pycon_nlp/00_spacy_intro.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# [spaCy](http://spacy.io/docs/#examples) introduction" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "## Load spaCy resources" 15 | ] 16 | }, 17 | { 18 | "cell_type": "code", 19 | "execution_count": null, 20 | "metadata": { 21 | "collapsed": false 22 | }, 23 | "outputs": [], 24 | "source": [ 25 | "# Import spacy and English models\n", 26 | "import spacy\n", 27 | "\n", 28 | "nlp = spacy.load('en')" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "Loading spaCy can take a while, in the meantime here are a few definitions to help you on your NLP journey.\n", 36 | "\n", 37 | "#### What are Stop Words?\n", 38 | "\n", 39 | "Stop words are the common words in a vocabulary which are of little value when considering word frequencies in text. This is because they don't provide much useful information about what the sentence is telling the reader.\n", 40 | "\n", 41 | "Example: _\"the\",\"and\",\"a\",\"are\",\"is\"_\n", 42 | "\n", 43 | "#### What is a Corpus?\n", 44 | "\n", 45 | "A corpus (plural: corpora) is a large collection of text or documents and can provide useful training data for NLP models. A corpus might be built from transcribed speech or a collection of manuscripts. Each item in a corpus is not necessarily unique and frequency counts of words can assist in uncovering the structure in a corpus.\n", 46 | "\n", 47 | "Examples:\n", 48 | "\n", 49 | "1. Every word written in the complete works of Shakespeare\n", 50 | "2. Every word spoken on BBC Radio channels for the past 30 years " 51 | ] 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "metadata": {}, 56 | "source": [ 57 | "## Process text" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": null, 63 | "metadata": { 64 | "collapsed": false 65 | }, 66 | "outputs": [], 67 | "source": [ 68 | "# Process sentences 'Hello, world. Natural Language Processing in 10 lines of code.' using spaCy\n", 69 | "doc = nlp(u'Hello, world. Natural Language Processing in 10 lines of code.')" 70 | ] 71 | }, 72 | { 73 | "cell_type": "markdown", 74 | "metadata": {}, 75 | "source": [ 76 | "## Get tokens and sentences\n", 77 | "\n", 78 | "#### What is a Token?\n", 79 | "A token is a single chopped up element of the sentence, which could be a word or a group of words to analyse. The task of chopping the sentence up is called \"tokenisation\".\n", 80 | "\n", 81 | "Example: The following sentence can be tokenised by splitting up the sentence into individual words.\n", 82 | "\n", 83 | "\t\"Cytora is going to PyCon!\"\n", 84 | "\t[\"Cytora\",\"is\",\"going\",\"to\",\"PyCon!\"]" 85 | ] 86 | }, 87 | { 88 | "cell_type": "code", 89 | "execution_count": null, 90 | "metadata": { 91 | "collapsed": false 92 | }, 93 | "outputs": [], 94 | "source": [ 95 | "# Get first token of the processed document\n", 96 | "token = doc[0]\n", 97 | "print(token)\n", 98 | "\n", 99 | "# Print sentences (one sentence per line)\n", 100 | "for sent in doc.sents:\n", 101 | " print(sent)" 102 | ] 103 | }, 104 | { 105 | "cell_type": "markdown", 106 | "metadata": {}, 107 | "source": [ 108 | "## Part of speech tags\n", 109 | "\n", 110 | "#### What is a Speech Tag?\n", 111 | "A speech tag is a context sensitive description of what a word means in the context of the whole sentence.\n", 112 | "More information about the kinds of speech tags which are used in NLP can be [found here](http://www.winwaed.com/blog/2011/11/08/part-of-speech-tags/).\n", 113 | "\n", 114 | "Examples:\n", 115 | "\n", 116 | "1. CARDINAL, Cardinal Number - 1,2,3\n", 117 | "2. PROPN, Proper Noun, Singular - \"Matic\", \"Andraz\", \"Cardiff\"\n", 118 | "3. INTJ, Interjection - \"Uhhhhhhhhhhh\"" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": null, 124 | "metadata": { 125 | "collapsed": false 126 | }, 127 | "outputs": [], 128 | "source": [ 129 | "# For each token, print corresponding part of speech tag\n", 130 | "for token in doc:\n", 131 | " print('{} - {}'.format(token, token.pos_))" 132 | ] 133 | }, 134 | { 135 | "cell_type": "markdown", 136 | "metadata": {}, 137 | "source": [ 138 | "## Visual part of speech tagging ([displaCy](https://displacy.spacy.io))" 139 | ] 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "metadata": {}, 144 | "source": [ 145 | "## Syntactic dependencies\n", 146 | "\n", 147 | "#### What are syntactic dependencies?