Yes, word sense disambiguation (WSD) algorithms can be used for multilingual applications. WSD aims to determine the correct meaning of a word within a specific context, and it is applicable to different languages.

  There are several approaches for multilingual WSD. One approach is to translate the context into a common language and apply WSD algorithms developed for that language. This approach requires translation and may introduce errors due to potential inaccuracies in the translation process.

  Another approach is to develop language-specific WSD algorithms using language-specific resources such as bilingual dictionaries, parallel corpora, or machine translation systems. These resources can help in aligning and mapping word senses across different languages.

  Additionally, there are cross-lingual WSD algorithms that aim to directly disambiguate words in multiple languages. These algorithms leverage shared knowledge or patterns across languages to disambiguate word senses. They may use techniques such as parallel corpora, machine translation, or cross-lingual embeddings to learn and transfer knowledge between languages.

  However, it's important to note that WSD performance can vary across languages due to factors such as differences in language structures, availability of annotated resources, and lexical ambiguities. Some languages may have limited resources or be more challenging for WSD than others.

  In summary, WSD algorithms can be adapted and utilized for multilingual applications, but the performance may depend on the specific language and available resources.