In natural language processing tasks, word vector evaluation is an important aspect for measuring the...

Word similarity tasks typically employ manually labeled datasets to evaluate word vectors, compute the cosine similarity between word vectors, and compare it with the manual labeling result.

Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.

The word analogy task evaluates the capability of word vectors in capturing semantic relationships between words, for example, by determining whether "king - man + woman = ?" is close to "queen".

Extrinsic evaluation is the main method used for evaluating word vectors because it directly reflects the performance of word vectors in real-world application tasks.