Context-based approaches for word sense disambiguation (WSD) have certain limitations that can affect their accuracy and reliability. Here are some notable limitations:

  1. Word Sense Ambiguity: Many words in natural language have multiple senses or meanings. Context-based approaches rely solely on the surrounding words and phrases to determine the correct sense of an ambiguous word. However, context alone may not always provide sufficient information to disambiguate the intended sense accurately.

  2. Polysemy and Homonymy: Polysemy refers to the situation where a word has multiple related senses, while homonymy refers to the situation where a word has unrelated senses. Context-based approaches struggle to differentiate between these two cases accurately. For example, the word 'bank' can refer to a financial institution (e.g., Bank of America) or to the side of a river. Without additional context or knowledge, it is challenging to determine the correct sense.

  3. Sparse Context: Context-based approaches heavily rely on the words in the immediate vicinity of the target word. However, in some cases, the context may not provide enough relevant information to disambiguate the word accurately. Limiting the analysis to only the immediate context can lead to incorrect sense disambiguation.

  4. Word Order Dependency: The order of words in a sentence can affect the meaning of the sentence. Context-based approaches that solely rely on local context may fail to consider long-distance dependencies between the target word and disambiguating clues located further away in the sentence or even in previous sentences.

  5. Lack of World Knowledge: Context-based approaches typically do not utilize external knowledge sources to disambiguate word senses. These approaches do not consider general knowledge or background information on a particular topic, which can be critical for accurate disambiguation. An example would be disambiguating between the word 'bat' as a flying mammal or as a piece of sports equipment.

  6. Ambiguity Resolution for Rare Words: Context-based approaches might struggle to disambiguate rare or uncommon words since they have limited training data. These approaches often rely on statistical models trained on large text corpora, and if a word occurs infrequently, it may not have enough instances for the model to learn its different senses accurately.

  To overcome these limitations, researchers have explored other approaches combining context with knowledge bases, parallel corpora, or labeled data. By integrating additional sources of information, such as semantic networks or domain-specific resources, the accuracy of WSD systems can be improved.