Meaning

Words with Consistent Diachronic Usage Patterns are Learned Earlier: A Computational Analysis Using Temporally Aligned Word Embeddings.

In this study, we use temporally aligned word embeddings and a large diachronic corpus of English to quantify language change in a data‐driven, scalable way, which is grounded in language use.

Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction

We investigate grounded language learning through real-world data, by modelling a teacher-learner dynamics through the natural interactions occurring between users and search engines.

Training Temporal Word Embeddings with a Compass

We introduce a novel model for word embedding alignment and test it on temporal word embeddings obtaining SOTA results.