spaCy/spacy/glossary.py

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# coding: utf8
from __future__ import unicode_literals
def explain(term):
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"""Get a description for a given POS tag, dependency label or entity type.
term (unicode): The term to explain.
RETURNS (unicode): The explanation, or `None` if not found in the glossary.
EXAMPLE:
>>> spacy.explain(u'NORP')
>>> doc = nlp(u'Hello world')
>>> print([w.text, w.tag_, spacy.explain(w.tag_) for w in doc])
"""
if term in GLOSSARY:
return GLOSSARY[term]
GLOSSARY = {
# POS tags
# Universal POS Tags
# http://universaldependencies.org/u/pos/
'ADJ': 'adjective',
'ADP': 'adposition',
'ADV': 'adverb',
'AUX': 'auxiliary',
'CONJ': 'conjunction',
'CCONJ': 'coordinating conjunction',
'DET': 'determiner',
'INTJ': 'interjection',
'NOUN': 'noun',
'NUM': 'numeral',
'PART': 'particle',
'PRON': 'pronoun',
'PROPN': 'proper noun',
'PUNCT': 'punctuation',
'SCONJ': 'subordinating conjunction',
'SYM': 'symbol',
'VERB': 'verb',
'X': 'other',
'EOL': 'end of line',
'SPACE': 'space',
# POS tags (English)
# OntoNotes 5 / Penn Treebank
# https://www.ling.upenn.edu/courses/Fall_2003/ling001/penn_treebank_pos.html
'.': 'punctuation mark, sentence closer',
',': 'punctuation mark, comma',
'-LRB-': 'left round bracket',
'-RRB-': 'right round bracket',
'``': 'opening quotation mark',
'""': 'closing quotation mark',
"''": 'closing quotation mark',
':': 'punctuation mark, colon or ellipsis',
'$': 'symbol, currency',
'#': 'symbol, number sign',
'AFX': 'affix',
'CC': 'conjunction, coordinating',
'CD': 'cardinal number',
'DT': 'determiner',
'EX': 'existential there',
'FW': 'foreign word',
'HYPH': 'punctuation mark, hyphen',
'IN': 'conjunction, subordinating or preposition',
'JJ': 'adjective',
'JJR': 'adjective, comparative',
'JJS': 'adjective, superlative',
'LS': 'list item marker',
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'MD': 'verb, modal auxiliary',
'NIL': 'missing tag',
'NN': 'noun, singular or mass',
'NNP': 'noun, proper singular',
'NNPS': 'noun, proper plural',
'NNS': 'noun, plural',
'PDT': 'predeterminer',
'POS': 'possessive ending',
'PRP': 'pronoun, personal',
'PRP$': 'pronoun, possessive',
'RB': 'adverb',
'RBR': 'adverb, comparative',
'RBS': 'adverb, superlative',
'RP': 'adverb, particle',
'TO': 'infinitival to',
'UH': 'interjection',
'VB': 'verb, base form',
'VBD': 'verb, past tense',
'VBG': 'verb, gerund or present participle',
'VBN': 'verb, past participle',
'VBP': 'verb, non-3rd person singular present',
'VBZ': 'verb, 3rd person singular present',
'WDT': 'wh-determiner',
'WP': 'wh-pronoun, personal',
'WP$': 'wh-pronoun, possessive',
'WRB': 'wh-adverb',
'SP': 'space',
'ADD': 'email',
'NFP': 'superfluous punctuation',
'GW': 'additional word in multi-word expression',
'XX': 'unknown',
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'BES': 'auxiliary "be"',
'HVS': 'forms of "have"',
# POS Tags (German)
# TIGER Treebank
# http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/annotation/tiger_introduction.pdf
'$(': 'other sentence-internal punctuation mark',
'$,': 'comma',
'$.': 'sentence-final punctuation mark',
'ADJA': 'adjective, attributive',
'ADJD': 'adjective, adverbial or predicative',
'APPO': 'postposition',
'APPR': 'preposition; circumposition left',
'APPRART': 'preposition with article',
'APZR': 'circumposition right',
'ART': 'definite or indefinite article',
'CARD': 'cardinal number',
'FM': 'foreign language material',
'ITJ': 'interjection',
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'KOKOM': 'comparative conjunction',
'KON': 'coordinate conjunction',
'KOUI': 'subordinate conjunction with "zu" and infinitive',
'KOUS': 'subordinate conjunction with sentence',
'NE': 'proper noun',
'NNE': 'proper noun',
'PAV': 'pronominal adverb',
'PROAV': 'pronominal adverb',
'PDAT': 'attributive demonstrative pronoun',
'PDS': 'substituting demonstrative pronoun',
