Lexical Semantics and Word Senses

Consider the word bank. In "She deposited her paycheck at the bank," the intended reading is a financial institution. In "The fisherman sat on the bank of the river," it names the sloping ground beside water. In "The pilot had to bank the aircraft," the verb means to tilt the aircraft laterally. The spelling is identical. The meanings are not. The examples differ in grammar and context as well as interpretation.

Lexical semantics studies how word forms relate to meanings and how those meanings relate to one another. One form can carry several meanings, and different forms can express similar meanings. Metaphor, metonymy, and specialization provide some recurring ways to relate usages. The bank examples make the distinction concrete: recognizing the spelling alone leaves the contextual choice unresolved.

Word-sense disambiguation, or WSD, is the task of assigning the intended sense to a word in context. It is one of the oldest problems in natural language processing, and one of the most instructive. WSD forces us to confront questions that recur throughout language AI:

• What counts as a meaning, and who decides?
• How fine-grained should semantic distinctions be?
• How much can context resolve, and where does world knowledge take over?
• How do we evaluate a semantic decision when annotators themselves disagree?
• When does an explicit sense label help an application, and when is it an unnecessary intermediate step?

These questions connect directly to material you have already seen. The distributional hypothesis gives us a way to represent meaning through context, but it also shows why distributional similarity is not the same as synonymy. TF-IDF and word embeddings give us vector representations, but a static embedding lookup assigns the same vector to each occurrence of a word type, so the lookup alone does not distinguish its contextual senses. Subword tokenization, sequence labeling, and contextual transformers all become relevant when we try to build sense-aware systems. A word tokenizer segments text into word occurrences. A subword tokenizer may split an occurrence into several pieces, which must be aligned back to the target word for WSD. A sequence labeler shows how labels can be assigned to individual tokens. A contextual transformer shows how the representation of a token can depend on the whole sentence. Each of these ideas prepares us to ask a more precise question: when a token appears in context, which conventionalized meaning should we associate with it?

This chapter builds the conceptual and practical foundation for lexical semantics in language AI. We start with the basic units: word forms, lemmas, lexemes, and senses. We then examine polysemy, synonymy, and other lexical relations. Next we survey lexical resources, especially WordNet, and the design choices that make sense inventories both useful and contentious. We formalize WSD and study knowledge-based, supervised, and contextual approaches. A worked example and a small implementation show how the pieces fit together. We close with limitations and the practical role of lexical semantics in modern systems. The goal throughout is not only to define terms, but to show why each distinction matters when you build, evaluate, or interpret a language AI system.