Lexical Semantics is the study of how words carry meaning and how those meanings relate to each other within a language. A central challenge in lexical semantics is dealing...
Lexical Semantics is the study of how words carry meaning and how those meanings relate to each other within a language. A central challenge in lexical semantics is dealing with ambiguity and polysemy—where a single word can have multiple meanings depending on context. For example, the word “bank” can mean a financial institution or the side of a river.
Word Sense Disambiguation (WSD) is the computational task of identifying the correct meaning of a word based on its context. Effective WSD is crucial for many NLP applications, such as machine translation, information retrieval, and sentiment analysis, since misunderstanding a word’s sense can lead to incorrect interpretations.
To address these challenges, lexical databases like WordNet play a vital role. WordNet organizes English words into sets of synonyms called synsets, each representing a distinct concept or sense. It also encodes relationships between these synsets—such as hypernyms (generalizations), hyponyms (specializations), and antonyms—forming a rich semantic network. This structured representation helps algorithms better understand word meanings and their relations.
By leveraging WordNet, computational systems can compare context words with candidate senses, improving their ability to disambiguate meanings. For example, if the surrounding words relate to finance, the system can infer that “bank” likely refers to a financial institution rather than a riverbank.
In summary, handling ambiguity and polysemy through WSD, supported by lexical resources like WordNet, enhances the accuracy of language understanding in NLP, enabling more precise and context-aware applications.