Generating Algorithmic Poetry

posted in: Happenings | 0

In this post, I’m sharing the python code that Sven Anderson and I used in our “Family Weekend” class for “Technologies of Reading: Human and Machine Approaches to Literature.” We began the class with two poems from an NPR article called, “Human or Machine: Can you tell who wrote these poems?“, which presents a series of sonnets and asks readers to distinguish between ones written by people and ones generated by computers. For our class, this challenge offered an occasion to think about what might be entailed in attempting to program poetic style, and it helped to reveal what even far more sophisticated algorithms than the one we share here have failed to achieve in imitating human poetry.

The question of what constitutes poetic style has and will continue to produce varied and complicated answers, but, at bottom, it comes down to choice. This experiment in generating “poetry” represents one way of simulating this process of choice by randomly picking words according to their frequency in a particular poet’s corpus. (Just to note, some of the functions in this script are in a separate file ( to make it easier to read. You can access those functions and the accompanying text files here.

Objective: We will be able to articulate specific features of poetic writing that can distinguish computer-generated poems from ones written by people.

In this class, we’ll be working with python code in the Jupyter notebook interface. What’s great about this platform is that we can easily move between formatted text, code, images, and pretty much any other type of digital object.

A word cloud generated from Abraham Lincoln’s Gettysburg Address. The size of the words correspond to their frequency in the speech. Source:

We will use this code to produce a randomly-generated poem based on a particular poet’s style.

How might we mathematically represent authorial choice?

We can start by attempting to simulate the process by which particular authors make stylistic choices — for instance, how they choose which words to use.

In programming, we produce simulations using algorithms, which are sets of rules for the computer to follow. They are computational recipes for turning a given input into an output. Here we create an algorithm in a function for calculating the tip for a meal.

def tipCalculator (mealTotal): ### Our "mealTotal" is our input. return mealTotal * 0.20 + mealTotal ### We add 20% of the total to the input to get our output

We’ve created the function, now we can run it:


Now let’s think about how we might simulate poetic word choice to produce randomly generated poetry.

# Load some functionality and some pre-computed probabilities. import functions import pickle dickinsonProbs = pickle.load(open('dickinsonProbs.p', 'rb'))

print(dickinsonProbs['the']) print(dickinsonProbs['heart']) 0.06506088945704097 0.0009419363520150709

These numbers tell us the likelihood of picking each word if we were to draw them at random from the text: the more frequent the word in the text, the greater chance of choosing it.

from random import random def randomWord(probs): '''Given probs, a dictionary of word probabilities, this returns a word according to how frequently that word is found in the dictionary.''' rnum = random() sumprob = 0.0 for k in probs.keys(): sumprob += probs[k] if sumprob > rnum: return k return k

We can simulate this process by using a function that randomly chooses words from Dickinson’s poetry, based on her word usage.

print(randomWord(dickinsonProbs)) drum

for i in range(10): print(randomWord(dickinsonProbs)) since but them most few sinks one repeal the delight

This frequency-based approach gives us a way to simulate the poet’s process of choosing words. But after we choose a random word what comes next?But after we choose a random word what comes next? Our approach (and the approach of most text generators, for that matter) draws upon the statistical features of language, which means that the question of what comes next can be answered by considering what came before. It’s tempting to think that word sequences are consequences of grammatical rules of language, but, in practice, such rules are commonly ignored and defied. We can get a better model of language by observing the frequency of particular word combinations in actual usage. In The Information (2011), James Gleick explains how this view emerged from Claude Shannon’s mathematical theory of information. From the unit of the character to phrases to full passages, preceding items can be used to predict a range of possible items that might follow:

Immediately after the word yellow, some words have a higher probability than usual and others virtually zero. After the word an, words beginning with consonants becoming exceedingly rare…And the structure can extend over long distances: in a message containing the word cow, even after many other characters intervene, the word cow is relatively likely to occur again. As is the word horse. A message, as Shannon saw, can behave like a dynamical system whose future is conditioned by its past history. (226)

