In traditional evaluations such as word similarity and word analogy, the aforementioned context-aware word embeddings work well since semantic information plays a vital role in these tasks, and this information is naturally addressed by word contexts.
in real-world applications, such as text classification and information retrieval, word contexts alone are insufficient to achieve success in the absence of task-specific features.