Yes, word sense disambiguation can be achieved without relying on machine learning techniques. Machine learning approaches, such as supervised learning algorithms, have been widely used in word sense disambiguation because they have shown good performance in dealing with the inherent ambiguity of natural language. However, there are other methods that can be used as well.

  One traditional approach is rule-based word sense disambiguation. This approach involves manually defining rules or patterns that capture the different senses of a word based on linguistic knowledge or lexical resources. For example, a rule might state that if a word appears near certain context words, it is likely to have a specific sense. These rules can be created manually or derived from existing linguistic resources such as dictionaries or thesauri.

  Another approach is knowledge-based or dictionary-based word sense disambiguation. This approach relies on the use of lexical resources or knowledge bases, such as WordNet, which contain definitions, synonyms, and semantic relationships between words. By leveraging these resources, algorithms can match the context of a word with the definitions and example sentences provided to identify the most appropriate sense.

  Hybrid approaches that combine multiple techniques have also been explored. These approaches combine rule-based, knowledge-based, and statistical methods to benefit from the strengths of each approach. For example, a system may first use rule-based methods to make initial sense assignments and then refine them using statistical methods.

  It is important to note that while machine learning has been successful in achieving high accuracy in word sense disambiguation, it also requires large amounts of annotated training data and can be computationally expensive. In contrast, rule-based and knowledge-based approaches can be more interpretable and less resource-intensive.

  In conclusion, while machine learning techniques have become popular for word sense disambiguation, they are not the only approach. Rule-based, knowledge-based, and hybrid approaches can also be effective in disambiguating word senses and have their own advantages and limitations. The choice of approach depends on the available resources, the specific task requirements, and the desired trade-offs between accuracy and interpretability.