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https://github.com/timschott/details

Code for "Details in the Novel" - ongoing research into fictional description @ Berkeley
https://github.com/timschott/details

bert digital-humanities nlp python r word-embeddings

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Code for "Details in the Novel" - ongoing research into fictional description @ Berkeley

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Code for "Details in the Novel"



## File Map:



* `BertPreTrainDataMunge.R`

  * Partitions annotated data set into train, dev and test sets.

* `BertTrainModel.ipynb`

  * Trains `BERT` classifiers of various sizes to determine optimal model for detail classification.

* `ClauseParse.ipynb`

  * Breaks a paragraph into its clauses. Used for early exploration of manually identifying a "detail" (with Zola's *Germinal* as a test case)

* `Clustering.ipynb`

  * Explores unsupervised clustering on my dataset (via k-means and hierarchical methods).

* `DetailSummarize.R`

  * Initial aggregation of data points following annotations.

* `DetailTagging.R`

  * Annotation script: iterates over a list of samples, printing out the text in the console and attaching the user's input to a data frame. Allows a reader to tag texts without knowing the source (*prima facie*). For now, used to apply scores to 364 800 character passages.

* `Embeddings.ipynb`

  * Generates and stores word embedding values from `BERT`. Also contains a function for "augmenting" embedding vectors with additional points of information.

* `GeneralWordFrequencies.ipynb`

  * Catch-all script that calculates the relative frequency of word `w` in a text `t`.

* `GetTimeWords.R`

  * Extracts relevant words from the [Harvard Inquirer](http://www.wjh.harvard.edu/~inquirer/) dictionary.

* `KeyWordsInContext.ipynb`

  * Highlights the usage of a word of interest across corpus. Associated cli version is `kwic.py`.

* `LogOddsTables.R`

  * Aggregates the results from `LogOdds` calculations and produces charts for each class' most distinctive words.

* `LogOddsWithPriors.ipynb`

  * Implements the log-odds ratio with an informative Dirichlet prior (described in [Monroe et al. 2009, Fighting Words](http://languagelog.ldc.upenn.edu/myl/Monroe.pdf)). As adapted from a (completed) INFO-256 [assignment](https://github.com/dbamman/anlp21/blob/main/2.compare/Log-odds%20ratio%20with%20priors_TODO.ipynb)

* `PrepositionChiSquared.ipynb`

  * Runs the chi-squared test for independence using the frequency of prepositions to determine whether there is a significance difference in each preposition's usage in detail and not_detail samples.

* `ProcessedTextsToFile.ipynb`

  * Helper file for shuttling texts through a processing pipeline (see `gut_tokenize.py`) and writing their outputs to a `/processed/` dir.

* `SamplerForAnnotation.ipynb`

  * Randomly draws `n` samples of `k` length from a directory of `.txt` files. Writes samples to a (configurable) output directory, maintaining the location (start:stop char index) in each text that a given sample was drawn from in order to easily trace the sample back to its place of context in a given text file.

* `SpecificitySummarize.R`

  * Investigates the outputs from `WordnetSpecificity.ipynb`. Tallies metrics by class "label," produces visualizations, determines normality of calculated data with `stat_qq()`, etc.

* `TimeSummarize.R`

  * Investigates the frequency of "Time" words per [Harvard Inquirer](http://www.wjh.harvard.edu/~inquirer/)

* `TokensWithHighestAttention.ipynb`

  * Pools the attention weights (from `BERT`) of each token. Outputs the 100 highest and 100 lowest attention-values. Provides an easy look up for a word of interest's attention weight.

* `WordnetSpecificity.ipynb`

  * Routine to calculate a "specificity" metric for an inputted block of text (described in [Nelson 2020, Computational

Grounded Theory: A Methodological Framework](https://journals.sagepub.com/doi/pdf/10.1177/0049124117729703)). Also applies part of speech tallies for classes of interest. Writes results to file.

* `concat_txts.sh`

  * Combines .txt files in a directory into one-big `.txt` file. (I'm trying to get better at bash...)

* `get_26_gutentexts.sh`

  * Invokes Wolff's Project Gutenberg [wrapper](https://github.com/c-w/gutenberg) for a provided series of books and their Gutenberg id's. Saves each book's output as `.txt` file. (Again, trying to get better at bash.)

* `bert_interp.py`

  * Uses the [captum](https://captum.ai/) library to extract and visualize the attention weights of each token used by `BERT` during the classification of my annotated samples. As adapted from an INFO-256 [activity](https://github.com/dbamman/anlp21/blob/main/9.neural/Interpretability.ipynb)

* `gut_tokenize.py`

  * Reusable/importable text-cleaning methods for books from Project Gutenberg. Attempts to advance past the opening "boilerplate," removes chapter numbers, etc. Relatively "unopinionated" -- doesn't remove numbers, punctuation, stopwords, etc. (maintains as much as possible to keep options open for downstream analysis).

* `data/` contains the feature matrix, which holds:

  * sample name

  * parts of speech data

  * specificity scores

  * my detail "rating" annotation

* `dh-fair/` contains the poster for my presentation at the [2022 Berkeley Digital Humanities](https://digitalhumanities.berkeley.edu/dh-fair-2022) fair

* `paper/` contains the project write-up, which is 6 pages and adheres to the ACL's formatting guidelines.

* `viz/` contains graphs and tables related to the log-odds and specificity work.



## Corpus:



* austen *pride_and_prejudice*

* austen *sense_and_sensibility*

* carroll *alice_in_wonderland*

* conrad *heart_of_darkness*

* defoe *robinson_crusoe*

* dickens *bleak_house*

* dickens *great_expectations*

* dostoevsky *crime_and_punishment*

* eliot *adam_bede*

* eliot *middlemarch*

* fielding *tom_jones*

* fitzgerald *great_gatsby*

* flaubert *madame_bovary*

* gaskell *north_and_south*

* hawthorne *scarlet_letter*

* lewis *babbit*

* melville *moby_dick*

* more *utopia*

* shelley *frankenstein*

* shelley *the_last_man*

* sinclair *the_jungle*

* tolstoy *anna_karenina*

* verne *around_the_world_in_80_days*

* verne *twenty_thousand_leagues_under_the_sea*

* wells *the_time_machine*

* wilde *picture_of_dorian_grey*

* zola *germinal*

* zola *the_fortune_of_the_rougons*