'PIAT': 'attributive indefinite pronoun without determiner',
'PIDAT': 'attributive indefinite pronoun with determiner',
'PIS': 'substituting indefinite pronoun',
'PPER': 'non-reflexive personal pronoun',
'PPOSAT': 'attributive possessive pronoun',
'PPOSS': 'substituting possessive pronoun',
'PRELAT': 'attributive relative pronoun',
'PRELS': 'substituting relative pronoun',
'PRF': 'reflexive personal pronoun',
'PTKA': 'particle with adjective or adverb',
'PTKANT': 'answer particle',
'PTKNEG': 'negative particle',
'PTKVZ': 'separable verbal particle',
'PTKZU': '"zu" before infinitive',
'PWAT': 'attributive interrogative pronoun',
'PWAV': 'adverbial interrogative or relative pronoun',
'PWS': 'substituting interrogative pronoun',
'TRUNC': 'word remnant',
'VAFIN': 'finite verb, auxiliary',
'VAIMP': 'imperative, auxiliary',
'VAINF': 'infinitive, auxiliary',
'VAPP': 'perfect participle, auxiliary',
'VMFIN': 'finite verb, modal',
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'VMINF': 'infinitive, modal',
'VMPP': 'perfect participle, modal',
'VVFIN': 'finite verb, full',
'VVIMP': 'imperative, full',
'VVINF': 'infinitive, full',
'VVIZU': 'infinitive with "zu", full',
'VVPP': 'perfect participle, full',
'XY': 'non-word containing non-letter',
# Noun chunks
'NP': 'noun phrase',
'PP': 'prepositional phrase',
'VP': 'verb phrase',
'ADVP': 'adverb phrase',
'ADJP': 'adjective phrase',
'SBAR': 'subordinating conjunction',
'PRT': 'particle',
'PNP': 'prepositional noun phrase',
# Dependency Labels (English)
# ClearNLP / Universal Dependencies
# https://github.com/clir/clearnlp-guidelines/blob/master/md/specifications/dependency_labels.md
'acomp': 'adjectival complement',
'advcl': 'adverbial clause modifier',
'advmod': 'adverbial modifier',
'agent': 'agent',
'amod': 'adjectival modifier',
'appos': 'appositional modifier',
'attr': 'attribute',
'aux': 'auxiliary',
'auxpass': 'auxiliary (passive)',
'cc': 'coordinating conjunction',
'ccomp': 'clausal complement',
'complm': 'complementizer',
'conj': 'conjunct',
'cop': 'copula',
'csubj': 'clausal subject',
'csubjpass': 'clausal subject (passive)',
'dep': 'unclassified dependent',
'det': 'determiner',
'dobj': 'direct object',
'expl': 'expletive',
'hmod': 'modifier in hyphenation',
'hyph': 'hyphen',
'infmod': 'infinitival modifier',
'intj': 'interjection',
'iobj': 'indirect object',
'mark': 'marker',
'meta': 'meta modifier',
'neg': 'negation modifier',
'nmod': 'modifier of nominal',
'nn': 'noun compound modifier',
'npadvmod': 'noun phrase as adverbial modifier',
'nsubj': 'nominal subject',
'nsubjpass': 'nominal subject (passive)',
'num': 'number modifier',
'number': 'number compound modifier',
'oprd': 'object predicate',
'obj': 'object',
'obl': 'oblique nominal',
'parataxis': 'parataxis',
'partmod': 'participal modifier',
'pcomp': 'complement of preposition',
'pobj': 'object of preposition',
'poss': 'possession modifier',
'possessive': 'possessive modifier',
'preconj': 'pre-correlative conjunction',
'prep': 'prepositional modifier',
'prt': 'particle',
'punct': 'punctuation',
'quantmod': 'modifier of quantifier',
'rcmod': 'relative clause modifier',
'root': 'root',
'xcomp': 'open clausal complement',
# Dependency labels (German)
# TIGER Treebank
# http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/annotation/tiger_introduction.pdf
# currently missing: 'cc' (comparative complement) because of conflict
# with English labels
'ac': 'adpositional case marker',
'adc': 'adjective component',
'ag': 'genitive attribute',
'ams': 'measure argument of adjective',
'app': 'apposition',
'avc': 'adverbial phrase component',
'cd': 'coordinating conjunction',
'cj': 'conjunct',
'cm': 'comparative conjunction',
'cp': 'complementizer',
'cvc': 'collocational verb construction',
'da': 'dative',
'dh': 'discourse-level head',
'dm': 'discourse marker',
'ep': 'expletive es',
'hd': 'head',
'ju': 'junctor',
'mnr': 'postnominal modifier',
'mo': 'modifier',
'ng': 'negation',
