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The Toolbox corpus reader returns Toolbox files as XML Element Tree objects.The following example loads the Rotokas dictionary, and figures out the distribution of part-of-speech tags for reduplicated words.In addition to the plaintext corpora, NLTK's data package also contains a wide variety of annotated corpora.For example, the Brown Corpus is annotated with part-of-speech tags, and defines additional methods print(brown.paras(categories='reviews')) # doctest: ELLIPSIS NORMALIZE_WHITESPACE 'There', 'was', 'about', 'that', 'song', 'something', ...], ['Not', 'the', 'noblest', 'performance', 'we', 'have', ...], ...], ...] print(brown.tagged_paras(categories='reviews')) # doctest: ELLIPSIS NORMALIZE_WHITESPACE

The Toolbox corpus reader returns Toolbox files as XML Element Tree objects.

The following example loads the Rotokas dictionary, and figures out the distribution of part-of-speech tags for reduplicated words.

In addition to the plaintext corpora, NLTK's data package also contains a wide variety of annotated corpora.

For example, the Brown Corpus is annotated with part-of-speech tags, and defines additional methods print(brown.paras(categories='reviews')) # doctest: ELLIPSIS NORMALIZE_WHITESPACE 'There', 'was', 'about', 'that', 'song', 'something', ...], ['Not', 'the', 'noblest', 'performance', 'we', 'have', ...], ...], ...] print(brown.tagged_paras(categories='reviews')) # doctest: ELLIPSIS NORMALIZE_WHITESPACE ('There', 'EX'), ('was', 'BEDZ'), ('about', 'IN'), ...], [('Not', '*'), ('the', 'AT'), ('noblest', 'JJT'), ...], ...], ...] print(brown.tagged_sents(tagset='universal')) # doctest: ELLIPSIS NORMALIZE_WHITESPACE

When given a list of document item names, the reader methods will concatenate together the contents of the individual documents.print(tree) # doctest: SKIP (CP-THT (C D atte) (IP-SUB ...) ... .)) (IP-MAT (IP-MAT-0 (PP (P On) (NP (ADJ o dre) (N wisan)))...) ... .)) (IP-MAT (NP-NOM-x-2 *exp*) (NP-DAT-1 (D^D D am) (ADJ^D unge dyldegum)) ... .)) (IP-MAT (ADVP (ADV Sw a)) (NP-NOM-x (PRO^N hit)) (ADVP-TMP (ADV^T oft)) ... .)) print(ycoe) # doctest: SKIP Traceback (most recent call last): Lookup Error: ********************************************************************** Resource 'corpora/ycoe' not found.For installation instructions, please see The CMU Pronunciation Dictionary corpus contains pronounciation transcriptions for over 100,000 words.print(timit.utteranceids()) # doctest: ELLIPSIS NORMALIZE_WHITESPACE ['dr1-fvmh0/sa1', 'dr1-fvmh0/sa2', 'dr1-fvmh0/si1466', 'dr1-fvmh0/si2096', 'dr1-fvmh0/si836', 'dr1-fvmh0/sx116', 'dr1-fvmh0/sx206', 'dr1-fvmh0/sx26', 'dr1-fvmh0/sx296', ...] print(timit.phones(item)) # doctest: NORMALIZE_WHITESPACE ['h#', 'k', 'l', 'ae', 's', 'pcl', 'p', 'dh', 'ax', 's', 'kcl', 'k', 'r', 'ux', 'ix', 'nx', 'y', 'ax', 'l', 'eh', 'f', 'tcl', 't', 'hh', 'ae', 'n', 'dcl', 'd', 'h#'] print(timit.spkrinfo(timit.spkrid(item))) # doctest: NORMALIZE_WHITESPACE Speaker Info(id='VMH0', sex='F', dr='1', use='TRN', recdate='03/11/86', birthdate='01/08/60', ht='5\'05"', race='WHT', edu='BS', comments='BEST NEW ENGLAND ACCENT SO FAR') twitter_samples.tokenized('tweets.20150430-223406.json') [['RT', '@Kirk Kus', ':', 'Indirect', 'cost', 'of', 'the', 'UK', 'being', 'in', ...], ['VIDEO', ':', 'Sturgeon', 'on', 'post-election', 'deals', ' OY'], ...] The Verb Net corpus is a lexicon that divides verbs into classes, based on their syntax-semantics linking behavior.The basic elements in the lexicon are verb lemmas, such as 'abandon' and 'accept', and verb classes, which have identifiers such as 'remove-10.1' and 'admire-31.2-1'.

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