SenSE: A Toolkit for Semantic Change Exploration via Word Embedding Alignment
Keywords:Word Embeddings, Embedding Alignment, Semantic Shift, Language Change
AbstractLexical Semantic Change (LSC) detection, also known as Semantic Shift, is the process of identifying and characterizing variations in language usage across different scenarios such as time and domain. It allows us to track the evolution of word senses, as well as to understand the difference between the language used in distinct communities. LSC detection is often done by applying a distance measure over vectors of two aligned word embedding matrices. In this demonstration, we present SenSE, an interactive semantic shift exploration toolkit that provides visualization and explanation of lexical semantic change for an input pair of text sources. Our system focuses on showing how the different alignment strategies may affect the output of an LSC model as well as on explaining semantic change based on the neighbors of a chosen target word, while also extracting examples of sentences where these semantic deviations appear. The system runs as a web application (available at http://sense.mgruppi.me), allowing the audience to interact by configuring the alignment strategies while visualizing the results in a web browser.
How to Cite
Gruppi, M., Adalı, S., & Chen, P.-Y. (2022). SenSE: A Toolkit for Semantic Change Exploration via Word Embedding Alignment. Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13170-13172. https://doi.org/10.1609/aaai.v36i11.21717
AAAI Demonstration Track