\n", 148 | "\n", 149 | "We have the speech tags and we have all of the tokens in a sentence, but how do we relate the two to uncover the syntax in a sentence? Syntactic dependencies describe how each type of word relates to each other in a sentence, this is important in NLP in order to extract structure and understand grammar in plain text.\n", 150 | "\n", 151 | "Example:\n", 152 | "\n", 153 | "" 154 | ] 155 | }, 156 | { 157 | "cell_type": "code", 158 | "execution_count": null, 159 | "metadata": { 160 | "collapsed": false 161 | }, 162 | "outputs": [], 163 | "source": [ 164 | "# Write a function that walks up the syntactic tree of the given token and collects all tokens to the root token (including root token).\n", 165 | "\n", 166 | "def tokens_to_root(token):\n", 167 | " \"\"\"\n", 168 | " Walk up the syntactic tree, collecting tokens to the root of the given `token`.\n", 169 | " :param token: Spacy token\n", 170 | " :return: list of Spacy tokens\n", 171 | " \"\"\"\n", 172 | " tokens_to_r = []\n", 173 | " while token.head is not token:\n", 174 | " tokens_to_r.append(token)\n", 175 | " token = token.head\n", 176 | " tokens_to_r.append(token)\n", 177 | "\n", 178 | " return tokens_to_r\n", 179 | "\n", 180 | "# For every token in document, print it's tokens to the root\n", 181 | "for token in doc:\n", 182 | " print('{} --> {}'.format(token, tokens_to_root(token)))\n", 183 | "\n", 184 | "# Print dependency labels of the tokens\n", 185 | "for token in doc:\n", 186 | " print('-> '.join(['{}-{}'.format(dependent_token, dependent_token.dep_) for dependent_token in tokens_to_root(token)]))\n" 187 | ] 188 | }, 189 | { 190 | "cell_type": "markdown", 191 | "metadata": {}, 192 | "source": [ 193 | "## Named entities\n", 194 | "\n", 195 | "#### Named Entities\n", 196 | "\n", 197 | "A named entity is any real world object such as a person, location, organisation or product with a proper name. \n", 198 | "\n", 199 | "Example:\n", 200 | "\n", 201 | "\t1. Barack Obama\n", 202 | "\t2. Edinburgh\n", 203 | "\t3. Ferrari Enzo" 204 | ] 205 | }, 206 | { 207 | "cell_type": "code", 208 | "execution_count": null, 209 | "metadata": { 210 | "collapsed": false 211 | }, 212 | "outputs": [], 213 | "source": [ 214 | "# Print all named entities with named entity types\n", 215 | "\n", 216 | "doc_2 = nlp(u\"I went to Paris where I met my old friend Jack from uni.\")\n", 217 | "for ent in doc_2.ents:\n", 218 | " print('{} - {}'.format(ent, ent.label_))" 219 | ] 220 | }, 221 | { 222 | "cell_type": "markdown", 223 | "metadata": {}, 224 | "source": [ 225 | "## Noun chunks\n", 226 | "\n", 227 | "#### What is a Noun Chunk?\n", 228 | "Noun chunks are the phrases based upon nouns recovered from tokenized text using the speech tags.\n", 229 | "\n", 230 | "Example:\n", 231 | "\n", 232 | "The sentence \"The boy saw the yellow dog\" has 2 noun objects, the boy and the dog. \n", 233 | "Therefore the noun chunks will be\n", 234 | "\n", 235 | "\t1. \"The boy\"\n", 236 | "\t2. \"the yellow dog\"" 237 | ] 238 | }, 239 | { 240 | "cell_type": "code", 241 | "execution_count": null, 242 | "metadata": { 243 | "collapsed": false 244 | }, 245 | "outputs": [], 246 | "source": [ 247 | "# Print noun chunks for doc_2\n", 248 | "print([chunk for chunk in doc_2.noun_chunks])" 249 | ] 250 | }, 251 | { 252 | "cell_type": "markdown", 253 | "metadata": {}, 254 | "source": [ 255 | "## Unigram probabilities" 256 | ] 257 | }, 258 | { 259 | "cell_type": "code", 260 | "execution_count": null, 261 | "metadata": { 262 | "collapsed": false 263 | }, 264 | "outputs": [], 265 | "source": [ 266 | "# For every token in doc_2, print log-probability of the word, estimated from counts from a large corpus \n", 267 | "for token in doc_2:\n", 268 | " print(token, ',', token.prob)" 269 | ] 270 | }, 271 | { 272 | "cell_type": "markdown", 273 | "metadata": {}, 274 | "source": [ 275 | "## Word embedding / Similarity\n", 276 | "\n", 277 | "#### What are Word embeddings?\n", 278 | "\n", 279 | "A word embedding is a representation of a word, and by extension a whole language corpus, in a vector or other form of numerical mapping. This allows words to be treated numerically with word similarity represented as spatial difference in the dimensions of the word embedding mapping.