Lexical Semantics and Word Senses › Summary

Lexical semantics studies how words carry meaning, how senses relate, and how context selects among them. The core units are word forms, lemmas, lexemes, and senses. Polysemy is the association of one lexeme with multiple related senses. Homonymy is the association of one form with unrelated meanings. Synonymy concerns sameness of meaning at the chosen granularity. Near-synonyms can differ in register, collocation, or connotation. These distinctions matter because they determine what a computational system is representing. A token is not a lemma. A lemma is not a sense. An inventory sense label is a representation, distinct from the entity or situation a particular use refers to. Each level has its own role in the pipeline.

Sense inventories provide the label space for WSD. WordNet organizes words into synsets. Semantic relations such as hypernymy and meronymy connect synsets, and lexical relations such as antonymy connect words in particular senses. FrameNet describes frames and frame elements. The documented English OntoNotes inventory groups finer WordNet distinctions. BabelNet, Wiktionary, and domain resources fill other coverage needs. Inventory mismatch is a first-class problem: results are not comparable across inventories without explicit mapping. The choice of inventory affects the difficulty of the task, the meaning of an accuracy score, and the usefulness of the result for downstream applications.

WSD is the task of choosing the intended sense of a word in context. In the presented Bayesian formulation, the decision rule combines a sense prior with a context likelihood. A discriminative classifier can estimate the posterior directly. The MFS baseline measures how often the senses selected from training counts match the gold labels on the evaluated targets. Knowledge-based methods such as Lesk use gloss overlap. Supervised methods learn classifiers from sense-tagged data. Contextual embeddings and neural models represent each token in context, enabling sense classification, sense embedding comparison, and Word-in-Context evaluation. LLMs offer prompt-based WSD; validate their output identifiers, disclose prompt and candidate choices, and investigate contamination when interpreting benchmark scores. Each approach has strengths and weaknesses. The best choice depends on the inventory, the domain, the data, and the task.

The hand-worked three-noun example showed how gloss overlap can select the riverbank sense. The executed all-POS implementation also exposed an error: the deposit sentence selected deposit.v.02 instead of the financial noun sense. The implementation demonstrated WordNet access, stemming, Lesk scoring, gloss vectors, and a tiny evaluation against an explicitly identified first-WordNet-sense proxy. The results illustrate the procedure and highlight both the power and the fragility of lexical overlap. Shared words can provide useful sense evidence, as the riverbank example shows. Exact overlap can be sparse. Inflections and synonyms can use different token strings. Those strings match only if preprocessing maps them to the same retained token. Supervised and contextual methods address some of these limitations, but they introduce their own challenges.

The practical choices are the inventory, candidate scope, evidence, and evaluation protocol. The code made one error visible: all-POS overlap selected a verb for the financial noun sentence. Use such inspected errors alongside aggregate scores when deciding which distinctions and interfaces an application needs. Next we move to sentence meaning and natural language inference.