Lexical Semantics and Word Senses › Words, Senses, and Lexemes

Before we can disambiguate a word, we need to distinguish its spelling and pronunciation from its grammatical analysis, lexical entry, and meaning in context. In NLP these distinctions need explicit terms. This need for precision is not terminological fussiness. Every stage of a pipeline makes a commitment about which object it is manipulating. If a tokenizer emits the string banked and a lemmatizer maps it to bank, the system has moved from a word form to a lemma. If a sense inventory then offers five noun senses and three verb senses for bank, the system must decide which part of speech and which sense are relevant. Confusing these levels leads to evaluation errors, annotation errors, and misleading claims about model performance.

Keep these levels separate when you build or evaluate an NLP pipeline. A tokenizer produces tokens. A lemmatizer maps tokens to lemmas. A sense inventory maps lemmas to senses. A WSD system maps a token in context to one of those senses. If we confuse tokens with lemmas or lemmas with senses, our evaluation and our error analysis become muddled. For example, a system that reports "accuracy on the word bank" without saying whether it evaluated noun senses only, verb senses only, or all parts of speech has reported a number whose meaning is unclear. A system that evaluates on lemmas but claims to have evaluated word forms has misdescribed its task. A system that uses one version of WordNet but compares its score to a paper that used another version may be making an invalid comparison. Precision about lexical units is therefore a prerequisite for precision about results.

The word bank illustrates the point. The string bank is a word form. The lemma spelling bank can index several lexemes and their senses. The inventory supplies the candidates, and a part-of-speech tag can narrow the list. The token bank in a particular sentence is one occurrence. The notebook uses depository_financial_institution.n.01 to identify the financial reading. None of these is the same object. The token is an event in a text. The word form is a type of surface realization. The lemma is an index. The sense is a conventional meaning. Inventory-based WSD assigns a sense from the declared inventory to a word occurrence in context.

Polysemy is the association of one lexeme with multiple related senses. Some relationships recur across words: an institution and its building, a container and its contents, or an animal and its meat. Others are less regular. These patterns help describe how usages are related; the examples alone do not explain why particular common words acquired their present number of senses.

The wider family of related meanings of the form run spans verb and noun uses. These are related lexical uses, not one inflectional paradigm:

• to move quickly on foot ("she ran to the station")
• to operate or manage ("he runs a small company")
• to flow ("the river runs north")
• to function ("the engine runs smoothly")
• to stand as a candidate ("she is running for office")
• a continuous period ("a run of good luck")
• an unraveling of stitches in stockings ("a run in her tights")
• a baseball score ("the team scored two runs")

These uses can be related conceptually through motion, continued activity, or progress. The candidacy use fits a race metaphor, while a stocking run involves successive stitches coming undone. These are ways to interpret present-day relationships, not proof of a single historical development for every sense. Cross-category relations and uncertain historical pathways also show why a broad family of meanings should not be confused with one lexeme's inflectional forms.

Polysemy often follows regular patterns. These patterns are productive, and speakers apply them to new words without explicit instruction:

• Animal and meat: chicken, lamb, turkey
• Container and contents: bottle, can, glass
• Institution and building: school, bank, church
• Creator and work: Dickens in "I am reading Dickens"
• Count and mass: beer ("a beer" versus "some beer"), paper
• Physical object and information: book, magazine, DVD

Regular polysemy is a challenge for sense inventories because it produces an open-ended set of uses. A dictionary cannot list every possible metonymic extension. A WSD system trained on a fixed inventory may encounter a use that fits no listed sense, or it may be forced to choose between two senses that are really the same underlying concept. For example, if a newspaper reports that "the newspaper fired its editor," this occurrence of newspaper selects the publishing organization: an organization can fire an editor. Other sentences can select the publication or the physical copy. A fine-grained inventory might list these as separate senses. A coarse inventory might merge them. Interpreting the organization reading is different from naming its sense in a particular inventory. When a system's output contract requires a single inventory label, it must make that discrete choice. This mismatch between natural understanding and forced-choice labeling is one of the recurring tensions in WSD.

Polysemous uses need not always force an exclusive choice. In "The book is heavy but interesting," heavy describes the physical object while interesting describes its content. Both aspects are available in the same sentence. This is a different example from "The bank raised its rates," which selects the institution reading and does not by itself show simultaneous use of a building sense. A discrete WSD label can be useful while still leaving such related aspects implicit.

Several kinds of evidence can help distinguish homonymy from polysemy.

• Etymology: Different historical origins can support a homonymy analysis.
• Relatedness: Perceived relatedness can support a polysemy analysis.
• Zeugma: Forcing two readings into one coordination can sound odd. "The bank was steep and charged high fees" is zeugmatic, which suggests distinct senses.
• Translation: Different conventional translations can suggest a distinction useful for a bilingual task. Languages divide meaning differently, so translation alone does not establish a sense boundary.
• Distribution: Different collocational patterns can supply evidence for a sense distinction.

Zeugma is a useful intuition pump, but it is not a reliable linguistic test. Some polysemous senses also produce zeugmatic sentences, and some homonyms can be coerced into puns. Lexicographers can use different criteria to separate homonyms from polysemous senses. If you are building a WSD system, you should not assume that the inventory's decision matches a theoretical truth. You should ask instead whether the distinction is useful for your task and whether annotators can apply it consistently.

Homonymy is the association of one form with unrelated meanings. Bank in the financial sense and bank in the river sense are classic homonyms. Their immediate histories differ: the financial word came through Middle French banque or Old Italian banca, which literally meant bench. Merriam-Webster describes the river word as probably Scandinavian and akin to Old Norse bakki. These qualified histories should not be turned into a certain direct derivation or a claim that the forms have no more distant common ancestry. Bat (the animal) and bat (the club) are homonyms. Bass (the fish) and bass (the low sound) are homonyms, and they are also homographs, since they are spelled the same but pronounced differently. Homonymy can involve homographs, which share spelling, homophones, which share pronunciation, or both. Sea and see are homophones but not homographs. Bank financial and bank river are homographs and homophones in their shared spelling and pronunciation, while their meanings are conventionally treated as unrelated.

Judgments of semantic relatedness can be graded. Carston's (2021) review describes relatedness ratings ranging from low-relatedness homonyms to highly related metonymic senses. In a dictionary, homonyms usually get separate headwords, while polysemous senses are grouped under one headword. In WordNet, both are represented as separate synsets, so the distinction is not always visible from the resource alone. For WSD, the practical question is whether the senses are distinct enough that a context model should choose between them. Two senses may be historically unrelated but functionally close in a particular domain, or historically related but functionally distinct in a task. The inventory designer must decide which difference matters for the intended use.

The distinction matters for lexicon design and for evaluation. If two senses are historically unrelated but cognitively close in a particular domain, annotators may struggle to label them consistently. If two senses are historically related but functionally distinct in a task, a coarse inventory may merge them and lose information the application needs. A machine translation system, for example, may need to distinguish two related senses if they translate differently. A sentiment system may not need the distinction if both senses carry the same polarity. The right granularity should therefore be chosen for the task.

Synonymy is equivalence of lexical meaning at a stated sense and granularity. