random letter generator writing shakespeare

#Random letter generator writing shakespeare how to
#Random letter generator writing shakespeare plus

Corpusġ5 genres, 1.15M words, tagged, categorizedġM words, tagged and parsed (Catalan, Spanish)ħ00k words, pos- and named-entity-tagged (Dutch, Spanish)ġ50k words, dependency parsed (Basque, Catalan)ĭependency parsed version of Penn Treebank sampleġ0k word senses, 170k manually annotated sentencesĩk sentences, tagged and parsed (Portuguese)

#Random letter generator writing shakespeare how to

For information aboutįor more examples of how to access NLTK corpora, NLTK providesĬonvenient ways to access several of these corpora, and has data packages containing corporaĪnd corpus samples, freely downloadable for use in teaching and research.ġ.2 lists some of the corpora. Named entities, syntactic structures, semantic roles, and so forth. Many text corpora contain linguistic annotations, representing POS tags, We can optionally specify particular categories or files to read: We can access the corpus as a list of words, or a list of sentences (where each sentence Mosteller: Probability with Statistical ApplicationsĮxample Document for Each Section of the Brown Corpus US Office of Civil and Defence Mobilization: The Family Fallout Shelter Underwood: Probing the Ethics of Realtors Have been categorized by genre, such as news, editorial, and so on. This corpus contains text from 500 sources, and the sources The Brown Corpus was the first million-word electronicĬorpus of English, created in 1961 at Brown University.

The filename contains the date, chatroom,Īnd number of posts e.g., 10-19-20s_706posts.xml contains 706 posts gathered from

#Random letter generator writing shakespeare plus

The corpus is organized into 15 files, where each file contains several hundred postsĬollected on a given date, for an age-specific chatroom (teens, 20s, 30s, 40s, plus a

Names of the form 'UserNNN', and manually edited to remove any other identifying information. The corpus contains over 10,000 posts, anonymized by replacing usernames with generic There is also a corpus of instant messaging chat sessions, originally collectedīy the Naval Postgraduate School for research on automatic detection of Internet predators. wine.txt Lovely delicate, fragrant Rhone wine. singles.txt 25 SEXY MALE, seeks attrac older single lady, for discreet encoun. pirates.txt PIRATES OF THE CARRIBEAN: DEAD MAN'S CHEST, by Ted Elliott & Terr. overheard.txt White guy: So, do you have any plans for this evening? Asian girl. grail.txt SCENE 1: KING ARTHUR: Whoa there! [clop. firefox.txt Cookie Manager: 'Don't allow sites that set removed cookies to se. The sents() function divides the text up into its sentences, where each sentence is Tells us how many letters occur in the text, including the spaces between words. So, for example, len(gutenberg.raw( 'blake-poems.txt')) The raw() function gives us the contents of the file The previous example also showed how we can access the 'raw' text of the book , (In fact, the average word length is reallyģ not 4, since the num_chars variable counts space characters.)īy contrast average sentence length and lexical diversityĪppear to be characteristics of particular authors. Observe that average word length appears to be a general property of English, since Item appears in the text on average (our lexical diversity score). This program displays three statistics for each text:Īverage word length, average sentence length, and the number of times each vocabulary 5 25 26 austen-emma.txt 5 26 17 austen-persuasion.txt 5 28 22 austen-sense.txt 4 34 79 bible-kjv.txt 5 19 5 blake-poems.txt 4 19 14 bryant-stories.txt 4 18 12 burgess-busterbrown.txt 4 20 13 carroll-alice.txt 5 20 12 chesterton-ball.txt 5 23 11 chesterton-brown.txt 5 18 11 chesterton-thursday.txt 4 21 25 edgeworth-parents.txt 5 26 15 melville-moby_dick.txt 5 52 11 milton-paradise.txt 4 12 9 shakespeare-caesar.txt 4 12 8 shakespeare-hamlet.txt 4 12 7 shakespeare-macbeth.txt 5 36 12 whitman-leaves.txt print(round(num_chars/num_words), round(num_words/num_sents), round(num_words/num_vocab), fileid) num_vocab = len(set(w.lower() for w in gutenberg.words(fileid)))