\n", 280 | "\n", 281 | "Example:\n", 282 | "\t\n", 283 | "With word embeddings we can understand that vector operations describe word similarity. This means that we can see vector proofs of statements such as:\n", 284 | "\n", 285 | "\tking-queen==man-woman" 286 | ] 287 | }, 288 | { 289 | "cell_type": "code", 290 | "execution_count": null, 291 | "metadata": { 292 | "collapsed": false 293 | }, 294 | "outputs": [], 295 | "source": [ 296 | "# For a given document, calculate similarity between 'apples' and 'oranges' and 'boots' and 'hippos'\n", 297 | "doc = nlp(u\"Apples and oranges are similar. Boots and hippos aren't.\")\n", 298 | "apples = doc[0]\n", 299 | "oranges = doc[2]\n", 300 | "boots = doc[6]\n", 301 | "hippos = doc[8]\n", 302 | "print(apples.similarity(oranges))\n", 303 | "print(boots.similarity(hippos))\n", 304 | "\n", 305 | "print()\n", 306 | "# Print similarity between sentence and word 'fruit'\n", 307 | "apples_sent, boots_sent = doc.sents\n", 308 | "fruit = doc.vocab[u'fruit']\n", 309 | "print(apples_sent.similarity(fruit))\n", 310 | "print(boots_sent.similarity(fruit))" 311 | ] 312 | }, 313 | { 314 | "cell_type": "markdown", 315 | "metadata": {}, 316 | "source": [] 317 | } 318 | ], 319 | "metadata": { 320 | "kernelspec": { 321 | "display_name": "Python 2", 322 | "language": "python", 323 | "name": "python2" 324 | }, 325 | "language_info": { 326 | "codemirror_mode": { 327 | "name": "ipython", 328 | "version": 2 329 | }, 330 | "file_extension": ".py", 331 | "mimetype": "text/x-python", 332 | "name": "python", 333 | "nbconvert_exporter": "python", 334 | "pygments_lexer": "ipython2", 335 | "version": "2.7.10" 336 | } 337 | }, 338 | "nbformat": 4, 339 | "nbformat_minor": 1 340 | } 341 | -------------------------------------------------------------------------------- /notebooks/conference_notebooks/pycon_nlp/01_pride_and_predjudice.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Pride & Prejudice analysis" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "# Real text analysis\n", 15 | "\n", 16 | "We got familiar with Spacy. In the next section we are going to analyse a real text (Pride & Prejudice). \n", 17 | "\n", 18 | "We would like to:\n", 19 | "* Extract the names of all the characters from the book (e.g. Elizabeth, Darcy, Bingley)\n", 20 | "* Visualize characters' occurences with regards to relative position in the book\n", 21 | "* Authomatically describe any character from the book\n", 22 | "* Find out which characters have been mentioned in a context of marriage\n", 23 | "* Build keywords extraction that could be used to display a word cloud ([example](http://www.cytora.com/data-samples.html))" 24 | ] 25 | }, 26 | { 27 | "cell_type": "markdown", 28 | "metadata": {}, 29 | "source": [ 30 | "## Load text file" 31 | ] 32 | }, 33 | { 34 | "cell_type": "code", 35 | "execution_count": 1, 36 | "metadata": { 37 | "collapsed": false 38 | }, 39 | "outputs": [], 40 | "source": [ 41 | "def read_file(file_name):\n", 42 | " with open(file_name, 'r') as file:\n", 43 | " return file.read().decode('utf-8')" 44 | ] 45 | }, 46 | { 47 | "cell_type": "markdown", 48 | "metadata": {}, 49 | "source": [ 50 | "## Process full text" 51 | ] 52 | }, 53 | { 54 | "cell_type": "code", 55 | "execution_count": 2, 56 | "metadata": { 57 | "collapsed": false 58 | }, 59 | "outputs": [], 60 | "source": [ 61 | "import spacy\n", 62 | "\n", 63 | "nlp = spacy.load('en')\n", 64 | "\n", 65 | "# Process `text` with Spacy NLP Parser\n", 66 | "text = read_file('data/pride_and_prejudice.txt')\n", 67 | "processed_text = nlp(text)" 68 | ] 69 | }, 70 | { 71 | "cell_type": "code", 72 | "execution_count": 3, 73 | "metadata": { 74 | "collapsed": false 75 | }, 76 | "outputs": [ 77 | { 78 | "name": "stdout", 79 | "output_type": "stream", 80 | "text": [ 81 | "6138\n", 82 | "[\"My dear Mr. Bennet,\" said his lady to him one day, \"have you heard that\n", 83 | "Netherfield Park is let at last?\"\n", 84 | "\n", 85 | ", Mr. Bennet replied that he had not.\n", 86 | "\n", 87 | "\", But it is,\" returned she; \"for Mrs. Long has just been here, and she\n", 88 | "told me all about it.\"\n", 89 | "\n", 90 | ", Mr. Bennet made no answer.\n", 91 | "\n", 92 | ", \"Do you not want to know who has taken it?\" cried his wife impatiently.\n", 93 | "\n", 94 | "]\n" 95 | ] 96 | } 97 | ], 98 | "source": [ 99 | "# How many sentences are in the book (Pride & Prejudice)?\n", 100 | "sentences = [s for s in processed_text.sents]\n", 101 | "print(len(sentences))\n", 102 | "\n", 103 | "# Print sentences from index 10 to index 15, to make sure that we have parsed the correct book\n", 104 | "print(sentences[10:15])" 105 | ] 106 | }, 107 | { 108 | "cell_type": "markdown", 109 | "metadata": {}, 110 | "source": [ 111 | "## Find all the personal names" 112 | ] 113 | }, 114 | { 115 | "cell_type": "code", 116 | "execution_count": 4, 117 | "metadata": { 118 | "collapsed": false 119 | }, 120 | "outputs": [ 121 | { 122 | "name": "stdout", 123 | "output_type": "stream", 124 | "text": [ 125 | "[(u'elizabeth', 622), (u'darcy', 312), (u'jane', 286), (u'bennet', 266), (u'bingley', 204), (u'wickham', 183), (u'collins', 178), (u'lydia', 162), (u'lizzy', 94), (u'gardiner', 92), (u'lady catherine', 71), (u'kitty', 71), (u'mary', 36), (u'william', 33), (u'hurst', 32), (u'phillips', 30), (u'forster', 26), (u'longbourn', 26), (u'miss bingley', 25), (u'lucas', 21)]\n" 126 | ] 127 | } 128 | ], 129 | "source": [ 130 | "# Extract all the personal names from Pride & Prejudice and count their occurrences. \n", 131 | "# Expected output is a list in the following form: [('elizabeth', 622), ('darcy', 312), ('jane', 286), ('bennet', 266) ...].\n", 132 | "\n", 133 | "from collections import Counter, defaultdict\n", 134 | "\n", 135 | "def find_character_occurences(doc):\n", 136 | " \"\"\"\n", 137 | " Return a list of actors from `doc` with corresponding occurences.\n", 138 | " \n", 139 | " :param doc: Spacy NLP parsed document\n", 140 | " :return: list of tuples in form\n", 141 | " [('elizabeth', 622), ('darcy', 312), ('jane', 286), ('bennet', 266)]\n", 142 | " \"\"\"\n", 143 | " \n", 144 | " characters = Counter()\n", 145 | " for ent in processed_text.ents:\n", 146 | " if ent.label_ == 'PERSON':\n", 147 | " characters[ent.lemma_] += 1\n", 148 | " \n", 149 | " return characters.most_common()\n", 150 | "\n", 151 | "print(find_character_occurences(processed_text)[:20])" 152 | ] 153 | }, 154 | { 155 | "cell_type": "markdown", 156 | "metadata": {}, 157 | "source": [ 158 | "## Plot characters personal names as a time series " 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 5, 164 | "metadata": { 165 | "collapsed": false 166 | }, 167 | "outputs": [ 168 | { 169 | "name": "stderr", 170 | "output_type": "stream", 171 | "text": [ 172 | "/Users/andrazhribernik/cytora/pycon-nlp-in-10-lines/venv/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n", 173 | " warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n" 174 | ] 175 | } 176 | ], 177 | "source": [ 178 | "# Matplotlib Jupyter HACK\n", 179 | "%matplotlib inline\n", 180 | "\n", 181 | "import matplotlib\n", 182 | "import numpy as np\n", 183 | "import matplotlib.pyplot as plt" 184 | ] 185 | }, 186 | { 187 | "cell_type": "code", 188 | "execution_count": 6, 189 | "metadata": { 190 | "collapsed": false 191 | }, 192 | "outputs": [], 193 | "source": [ 194 | "# Plot characters' mentions as a time series relative to the position of the actor's occurrence in a book.\n", 195 | "\n", 196 | "def get_character_offsets(doc):\n", 197 | " \"\"\"\n", 198 | " For every character in a `doc` collect all the occurences offsets and store them into a list. \n", 199 | " The function returns a dictionary that has actor lemma as a key and list of occurences as a value for every character.\n", 200 | " \n", 201 | " :param doc: Spacy NLP parsed document\n", 202 | " :return: dict object in form\n", 203 | " {'elizabeth': [123, 543, 4534], 'darcy': [205, 2111]}\n", 204 | " \"\"\"\n", 205 | " \n", 206 | " character_offsets = defaultdict(list)\n", 207 | " for ent in doc.ents:\n", 208 | " if ent.label_ == 'PERSON':\n", 209 | " character_offsets[ent.lemma_].append(ent.start)\n", 210 | " \n", 211 | " return dict(character_offsets)\n", 212 | "\n", 213 | "character_occurences = get_character_offsets(processed_text)" 214 | ] 215 | }, 216 | { 217 | "cell_type": "code", 218 | "execution_count": 7, 219 | "metadata": { 220 | "collapsed": false 221 | }, 222 | "outputs": [ 223 | { 224 | "data": { 225 | "image/png": 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jI6H1z3MTgDkj9Lh7TTUamnwDL4C5O234+9gUZCp09Ih0556fvdUufFciv0n4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226 | "text/plain": [ 227 | "" 228 | ] 229 | }, 230 | "metadata": {}, 231 | "output_type": "display_data" 232 | } 233 | ], 234 | "source": [ 235 | "from matplotlib.pyplot import hist\n", 236 | "from cycler import cycler\n", 237 | "\n", 238 | "NUM_BINS = 10\n", 239 | "\n", 240 | "def normalize(occurencies, normalization_constant):\n", 241 | " return [o / float(len(processed_text)) for o in occurencies]\n", 242 | "\n", 243 | "def plot_character_timeseries(character_offsets, character_labels, normalization_constant=None):\n", 244 | " \"\"\"\n", 245 | " Plot characters' personal names specified in `character_labels` list as time series.