From this perspective, modeling language might be considered akin to modeling weather or some other natural phenomena, and we can build the implications of this approach into our poetry generator. We can choose the next word based on which words are likely to follow the first word, according to the poet’s usage.

dickinsonBigrams = pickle.load(open('dickinsonBigrams.p', 'rb')) shakespeareProbs = pickle.load(open('shakeProbs.p', 'rb')) shakespeareBigrams = pickle.load(open('shakeBigrams.p', 'rb')) print(shakespeareBigrams['compare']) {'thou': 0.3333333333333333, 'with': 0.16666666666666666, 'myself': 0.16666666666666666, 'thee': 0.16666666666666666, 'them': 0.16666666666666666}

In Shakespeare’s Sonnets, *thou* is the most likely word to follow *compare*. With this process of randomly choosing words likely to follow one another we can build entire poems. In the following algorithm, we combine this process with a method of making the poems rhyme by matching the last word of every odd line with a random rhyming word in every even line.

def generatePoem(probs, bigrams, lineLength, poemLength): poemLines = [] # create poemLines from probabilities for i in range(poemLength): line = functions.generateFromBigrams(probs, bigrams, lineLength) poemLines.append(line) lineCount = 1 newLines = [] for line in poemLines: if lineCount % 2 == 1: # an odd line sets the rhyme rhymedLine = line newLines.append(rhymedLine) lineCount += 1 elif lineCount % 2 == 0: # an even line must match rhyme of rhymingLine = line # preceding line try: if rhymedLine[-1] in functions.pronounce.keys(): newWord = functions.rhyme(rhymedLine[-1], 3)[0] rhymingLine[-1] = newWord # fix the rhyme to match newLines.append(rhymingLine) lineCount += 1 except: newLines.append(rhymingLine) lineCount += 1 # Now we concatenate to a single string. fullPoem = [] for line in newLines: line.append('\n') fullLine = ' '.join(word for word in line) fullPoem.append(fullLine) return ''.join(line for line in fullPoem)

Now we can generate our own “poems” from the word choice of either Shakespeare or Emily Dickinson.

Generate Shakespearean poetry

The generatePoem function takes the poet’s word probabilities, the bigram probabilities, and numbers for the line length and number of lines.

print(generatePoem(shakespeareProbs, shakespeareBigrams, 8, 14 ))

praises worse than at that i not so affined
that our time and eyes have might to give
in my bosoms ward but let me do forgive
yore those children nursed deliverd from serving with thee
which in the strength and they look what thee
from the first the morning have been mine eye
thy fair assistance in loves fire shall in ai
yet the perfumed tincture of my desire keep open
they see barren tender feeling but yet eyes alagappan
doubting the face should transport me that is kind
enforced to my deepest sense to his brief affined
and gives thee back the past i have often
not my love thy store when days when acetaminophen

The Shakespearean “poem” begins with praises as the randomly chosen word, weighted by the word frequencies in the sonnets. Each succeeding word then is chosen according to the likelihood of following the preceding one. This doesn’t mean, though, that each word is the most likely to follow the previous one; rather, like the first word, it is selected randomly with the weighted frequencies factored in. This is why running the generatePoem function will produce new lines each time. The resulting poem-like thing resembles English grammar, but most lines don’t make sense, or at least require a form of creative reading. Additionally, the rhyming words that appear at the end of the even-numbered lines come from outside of Shakespeare’s sonnets, giving the poem a bit of a mad-libs quality — I actually do like the concluding couplet.

Generate Dickinsonian Poery

print(generatePoem(dickinsonProbs, dickinsonBigrams, 8, 6))

for heaven she had come nor simplified her
consummate plush how cold i had the her
off for pearl then of love is finished
you lost was there came out time abolished
held but internal evidence gives us is overcome
he stayed away upon the merchant smiled branscome