Word Sense Disambiguation (WSD) is the task in Natural Language Processing (NLP) of determining which sense (meaning) of a word is used in a given context when the word has multiple meanings (i.e., it is polysemous).

Example:

Consider the word “bank” in the following sentences:

  1. She sat by the bank of the river.

  2. He went to the bank to deposit a check.

Here, “bank” has different meanings:

  • In (1), it refers to the side of a river.

  • In (2), it refers to a financial institution.

WSD helps algorithms decide the correct meaning based on surrounding words (context).

Why WSD is Important:

  • Machine Translation: Translating ambiguous words correctly.

  • Information Retrieval: Understanding query intent.

  • Chatbots / Virtual Assistants: Providing accurate responses.

  • Text Mining / Knowledge Extraction: Linking correct entities or facts.

Common Approaches to WSD:

  1. Knowledge-based Methods:

    • Use lexical resources like WordNet.

    • Algorithms: Lesk algorithm, semantic similarity measures.

  2. Supervised Learning:

    • Uses labeled corpora (sense-annotated text).

    • Algorithms: Decision Trees, SVMs, Neural Networks.

    • Limitation: Requires large, annotated datasets.

  3. Unsupervised Learning:

    • Clusters word occurrences into different senses without labeled data.

    • Uses context similarity, co-occurrence statistics.

  4. Neural / Deep Learning Methods:

    • Contextual embeddings (e.g., BERT, ELMo) have significantly improved WSD performance.

    • Fine-tuning models on WSD tasks gives state-of-the-art results.

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