There are several commonly used features in word sense disambiguation algorithms. These features aim to capture different aspects of the word and its context to help determine the most appropriate sense. Some of these features include:

  1. Lexical Features: This includes the word itself, its lemma, part-of-speech tags, and the surrounding words. These features provide information about the immediate context and help to distinguish between different senses.

  2. Syntactic Features: These features consider the grammatical structure of the sentence, such as the dependency relations between words or the constituent parse tree. Syntactic features can provide valuable information about the role and relationship of the target word with other words in the sentence.

  3. Semantic Features: These features aim to capture the meaning of the word and its context. This can be done using semantic resources such as WordNet or distributional semantic models like word embeddings. Semantic features can help to differentiate between senses based on their semantic relatedness to other words in the context.

  4. Collocation Features: These features consider fixed combinations of words that frequently co-occur with the target word. By observing collocation patterns, the algorithm can get clues about the specific sense of the word in question.

  5. Word Frequency Features: The frequency of a word or its collocations in a given corpus can also be used as a feature. Certain senses may be more frequent in a particular domain or genre, and frequency information can help in disambiguating between different senses.

  6. Word Sense Features: These features utilize existing sense inventories such as WordNet or other domain-specific knowledge bases. They can leverage the hierarchical structure and relationships between senses to guide the disambiguation process.

  7. Context Window Features: These features consider a wider context window around the target word, rather than just the immediate neighbors. By looking at a larger context, the algorithm can get a better understanding of the overall context in which the word appears.

  It's important to note that different word sense disambiguation algorithms may use variations or combinations of these features, depending on the specific approach and the resources available. The choice of features can have a significant impact on the performance of the disambiguation algorithm.