petershan1119 / usage_change Goto Github PK

Code used in our ACL 2020 paper titled 'Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora'.

The problem of comparing two bodies of text and searching for words that differ in their usage between them arises often in digital humanities and computational social science. This is commonly approached by training word embeddings on each corpus, aligning the vector spaces, and looking for words whose cosine distance in the aligned space is large. However, these methods often require extensive filtering of the vocabulary to perform well, and---as we show in this work---result in unstable, and hence less reliable, results. We propose an alternative approach that does not use vector space alignment, and instead considers the neighbors of each word. The method is simple, interpretable and stable. We demonstrate its effectiveness in 9 different setups, considering different corpus splitting criteria (age, gender and profession of tweet authors, time of tweet) and different languages (English, French and Hebrew).

t-SNE visualization of top-50 neighbors from each corpus for word clutch, Gender split with cyan for neighbors only in female corpus and violet for neighbors only in male corpus.

t-SNE visualization of top-50 neighbors from each corpus for word dam, Age split with cyan for neighbors only in older corpus and violet for neighbors only in young corpus.

• nltk
• numpy
• scipy
• sklearn
• matplotlib
• gensim (for custom datasets)

• Download the data, embeddings, vocab files used in our paper from Drive.
• Open source/run.sh, set the variables STD_DIR to the path of the extracted data/embeddings and RES_DIR to the path where our method outputs and visualization plots will be stored.
• Run our detection method on Age split (young vs. old):

bash source/run.sh detect standard young old
(format: bash source/run.sh detect standard <split_a> <split_b>)

will save the most changed words, stability scores and so on at $RES_DIR/detect_young_old_*. Other splits you can specify include: male, female, performer, creator, sports, weekday, weekend, hebrew2014, hebrew2018, french2014 and french2018.

• Visualize the nearest neighbors of given words (dam, assist) in two subspaces:

bash source/run.sh visualize standard young old dam,assist
(format: bash source/run.sh visualize standard <split_a> <split_b> <words_in_csv>)

will save the two plots for each word as pdf at $RES_DIR/vis_young_old*.

• Ensure you preprocess the subcorpus (e.g., tokenization, handling numbers, removing URLs) and keep each sentence in a single line.
• Open source/run.sh, set the variables STD_DIR to the path of the extracted data and and RES_DIR to the path where our method outputs and visualization plots will be stored.
• Train word2vec for both the subcorpus (say corp1_posts and corp2_posts):

bash source/run.sh train custom corp1_posts corp1 corp2_posts corp2
(format: bash source/run.sh train custom <custom_split_a_txt_file> <custom_split_a_short_name> <custom_split_b_txt_file> <custom_split_b_short_name>)

will save the embeddings, vocab files at $RES_DIR/corp1* and $RES_DIR/corp2*. Note that corp1 and corp2 are shorthand names for the two subcorpus, mainly to aid bookkeeping.

• Run our detection method on the custom split:

bash source/run.sh detect custom corp1_posts corp2_posts corp1 corp2
(format: bash source/run.sh detect custom <custom_split_a_txt_file> <custom_split_b_txt_file> <custom_split_a_short_name> <custom_split_b_short_name>)

will save the most changed words, stability scores and so on at $RES_DIR/detect_corp1_corp2_*.

• Visualize the nearest neighbors of given words (word_a, word_b) in two subspaces:

bash source/run.sh visualize custom corp1_posts corp2_posts corp1 corp2 word_a,word_b
(format: bash source/run.sh visualize custom <custom_split_a_txt_file> <custom_split_b_txt_file> <custom_split_a_short_name> <custom_split_b_short_name> <words_in_csv>)

will save the two plots for each word as pdf at $RES_DIR/vis_corp1_corp2_*.

If you find this project useful, please cite the paper:

@inproceedings{gonen-etal-2020-simple,
    title = "Simple, Interpretable and Stable Method for Detecting Words with Usage Change across Corpora",
    author = "Gonen, Hila and Jawahar, Ganesh and Seddah, Djam{\'e}  and Goldberg, Yoav",
    booktitle = "Association for Computational Linguistics",
    year = "2020",
    pages = "538--555"
}

If you have any questions or suggestions, please contact Hila Gonen and Ganesh Jawahar.

This repository is GPL-licensed.