\n", 246 | " \n", 247 | " :param character_offsets: dict object in form {'elizabeth': [123, 543, 4534], 'darcy': [205, 2111]}\n", 248 | " :param character_labels: list of strings that should match some of the keys in `character_offsets`\n", 249 | " :param normalization_constant: int\n", 250 | " \"\"\"\n", 251 | " x = [character_offsets[character_label] for character_label in character_labels] \n", 252 | " \n", 253 | " with plt.style.context('fivethirtyeight'):\n", 254 | " plt.figure()\n", 255 | " n, bins, patches = plt.hist(x, NUM_BINS, label=character_labels)\n", 256 | " plt.clf()\n", 257 | " \n", 258 | " ax = plt.subplot(111)\n", 259 | " for i, a in enumerate(n):\n", 260 | " ax.plot([float(x) / (NUM_BINS - 1) for x in range(len(a))], a, label=character_labels[i])\n", 261 | " \n", 262 | " matplotlib.rcParams['axes.prop_cycle'] = cycler(color=['r','k','c','b','y','m','g','#54a1FF'])\n", 263 | " ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n", 264 | "\n", 265 | "#plot_character_timeseries(character_occurences, ['darcy', 'bingley'], normalization_constant=len(processed_text))\n", 266 | "plot_character_timeseries(character_occurences, ['darcy', 'bingley'])" 267 | ] 268 | }, 269 | { 270 | "cell_type": "markdown", 271 | "metadata": {}, 272 | "source": [ 273 | "## Spacy parse tree in action" 274 | ] 275 | }, 276 | { 277 | "cell_type": "code", 278 | "execution_count": 8, 279 | "metadata": { 280 | "collapsed": false 281 | }, 282 | "outputs": [ 283 | { 284 | "name": "stdout", 285 | "output_type": "stream", 286 | "text": [ 287 | "[u'silent', u'such', u'much', u'his', u'his', u'what', u'charming', u'young', u'grave', u'her', u'his', u'particular', u'disappointing', u'late', u'late', u'late', u'late', u'intimate', u'confidential', u'present', u'her', u'much', u'his', u'her', u'your', u'late', u'late', u'present', u'abominable', u'all', u'late', u'poor', u'handsome', u'her', u'last', u'her', u'her', u'little', u'his', u'disagreeable', u'clever', u'worth', u'little', u'grow', u'studious', u'sorry', u'unworthy', u'answerable', u'unappeasable', u'impatient', u'ashamed', u'kind', u'proud', u'tall', u'punctual', u'engaged', u'fond']\n" 288 | ] 289 | } 290 | ], 291 | "source": [ 292 | "# Find words (adjectives) that describe Mr. Darcy.\n", 293 | "\n", 294 | "def get_character_adjectives(doc, character_lemma):\n", 295 | " \"\"\"\n", 296 | " Find all the adjectives related to `character_lemma` in `doc`\n", 297 | " \n", 298 | " :param doc: Spacy NLP parsed document\n", 299 | " :param character_lemma: string object\n", 300 | " :return: list of adjectives related to `character_lemma`\n", 301 | " \"\"\"\n", 302 | " \n", 303 | " adjectives = []\n", 304 | " for ent in processed_text.ents:\n", 305 | " if ent.lemma_ == character_lemma:\n", 306 | " for token in ent.subtree:\n", 307 | " if token.pos_ == 'ADJ': # Replace with if token.dep_ == 'amod':\n", 308 | " adjectives.append(token.lemma_)\n", 309 | " \n", 310 | " for ent in processed_text.ents:\n", 311 | " if ent.lemma_ == character_lemma:\n", 312 | " if ent.root.dep_ == 'nsubj':\n", 313 | " for child in ent.root.head.children:\n", 314 | " if child.dep_ == 'acomp':\n", 315 | " adjectives.append(child.lemma_)\n", 316 | " \n", 317 | " return adjectives\n", 318 | "\n", 319 | "print(get_character_adjectives(processed_text, 'darcy'))" 320 | ] 321 | }, 322 | { 323 | "cell_type": "code", 324 | "execution_count": 9, 325 | "metadata": { 326 | "collapsed": false 327 | }, 328 | "outputs": [ 329 | { 330 | "name": "stdout", 331 | "output_type": "stream", 332 | "text": [ 333 | "[(u'Elizabeth', 42), (u'Bennet', 30), (u'Jane', 15), (u'Miss Bingley', 9), (u'Lizzy', 6), (u'Gardiner', 5), (u'Lydia', 4), (u'Darcy', 4), (u'Wickham', 4), (u'Bingley', 4)]\n" 334 | ] 335 | } 336 | ], 337 | "source": [ 338 | "# Find characters that are 'talking', 'saying', 'doing' the most. Find the relationship between \n", 339 | "# entities and corresponding root verbs.\n", 340 | "\n", 341 | "character_verb_counter = Counter()\n", 342 | "VERB_LEMMA = 'say'\n", 343 | "\n", 344 | "for ent in processed_text.ents:\n", 345 | " if ent.label_ == 'PERSON' and ent.root.head.lemma_ == VERB_LEMMA:\n", 346 | " character_verb_counter[ent.text] += 1\n", 347 | "\n", 348 | "print(character_verb_counter.most_common(10)) \n", 349 | " \n", 350 | "# Find all the characters that got married in the book.