Tracing semantic shifts for Russian

This repository features the code and the dataset related to the paper "Tracing cultural diachronic semantic shifts in Russian using word embeddings: test sets and baselines" by Vadim Fomin, Daria Bakshandayeva, Julia Rodina and Andrey Kutuzov (accepted to Dialog-2019). The slides of the presentation used during Dialog-2019 are available at dialogue_slides.pdf.

Dataset

The micro.csv file in the datasets directory contains Russian adjectives manually annotated for temporal semantic shifts in the time span from 2000 to 2014.

It consists of 280 entries. For each entry, three annotators considered a word from the column WORD (e.g., свиной 'of a swine, related to a swine') and decided to what degree the word in question has changed its meaning from year in the BASE_YEAR column (e.g., 2009, the year when swine flu was widely discussed in media) to the next year. Individual annotator scores can be found in the ASSESSOR1, ASSESSOR2, and ASSESSOR3 columns. Scores are on the scale from 0 to 2; to calculate the final score, a simple arithmetic mean of the scores was taken (the ASSESSOR_MEAN column) and rounded to the nearest integer. The rounded value was considered to be the ground truth (the GROUND TRUTH column).

The macro.csv file in the same directory contains 215 Russian words. 43 of them (35 nouns and 5 adjectives) are manually picked words that have undergone semantic changes from pre-Soviet through Soviet times. There also are four fillers per each target word (152 nouns and 20 adjectives). The target words are marked as 1 and the fillers are marked as 0.

See the paper for further details of the dataset creation.

Code

The algos directory contains our implementation of the semantic shift detection algorithms used to trace semantic shifts in Russian words:

• Jaccard distance
• Kendall tau distance
• Procrustes alignment
• Global Anchors

Using the code

Given two embedding models you can evaluate what is the degree of semantic change for any given word X (must be present in both models).

Run the score_word.py script as follows:

python3 score_word.py -w X -m1 2000.model -m2 2014.model

This will print out the scores according to each of the 4 algorithms (higher score means higher similarity between the word meaning in two models):

KendallTau score: -0.05795918367346939 (from -1 to 1)
Jaccard score: 0.0 (from 0 to 1)
Global Anchors score: 0.36681556701660156 (from -1 to 1)
Procrustes aligner score: 0.17986169457435608 (from -1 to 1)

Historical embedding models for Russian

The diachronic word embedding models we used in the paper are available for downloading at https://rusvectores.org/news_history/diachrony_russian/.

BibTex

@article{fomin-et-al-2019,
  title={Tracing cultural diachronic semantic shifts in {R}ussian using word embeddings: test sets and baselines},
  author={Fomin, Vadim and Bakshandaeva, Daria and Rodina, Julia and Kutuzov, Andrey},
  journal={Komp'yuternaya Lingvistika i Intellektual'nye Tekhnologii: Dialog conference},
  pages={203--218},
  url={http://www.dialog-21.ru/media/4598/fominvplusetal-116.pdf},
  year={2019}
}