Recently, I finished reading “City of Quartz: Excavating the Future in Los Angeles” by Mike Davis. The book is an incredible journey through the history of Los Angeles, exploring its urban, social, and political landscapes. While diving into the story, I was struck by Davis’ unique writing style and the sheer number of uncommon terms he uses throughout the book. This piqued my curiosity, and I decided to find out what some of the least common words in “City of Quartz” are.
Iterative Approach to Identifying Uncommon Words
We started by defining uncommon words based on the average word frequency within the text itself. However, this approach proved insufficient, as it still included words like “entrepreneurship,” which are not uncommon in day-to-day speech. To refine our analysis, we decided to use external word frequency datasets.
Our first attempt was to use the Google Books Ngrams dataset, which provides word frequencies based on a large collection of books. We employed this dataset as an exclusion list to filter out more common words in everyday language. Despite the improvement, we still wanted a more sophisticated approach to identifying uncommon words.
We finally settled on using a sorted list of English words by frequency called `gwordlist`, based on Google’s Trillion Word Corpus. This dataset allowed us to create a frequency-based filtering system, ensuring that our list of uncommon words was more accurate and meaningful.
Preprocessing, Tokenization, and Word Frequency Calculation
import string
from collections import Counter
from nltk.corpus import stopwords
import re
# Read the gwordlist word frequency list
gwordlist_file = "/path/to/frequency-alpha-alldicts.txt"
with open(gwordlist_file, "r", encoding="utf-8") as file:
gwordlist_lines = [line.strip() for line in file.readlines() if not line.startswith("#")]
# Create a dictionary to store the word and its frequency rank
gwordlist_rank = {line.split()[1]: int(line.split()[0]) for line in gwordlist_lines}
# Read the text from a text file
file_path = "/path/to/quartz.txt"
with open(file_path, "r", encoding="utf-8") as file:
text = file.read()
# Preprocessing and tokenization
text_clean = re.sub(f"[{string.punctuation}]", " ", text)
tokens = text_clean.split()
# Remove tokens containing non-alphabet characters
tokens_alpha = [token for token in tokens if token.isalpha()]
# Lowercase conversion and stopword removal
stop_words = set(stopwords.words("english"))
tokens_lower = [token.lower() for token in tokens_alpha if token.lower() not in stop_words]
# Calculate word frequencies
word_frequencies = Counter(tokens_lower)
With the dataset in place, we began by preprocessing the text, removing unwanted characters and splitting it into individual words or tokens. We also removed tokens containing non-alphabet characters, such as numbers and punctuation marks.
Next, we converted all tokens to lowercase and removed common stopwords using the `nltk` library, ensuring a clean list of tokens for further analysis. We then calculated the occurrences of each unique word in the list using Python’s `Counter` class from the `collections` module.
Filtering Uncommon Words with the `gwordlist` Datas
# Define a threshold for uncommon words based on gwordlist rank
uncommon_rank_threshold = 10000 # You can adjust this value according to your needs
# Filter uncommon words
uncommon_words = [(word, freq, gwordlist_rank.get(word, uncommon_rank_threshold + 1))
for word, freq in word_frequencies.items()
if gwordlist_rank.get(word, uncommon_rank_threshold + 1) > uncommon_rank_threshold]
To determine the rarity of words in “City of Quartz,” we compared the words in the book against the `gwordlist` dataset. We defined a threshold for uncommon words based on the rank of words in the `gwordlist` dataset. If a word’s rank was higher than the threshold, it was considered uncommon.
Sorting and Printing the List
# Sort the list of uncommon words by their rarity (gwordlist rank)
uncommon_words_sorted = sorted(uncommon_words, key=lambda x: -x[2])
# Print the list of most uncommon words and their frequencies in the book
for word, freq, rank in uncommon_words_sorted:
print(f"{word} (Frequency: {freq}) - Rank: {rank}")
Finally, we sorted the list of uncommon words by their frequency in the book and printed the most frequent, most uncommon words. This gave us an insight into the unique vocabulary used by Mike Davis in “City of Quartz.”
Results
zaitech (Frequency: 2) - Rank: 161242 brogued (Frequency: 1) - Rank: 155591 alchemize (Frequency: 1) - Rank: 147303 reindict (Frequency: 1) - Rank: 145838 phoniest (Frequency: 1) - Rank: 145076 unheroically (Frequency: 1) - Rank: 140097 mindbending (Frequency: 1) - Rank: 139654 cooptative (Frequency: 1) - Rank: 132787 carpetbagging (Frequency: 1) - Rank: 130357 polemicize (Frequency: 1) - Rank: 126281 reassemblage (Frequency: 1) - Rank: 124533 vapidly (Frequency: 1) - Rank: 124468 emblematize (Frequency: 2) - Rank: 122755 unbeholden (Frequency: 1) - Rank: 122208 quintupling (Frequency: 1) - Rank: 120877 monsignors (Frequency: 1) - Rank: 120514 endower (Frequency: 1) - Rank: 118853 villify (Frequency: 1) - Rank: 118793 redbaiting (Frequency: 1) - Rank: 118515 spreadeagle (Frequency: 1) - Rank: 118437 ecumenicism (Frequency: 1) - Rank: 118353 polymathic (Frequency: 1) - Rank: 118183 muckrake (Frequency: 1) - Rank: 117606 intermural (Frequency: 1) - Rank: 117573 acrobatically (Frequency: 1) - 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This experiment gives a glimpse on how computational methods can provide insights into the language and style of an author, enriching our understanding and appreciation of their work.
Please note that the “we” in this article is me and a LLM who helped with the code generation and rewrote this post for clarity.
Hi Giacomo,
Your work is inspiring. Thank you for making and sharing it.
I learned of your work via a coworker’s link to The Infinite Conversation, then I found “Down the Meta Hole” which I loved. I haven’t yet explored most of your thoughtful projects, but I look forward to doing so.
I am an artist in Los Angeles (originally from Oregon) and I’m currently reading “City of Quartz”, although I’m only at the beginning.
You can see some of my paintings at the link below, and you might enjoy a project I designed along with my wife http://www.misteralphabet [dot] com
Warm Regards from Eugene, Oregon
-Marshall