\n", 351 | "#\n", 352 | "# Here is an example sentence from which this information could be extracted:\n", 353 | "# \n", 354 | "# \"her mother was talking to that one person (Lady Lucas) freely,\n", 355 | "# openly, and of nothing else but her expectation that Jane would soon\n", 356 | "# be married to Mr. Bingley.\"\n", 357 | "#\n" 358 | ] 359 | }, 360 | { 361 | "cell_type": "markdown", 362 | "metadata": {}, 363 | "source": [ 364 | "## Extract Keywords" 365 | ] 366 | }, 367 | { 368 | "cell_type": "code", 369 | "execution_count": 10, 370 | "metadata": { 371 | "collapsed": false 372 | }, 373 | "outputs": [ 374 | { 375 | "data": { 376 | "text/plain": [ 377 | "[(u'al - qaeda', 3),\n", 378 | " (u'terrorism', 3),\n", 379 | " (u'religion', 2),\n", 380 | " (u'the responsibility', 2),\n", 381 | " (u'excommunication', 2),\n", 382 | " (u'saudi arabia', 2),\n", 383 | " (u'the ministry', 2),\n", 384 | " (u'interior', 2),\n", 385 | " (u'many country', 2),\n", 386 | " (u'all gulf , arab', 1),\n", 387 | " (u'concept', 1),\n", 388 | " (u'the sykes - picot agreement', 1),\n", 389 | " (u'even international effort', 1),\n", 390 | " (u'ramadan', 1),\n", 391 | " (u'recent month', 1),\n", 392 | " (u'no place', 1),\n", 393 | " (u'official and non - official religious institution', 1),\n", 394 | " (u'tyrant', 1),\n", 395 | " (u'scientific critique', 1),\n", 396 | " (u'misconception', 1)]" 397 | ] 398 | }, 399 | "execution_count": 10, 400 | "metadata": {}, 401 | "output_type": "execute_result" 402 | } 403 | ], 404 | "source": [ 405 | "# Extract Keywords using noun chunks from the news article (file 'article.txt').\n", 406 | "# Spacy will pick some noun chunks that are not informative at all (e.g. we, what, who).\n", 407 | "# Try to find a way to remove non informative keywords.\n", 408 | "\n", 409 | "article = read_file('data/article.txt')\n", 410 | "doc = nlp(article)\n", 411 | "\n", 412 | "keywords = Counter()\n", 413 | "for chunk in doc.noun_chunks:\n", 414 | " if nlp.vocab[chunk.lemma_].prob < - 8: # probablity value -8 is arbitrarily selected threshold\n", 415 | " keywords[chunk.lemma_] += 1\n", 416 | "\n", 417 | "keywords.most_common(20)" 418 | ] 419 | }, 420 | { 421 | "cell_type": "code", 422 | "execution_count": null, 423 | "metadata": { 424 | "collapsed": true 425 | }, 426 | "outputs": [], 427 | "source": [] 428 | } 429 | ], 430 | "metadata": { 431 | "kernelspec": { 432 | "display_name": "Python 2", 433 | "language": "python", 434 | "name": "python2" 435 | }, 436 | "language_info": { 437 | "codemirror_mode": { 438 | "name": "ipython", 439 | "version": 2 440 | }, 441 | "file_extension": ".py", 442 | "mimetype": "text/x-python", 443 | "name": "python", 444 | "nbconvert_exporter": "python", 445 | "pygments_lexer": "ipython2", 446 | "version": "2.7.10" 447 | } 448 | }, 449 | "nbformat": 4, 450 | "nbformat_minor": 1 451 | } 452 | -------------------------------------------------------------------------------- /notebooks/conference_notebooks/pycon_nlp/02_rand_dataset.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# RAND Dataset analysis" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | "This is an open challenge to apply what you have learnt analysing Pride and Prejudice with spaCy on a dataset of real events. We have preprocessed the [RAND Terrorism Dataset]((http://www.rand.org/nsrd/projects/terrorism-incidents.html) for this task reducing the data to 10033 articles from 1968 to 2009.\n", 15 | "\n", 16 | "Can you find out the following using the code you have written?\n", 17 | "- Who are the terrorist groups and other persons mentioned in each article?\n", 18 | "- What locations are mentioned in each article? Hint: a location just has a different label to a person\n", 19 | "- From all of your entities, can you find out which named entities are terrorists from the syntactic relationships?\n", 20 | "- With all of this information, can you plot a figure expressing the relationships between locations and terrorists?