  Many words have evolved in meaning as a result of cultural and social change.
Understanding such changes is crucial for modelling language and cultural
evolution. Low-dimensional embedding methods have shown promise in detecting
words' meaning change by encoding them into dense vectors. However, when
exploring semantic change of words over time, these methods require the
alignment of word embeddings across different time periods. This process is
computationally expensive, prohibitively time consuming and suffering from
contextual variability. In this paper, we propose a new and scalable method for
encoding words from different time periods into one dense vector space. This
can greatly improve performance when it comes to identifying words that have
changed in meaning over time. We evaluated our method on dataset from Google
Books N-gram. Our method outperformed three other popular methods in terms of
the number of words correctly identified to have changed in meaning.
Additionally, we provide an intuitive visualization of the semantic evolution
of some words extracted by our method
