Quantifying Diachronic Language Change via Word Embeddings: Analysis of Social Events using 11 Years News Articles in Japanese and English

Shotaro Ishihara, Hiromu Takahashi, and Hono Shirai (2023). Quantifying Diachronic Language Change via Word Embeddings: Analysis of Social Events using 11 Years News Articles in Japanese and English. 9th International Conference on Computational Social Science.
https://upura.github.io/pdf/ic2s2_2023_semantic_shift.pdf
https://www.ic2s2.org/

Transcript

1. Quantifying Diachronic Language Change via
   Word Embeddings: Analysis of Social Events using
   11 Years News Articles in Japanese and English
   Research Overview
   ● We quantitatively analyzed semantic
   shifts caused by social events across
   multiple corpora and years (using
   news articles published in Japanese
   and English between 2011-2021).
   ● Studies on the analysis of social
   events have often focused on a
   single event, and it is important to
   explore more comprehensive method.
   ● RQ1: Is the semantic shift caused by
   COVID-19 larger?
   ● RQ2: Are the trends of change in
   Japan and English similar?
   ● A1&2: Yes (at least in our approach)
   Shotaro Ishihara (Nikkei Inc. shotaro.ishihara@nex.nikkei.com ), Hiromu Takahashi, Hono Shirai
   [1] How COVID-19 is changing our language: Detecting
   semantic shift in twitter word embeddings.
   [2] Semantic Shift Stability: Efficient Way to Detect
   Performance Degradation of Word Embeddings and
   Pre-trained Language Models. (AACL2022)
   Acknowledgments
   We are grateful to Kunihiro Miyazaki for
   the useful research discussions.
   Experimental Results
   ● A1: The semantic shift stability for
   2019-2020 was observed to be the
   lowest for Nikkei (ja) and NOW (en), the
   degree of change was the greatest.
   ● A2: The correlation coefficient between
   Nikkei and NOW was calculated to be
   0.66, indicating a similar trend.
   Approach
   1. Corpora are divided by year, and
   word2vec models are trained.
   2. We take two trained word2vec
   models as input and derive rotation
   matrices (R) to align their
   coordinate axes.
   3. Stability can be calculated by the
   similarities in two directions. [1]
   4. We refer to the average value of
   stab of words as semantic shift
   stability, and adopted this as a
   representative value. [2]
   Inferring Reason of Semantic Shifts
   It has the advantage of identifying words
   that exhibited a significant semantic shift
   (words with the lowest stab) in 2019-2020.
   ● Nikkei: infection, spread, corona,
   vaccine, virus, mask, infected, North
   Korea, vaccination, and epidemic.
   ● NOW: king, Scott, de, virus, masks,
   wear, mask, pi, q, and wearing.
   => Words related to COVID-19 appeared at
   the top of the lists. Note that the analysis
   for 2015-2016 implied the impact of the
   U.S. presidential election.
   

   View Slide