'nk': 'noun kernel element',
'nmc': 'numerical component',
'oa': 'accusative object',
'oc': 'clausal object',
'og': 'genitive object',
'op': 'prepositional object',
'par': 'parenthetical element',
'pd': 'predicate',
'pg': 'phrasal genitive',
'ph': 'placeholder',
'pm': 'morphological particle',
'pnc': 'proper noun component',
'rc': 'relative clause',
're': 'repeated element',
'rs': 'reported speech',
'sb': 'subject',
# Named Entity Recognition
# OntoNotes 5
# https://catalog.ldc.upenn.edu/docs/LDC2013T19/OntoNotes-Release-5.0.pdf
'PERSON': 'People, including fictional',
'NORP': 'Nationalities or religious or political groups',
'FACILITY': 'Buildings, airports, highways, bridges, etc.',
💫 Port master changes over to develop (#2979) * Create aryaprabhudesai.md (#2681) * Update _install.jade (#2688) Typo fix: "models" -> "model" * Add FAC to spacy.explain (resolves #2706) * Remove docstrings for deprecated arguments (see #2703) * When calling getoption() in conftest.py, pass a default option (#2709) * When calling getoption() in conftest.py, pass a default option This is necessary to allow testing an installed spacy by running: pytest --pyargs spacy * Add contributor agreement * update bengali token rules for hyphen and digits (#2731) * Less norm computations in token similarity (#2730) * Less norm computations in token similarity * Contributor agreement * Remove ')' for clarity (#2737) Sorry, don't mean to be nitpicky, I just noticed this when going through the CLI and thought it was a quick fix. That said, if this was intention than please let me know. * added contributor agreement for mbkupfer (#2738) * Basic support for Telugu language (#2751) * Lex _attrs for polish language (#2750) * Signed spaCy contributor agreement * Added polish version of english lex_attrs * Introduces a bulk merge function, in order to solve issue #653 (#2696) * Fix comment * Introduce bulk merge to increase performance on many span merges * Sign contributor agreement * Implement pull request suggestions * Describe converters more explicitly (see #2643) * Add multi-threading note to Language.pipe (resolves #2582) [ci skip] * Fix formatting * Fix dependency scheme docs (closes #2705) [ci skip] * Don't set stop word in example (closes #2657) [ci skip] * Add words to portuguese language _num_words (#2759) * Add words to portuguese language _num_words * Add words to portuguese language _num_words * Update Indonesian model (#2752) * adding e-KTP in tokenizer exceptions list * add exception token * removing lines with containing space as it won't matter since we use .split() method in the end, added new tokens in exception * add tokenizer exceptions list * combining base_norms with norm_exceptions * adding norm_exception * fix double key in lemmatizer * remove unused import on punctuation.py * reformat stop_words to reduce number of lines, improve readibility * updating tokenizer exception * implement is_currency for lang/id * adding orth_first_upper in tokenizer_exceptions * update the norm_exception list * remove bunch of abbreviations * adding contributors file * Fixed spaCy+Keras example (#2763) * bug fixes in keras example * created contributor agreement * Adding French hyphenated first name (#2786) * Fix typo (closes #2784) * Fix typo (#2795) [ci skip] Fixed typo on line 6 "regcognizer --> recognizer" * Adding basic support for Sinhala language. (#2788) * adding Sinhala language package, stop words, examples and lex_attrs. * Adding contributor agreement * Updating contributor agreement * Also include lowercase norm exceptions * Fix error (#2802) * Fix error ValueError: cannot resize an array that references or is referenced by another array in this way. Use the resize function * added spaCy Contributor Agreement * Add charlax's contributor agreement (#2805) * agreement of contributor, may I introduce a tiny pl languge contribution (#2799) * Contributors agreement * Contributors agreement * Contributors agreement * Add jupyter=True to displacy.render in documentation (#2806) * Revert "Also include lowercase norm exceptions" This reverts commit 70f4e8adf37cfcfab60be2b97d6deae949b30e9e. * Remove deprecated encoding argument to msgpack * Set up dependency tree pattern matching skeleton (#2732) * Fix bug when too many entity types. Fixes #2800 * Fix Python 2 test failure * Require older msgpack-numpy * Restore encoding arg on msgpack-numpy * Try to fix version pin for msgpack-numpy * Update Portuguese Language (#2790) * Add words to portuguese language _num_words * Add words to portuguese language _num_words * Portuguese - Add/remove stopwords, fix tokenizer, add currency symbols * Extended punctuation and norm_exceptions in the Portuguese language * Correct error in spacy universe docs concerning spacy-lookup (#2814) * Update Keras Example for (Parikh et al, 2016) implementation (#2803) * bug fixes in keras example * created contributor agreement * baseline for Parikh model * initial version of parikh 2016 implemented * tested asymmetric models * fixed grevious error in normalization * use standard SNLI test file * begin to rework parikh example * initial version of running example * start to document the new version * start to document the new version * Update Decompositional Attention.ipynb * fixed calls to similarity * updated the README * import sys package duh * simplified indexing on mapping word to IDs * stupid python indent error * added code from https://github.com/tensorflow/tensorflow/issues/3388 for tf bug workaround * Fix typo (closes #2815) [ci skip] * Update regex version dependency * Set version to 2.0.13.dev3 * Skip seemingly problematic test * Remove problematic test * Try previous version of regex * Revert "Remove problematic test" This reverts commit bdebbef45552d698d390aa430b527ee27830f11b. * Unskip test * Try older version of regex * 💫 Update training examples and use minibatching (#2830) <!--- Provide a general summary of your changes in the title. --> ## Description Update the training examples in `/examples/training` to show usage of spaCy's `minibatch` and `compounding` helpers ([see here](https://spacy.io/usage/training#tips-batch-size) for details). The lack of batching in the examples has caused some confusion in the past, especially for beginners who would copy-paste the examples, update them with large training sets and experienced slow and unsatisfying results. ### Types of change enhancements ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Visual C++ link updated (#2842) (closes #2841) [ci skip] * New landing page * Add contribution agreement * Correcting lang/ru/examples.py (#2845) * Correct some grammatical inaccuracies in lang\ru\examples.py; filled Contributor Agreement * Correct some grammatical inaccuracies in lang\ru\examples.py * Move contributor agreement to separate file * Set version to 2.0.13.dev4 * Add Persian(Farsi) language support (#2797) * Also include lowercase norm exceptions * Remove in favour of https://github.com/explosion/spaCy/graphs/contributors * Rule-based French Lemmatizer (#2818) <!--- Provide a general summary of your changes in the title. --> ## Description <!--- Use this section to describe your changes. If your changes required testing, include information about the testing environment and the tests you ran. If your test fixes a bug reported in an issue, don't forget to include the issue number. If your PR is still a work in progress, that's totally fine – just include a note to let us know. --> Add a rule-based French Lemmatizer following the english one and the excellent PR for [greek language optimizations](https://github.com/explosion/spaCy/pull/2558) to adapt the Lemmatizer class. ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> - Lemma dictionary used can be found [here](http://infolingu.univ-mlv.fr/DonneesLinguistiques/Dictionnaires/telechargement.html), I used the XML version. - Add several files containing exhaustive list of words for each part of speech - Add some lemma rules - Add POS that are not checked in the standard Lemmatizer, i.e PRON, DET, ADV and AUX - Modify the Lemmatizer class to check in lookup table as a last resort if POS not mentionned - Modify the lemmatize function to check in lookup table as a last resort - Init files are updated so the model can support all the functionalities mentioned above - Add words to tokenizer_exceptions_list.py in respect to regex used in tokenizer_exceptions.py ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [X] I have submitted the spaCy Contributor Agreement. - [X] I ran the tests, and all new and existing tests passed. - [X] My changes don't require a change to the documentation, or if they do, I've added all required information. * Set version to 2.0.13 * Fix formatting and consistency * Update docs for new version [ci skip] * Increment version [ci skip] * Add info on wheels [ci skip] * Adding "This is a sentence" example to Sinhala (#2846) * Add wheels badge * Update badge [ci skip] * Update README.rst [ci skip] * Update murmurhash pin * Increment version to 2.0.14.dev0 * Update GPU docs for v2.0.14 * Add wheel to setup_requires * Import prefer_gpu and require_gpu functions from Thinc * Add tests for prefer_gpu() and require_gpu() * Update requirements and setup.py * Workaround bug in thinc require_gpu * Set version to v2.0.14 * Update push-tag script * Unhack prefer_gpu * Require thinc 6.10.6 * Update prefer_gpu and require_gpu docs [ci skip] * Fix specifiers for GPU * Set version to 2.0.14.dev1 * Set version to 2.0.14 * Update Thinc version pin * Increment version * Fix msgpack-numpy version pin * Increment version * Update version to 2.0.16 * Update version [ci skip] * Redundant ')' in the Stop words' example (#2856) <!--- Provide a general summary of your changes in the title. --> ## Description <!--- Use this section to describe your changes. If your changes required testing, include information about the testing environment and the tests you ran. If your test fixes a bug reported in an issue, don't forget to include the issue number. If your PR is still a work in progress, that's totally fine – just include a note to let us know. --> ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [ ] I have submitted the spaCy Contributor Agreement. - [ ] I ran the tests, and all new and existing tests passed. - [ ] My changes don't require a change to the documentation, or if they do, I've added all required information. * Documentation improvement regarding joblib and SO (#2867) Some documentation improvements ## Description 1. Fixed the dead URL to joblib 2. Fixed Stack Overflow brand name (with space) ### Types of change Documentation ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * raise error when setting overlapping entities as doc.ents (#2880) * Fix out-of-bounds access in NER training The helper method state.B(1) gets the index of the first token of the buffer, or -1 if no such token exists. Normally this is safe because we pass this to functions like state.safe_get(), which returns an empty token. Here we used it directly as an array index, which is not okay! This error may have been the cause of out-of-bounds access errors during training. Similar errors may still be around, so much be hunted down. Hunting this one down took a long time...I printed out values across training runs and diffed, looking for points of divergence between runs, when no randomness should be allowed. * Change PyThaiNLP Url (#2876) * Fix missing comma * Add example showing a fix-up rule for space entities * Set version to 2.0.17.dev0 * Update regex version * Revert "Update regex version" This reverts commit 62358dd867d15bc6a475942dff34effba69dd70a. * Try setting older regex version, to align with conda * Set version to 2.0.17 * Add spacy-js to universe [ci-skip] * Add spacy-raspberry to universe (closes #2889) * Add script to validate universe json [ci skip] * Removed space in docs + added contributor indo (#2909) * - removed unneeded space in documentation * - added contributor info * Allow input text of length up to max_length, inclusive (#2922) * Include universe spec for spacy-wordnet component (#2919) * feat: include universe spec for spacy-wordnet component * chore: include spaCy contributor agreement * Minor formatting changes [ci skip] * Fix image [ci skip] Twitter URL doesn't work on live site * Check if the word is in one of the regular lists specific to each POS (#2886) * 💫 Create random IDs for SVGs to prevent ID clashes (#2927) Resolves #2924. ## Description Fixes problem where multiple visualizations in Jupyter notebooks would have clashing arc IDs, resulting in weirdly positioned arc labels. Generating a random ID prefix so even identical parses won't receive the same IDs for consistency (even if effect of ID clash isn't noticable here.) ### Types of change bug fix ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Fix typo [ci skip] * fixes symbolic link on py3 and windows (#2949) * fixes symbolic link on py3 and windows during setup of spacy using command python -m spacy link en_core_web_sm en closes #2948 * Update spacy/compat.py Co-Authored-By: cicorias <cicorias@users.noreply.github.com> * Fix formatting * Update universe [ci skip] * Catalan Language Support (#2940) * Catalan language Support * Ddding Catalan to documentation * Sort languages alphabetically [ci skip] * Update tests for pytest 4.x (#2965) <!--- Provide a general summary of your changes in the title. --> ## Description - [x] Replace marks in params for pytest 4.0 compat ([see here](https://docs.pytest.org/en/latest/deprecations.html#marks-in-pytest-mark-parametrize)) - [x] Un-xfail passing tests (some fixes in a recent update resolved a bunch of issues, but tests were apparently never updated here) ### Types of change <!-- What type of change does your PR cover? Is it a bug fix, an enhancement or new feature, or a change to the documentation? --> ## Checklist <!--- Before you submit the PR, go over this checklist and make sure you can tick off all the boxes. [] -> [x] --> - [x] I have submitted the spaCy Contributor Agreement. - [x] I ran the tests, and all new and existing tests passed. - [x] My changes don't require a change to the documentation, or if they do, I've added all required information. * Fix regex pin to harmonize with conda (#2964) * Update README.rst * Fix bug where Vocab.prune_vector did not use 'batch_size' (#2977) Fixes #2976 * Fix typo * Fix typo * Remove duplicate file * Require thinc 7.0.0.dev2 Fixes bug in gpu_ops that would use cupy instead of numpy on CPU * Add missing import * Fix error IDs * Fix tests
2018-11-29 18:30:29 +03:00
'FAC': 'Buildings, airports, highways, bridges, etc.',
'ORG': 'Companies, agencies, institutions, etc.',
'GPE': 'Countries, cities, states',
'LOC': 'Non-GPE locations, mountain ranges, bodies of water',
'PRODUCT': 'Objects, vehicles, foods, etc. (not services)',
'EVENT': 'Named hurricanes, battles, wars, sports events, etc.',
'WORK_OF_ART': 'Titles of books, songs, etc.',
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'LAW': 'Named documents made into laws.',
'LANGUAGE': 'Any named language',
'DATE': 'Absolute or relative dates or periods',
'TIME': 'Times smaller than a day',
'PERCENT': 'Percentage, including "%"',
'MONEY': 'Monetary values, including unit',
'QUANTITY': 'Measurements, as of weight or distance',
'ORDINAL': '"first", "second", etc.',
'CARDINAL': 'Numerals that do not fall under another type',
# Named Entity Recognition
# Wikipedia
# http://www.sciencedirect.com/science/article/pii/S0004370212000276
# https://pdfs.semanticscholar.org/5744/578cc243d92287f47448870bb426c66cc941.pdf
'PER': 'Named person or family.',
'MISC': ('Miscellaneous entities, e.g. events, nationalities, '
'products or works of art'),
}