\n", 21 | "\n", 22 | "There are no right answers to any of these questions, and there might not even be an answer at all." 23 | ] 24 | }, 25 | { 26 | "cell_type": "markdown", 27 | "metadata": {}, 28 | "source": [ 29 | "#### Example result using the full ~40,000 article dataset\n", 30 | "\n", 31 | "![Heatmap of terrorist group and country](images/example_output_full.png)" 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 1, 37 | "metadata": { 38 | "collapsed": false 39 | }, 40 | "outputs": [], 41 | "source": [ 42 | "# To get you started we can import Pandas and Seaborn which might help you\n", 43 | "# build a graph or visualisation of the data\n", 44 | "% matplotlib inline\n", 45 | "\n", 46 | "from collections import defaultdict, Counter\n", 47 | "\n", 48 | "import matplotlib.pyplot as plt\n", 49 | "import matplotlib as mpl\n", 50 | "import pandas as pd\n", 51 | "import seaborn as sns\n", 52 | "import spacy\n", 53 | "\n", 54 | "nlp = spacy.load('en')\n", 55 | "\n", 56 | "def read_file_to_list(file_name):\n", 57 | " with open(file_name, 'r') as file:\n", 58 | " return file.readlines()\n", 59 | "\n", 60 | "# The file has been re-encoded in UTF-8, the source encoding is Latin-1\n", 61 | "terrorism_articles = read_file_to_list('data/rand-terrorism-dataset.txt')\n", 62 | "\n", 63 | "# Create a list of spaCy Doc objects representing articles\n", 64 | "terrorism_articles_nlp = [nlp(art) for art in terrorism_articles]" 65 | ] 66 | }, 67 | { 68 | "cell_type": "markdown", 69 | "metadata": {}, 70 | "source": [ 71 | "## Example solution" 72 | ] 73 | }, 74 | { 75 | "cell_type": "markdown", 76 | "metadata": {}, 77 | "source": [ 78 | "### Define some geographical areas and groups to inspect\n", 79 | "\n", 80 | "These are commonly mentioned in the full dataset, which you can prove for yourself using the same approaches as in the previous tutorial for Pride and Prejudice. You can process the full dataset at once into one spaCy span using the read_file() function. " 81 | ] 82 | }, 83 | { 84 | "cell_type": "code", 85 | "execution_count": 2, 86 | "metadata": { 87 | "collapsed": false 88 | }, 89 | "outputs": [], 90 | "source": [ 91 | "common_terrorist_groups = [\n", 92 | " 'taliban', \n", 93 | " 'al - qaeda', \n", 94 | " 'hamas', \n", 95 | " 'fatah', \n", 96 | " 'plo', \n", 97 | " 'bilad al - rafidayn'\n", 98 | "]\n", 99 | "\n", 100 | "common_locations = [\n", 101 | " 'iraq',\n", 102 | " 'baghdad', \n", 103 | " 'kirkuk', \n", 104 | " 'mosul', \n", 105 | " 'afghanistan', \n", 106 | " 'kabul',\n", 107 | " 'basra', \n", 108 | " 'palestine', \n", 109 | " 'gaza', \n", 110 | " 'israel', \n", 111 | " 'istanbul', \n", 112 | " 'beirut', \n", 113 | " 'pakistan'\n", 114 | "]" 115 | ] 116 | }, 117 | { 118 | "cell_type": "markdown", 119 | "metadata": {}, 120 | "source": [ 121 | "## Inspect each article for mentions of groups and locations" 122 | ] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": 3, 127 | "metadata": { 128 | "collapsed": false 129 | }, 130 | "outputs": [ 131 | { 132 | "data": { 133 | "text/plain": [ 134 | "11" 135 | ] 136 | }, 137 | "execution_count": 3, 138 | "metadata": {}, 139 | "output_type": "execute_result" 140 | } 141 | ], 142 | "source": [ 143 | "location_entity_dict = defaultdict(Counter)\n", 144 | "\n", 145 | "for article in terrorism_articles_nlp:\n", 146 | " #Get all the groups and location entity in the article\n", 147 | " article_terrorist_cands = [ent.lemma_ for ent in article.ents if ent.label_ == 'PERSON' or ent.label_ == 'ORG']\n", 148 | " article_location_cands = [ent.lemma_ for ent in article.ents if ent.label_ == 'GPE']\n", 149 | "\n", 150 | " #Filter groups and locations for only those which we are interested in\n", 151 | " terrorist_candidates = [ent for ent in article_terrorist_cands if ent in common_terrorist_groups]\n", 152 | " location_candidates = [loc for loc in article_location_cands if loc in common_locations]\n", 153 | "\n", 154 | " for found_entity in terrorist_candidates:\n", 155 | " for found_location in location_candidates:\n", 156 | " location_entity_dict[found_entity][found_location] += 1\n", 157 | "\n", 158 | "# Let's inspect a specific combination as a cursory check on the for loop operating correctly\n", 159 | "location_entity_dict['plo']['beirut']" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | "## Transform defaultdict to a Pandas DataFrame" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 4, 172 | "metadata": { 173 | "collapsed": false 174 | }, 175 | "outputs": [ 176 | { 177 | "data": { 178 | "text/html": [ 179 | "
