Current word sense disambiguation (WSD) techniques have certain limitations that affect their accuracy and applicability in natural language processing tasks. Some of these limitations include:

  1. Polysemy: Polysemy refers to words having multiple meanings. WSD techniques often struggle to accurately disambiguate words with multiple senses, as identifying the correct sense in context can be challenging. For example, the word "bank" can refer to a financial institution or a riverbank. WSD algorithms may have difficulty distinguishing between these different senses.

  2. Ambiguity in context: While WSD algorithms aim to disambiguate words based on their surrounding context, sometimes the context itself can be ambiguous. This can lead to incorrect sense disambiguation. For instance, consider the sentence "He broke the record." Without additional context, it is unclear whether the word "record" refers to a vinyl disc or an achievement.

  3. Lack of comprehensive lexical resources: WSD algorithms heavily rely on lexical resources, such as dictionaries and word sense inventories, to map words to their senses. However, these resources may not cover all possible word senses or may not be continually updated to reflect contemporary language usage.

  4. Domain specificity: WSD techniques often perform better within a specific domain or a well-defined set of topics. When applied to a different domain or new vocabulary, the accuracy of WSD algorithms may decrease. This limitation stems from the fact that WSD models are trained on specific datasets, and their performance can vary outside that domain.

  5. Sense granularity: WSD algorithms may struggle with fine-grained sense disambiguation. For example, distinguishing between "computer mouse" and "rodent mouse" can be challenging for some algorithms. The granularity of available senses in a lexical resource may impact the accuracy of WSD techniques.

  6. Contextual ambiguity: Some words do not have clear-cut boundaries between their senses, and their meanings can be subjective or context-dependent. For example, words like "beautiful" or "interesting" can have different interpretations based on the person's perspective or the cultural context. WSD techniques may struggle to handle such contextual ambiguity.

  Overcoming these limitations is an active area of research in natural language processing. Researchers are exploring more advanced techniques, such as deep learning models, which can leverage large-scale contextual information and improve the accuracy of word sense disambiguation. Additionally, efforts are being made to create more comprehensive and up-to-date lexical resources to better address the challenges posed by polysemy and domain specificity.