Yes, word embeddings can be used for named entity disambiguation. Named entity disambiguation refers to the task of determining the correct meaning or entity for a given mention of a named entity in text. Word embeddings are dense vector representations of words that capture semantic and contextual information.

  To use word embeddings for named entity disambiguation, one approach is to represent each named entity mention and candidate entity as word embeddings. By comparing the similarity between the word embeddings of the mention and the candidate entities, the most similar candidate can be chosen as the disambiguated entity.

  Several methods can be employed to compute the similarity between word embeddings, such as cosine similarity or Euclidean distance. The similarity between the word embeddings captures the semantic relatedness between the mention and the candidate entity.

  Furthermore, contextualized word embeddings, such as BERT (Bidirectional Encoder Representations from Transformers), can also be utilized for named entity disambiguation. Contextualized word embeddings consider the surrounding context of the word, which can provide better representations for entity mentions in context.

  It is important to note that word embeddings alone may not be sufficient for accurate named entity disambiguation, especially in cases where multiple entities share similar embeddings or when there is limited contextual information. In such cases, combining word embeddings with other techniques like knowledge graphs, entity linking, or semantic parsers can improve the disambiguation accuracy.

  Overall, word embeddings are a useful tool for named entity disambiguation as they capture semantic and contextual information, and their application can enhance entity disambiguation tasks.