\n", 180 | "\n", 181 | " \n", 182 | " \n", 183 | " \n", 184 | " \n", 185 | " \n", 186 | " \n", 187 | " \n", 188 | " \n", 189 | " \n", 190 | " \n", 191 | " \n", 192 | " \n", 193 | " \n", 194 | " \n", 195 | " \n", 196 | " \n", 197 | " \n", 198 | " \n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | " \n", 252 | " \n", 253 | " \n", 254 | " \n", 255 | " \n", 256 | " \n", 257 | " \n", 258 | " \n", 259 | " \n", 260 | " \n", 261 | " \n", 262 | " \n", 263 | " \n", 264 | " \n", 265 | " \n", 266 | " \n", 267 | " \n", 268 | " \n", 269 | " \n", 270 | " \n", 271 | " \n", 272 | " \n", 273 | " \n", 274 | " \n", 275 | " \n", 276 | " \n", 277 | " \n", 278 | " \n", 279 | " \n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | "
al - qaedabilad al - rafidaynfatahhamasplotaliban
afghanistan60000260
baghdad20310000
basra040000
beirut0011110
gaza00117000
iraq53231060
israel101621190
istanbul300000
kabul3000057
kirkuk500000
mosul1640000
pakistan6000014
palestine240130
\n", 312 | "
" 313 | ], 314 | "text/plain": [ 315 | " al - qaeda bilad al - rafidayn fatah hamas plo taliban\n", 316 | "afghanistan 6 0 0 0 0 260\n", 317 | "baghdad 20 31 0 0 0 0\n", 318 | "basra 0 4 0 0 0 0\n", 319 | "beirut 0 0 1 1 11 0\n", 320 | "gaza 0 0 11 70 0 0\n", 321 | "iraq 53 23 1 0 6 0\n", 322 | "israel 1 0 16 21 19 0\n", 323 | "istanbul 3 0 0 0 0 0\n", 324 | "kabul 3 0 0 0 0 57\n", 325 | "kirkuk 5 0 0 0 0 0\n", 326 | "mosul 16 4 0 0 0 0\n", 327 | "pakistan 6 0 0 0 0 14\n", 328 | "palestine 2 4 0 1 3 0" 329 | ] 330 | }, 331 | "execution_count": 4, 332 | "metadata": {}, 333 | "output_type": "execute_result" 334 | } 335 | ], 336 | "source": [ 337 | "# Transform the dictionary into a pandas DataFrame and fill NaN values with zeroes\n", 338 | "location_entity_df = pd.DataFrame.from_dict(dict(location_entity_dict), dtype=int)\n", 339 | "location_entity_full_df = location_entity_df.fillna(value=0).astype(int)\n", 340 | "# Show DF to console\n", 341 | "location_entity_full_df" 342 | ] 343 | }, 344 | { 345 | "cell_type": "code", 346 | "execution_count": 5, 347 | "metadata": { 348 | "collapsed": false 349 | }, 350 | "outputs": [ 351 | { 352 | "data": { 353 | "image/png": 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xccHOzo41a9YQExPDDz/8YLxKxoPpHk9b2CYmJjJp0iQ2bdpEdHQ0X331FZkz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\niOGd0IE5AAAAAElFTkSuQmCC\n", 354 | "text/plain": [ 355 | "" 356 | ] 357 | }, 358 | "metadata": {}, 359 | "output_type": "display_data" 360 | } 361 | ], 362 | "source": [ 363 | "# Seaborn can transform a DataFrame directly into a figure\n", 364 | "\n", 365 | "plt.figure()\n", 366 | "hmap = sns.heatmap(location_entity_full_df, annot=True, fmt='d', cmap='YlGnBu', cbar=False)\n", 367 | "\n", 368 | "# Add features using the under the hood plt interface\n", 369 | "plt.title('Global Incidents by Terrorist group')\n", 370 | "plt.xticks(rotation=30)\n", 371 | "plt.show()" 372 | ] 373 | }, 374 | { 375 | "cell_type": "code", 376 | "execution_count": 6, 377 | "metadata": { 378 | "collapsed": false 379 | }, 380 | "outputs": [ 381 | { 382 | "data": { 383 | "image/png": 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z5zN8+HBsbW1Nd8p40fs716hRgz59+jB+/Hjq16/Pjz/+yJw5c7C3tydr1qym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\nX35JcHAw1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384 | "text/plain": [ 385 | "" 386 | ] 387 | }, 388 | "metadata": {}, 389 | "output_type": "display_data" 390 | } 391 | ], 392 | "source": [ 393 | "# You can also mask all the zero figures using features of the DataFrame\n", 394 | "heat_mask = location_entity_df.isnull()\n", 395 | "\n", 396 | "hmap = sns.heatmap(location_entity_full_df, annot=True, fmt='d', cmap='YlGnBu', cbar=False, mask=heat_mask)\n", 397 | "\n", 398 | "# Add features using the under the hood plt interface\n", 399 | "sns.axes_style('white')\n", 400 | "plt.title('Global Incidents by Terrorist group')\n", 401 | "plt.xticks(rotation=30)\n", 402 | "plt.show()" 403 | ] 404 | } 405 | ], 406 | "metadata": { 407 | "kernelspec": { 408 | "display_name": "Python 3", 409 | "language": "python", 410 | "name": "python3" 411 | }, 412 | "language_info": { 413 | "codemirror_mode": { 414 | "name": "ipython", 415 | "version": 3 416 | }, 417 | "file_extension": ".py", 418 | "mimetype": "text/x-python", 419 | "name": "python", 420 | "nbconvert_exporter": "python", 421 | "pygments_lexer": "ipython3", 422 | "version": "3.5.2" 423 | } 424 | }, 425 | "nbformat": 4, 426 | "nbformat_minor": 1 427 | } 428 | --------------------------------------------------------------------------------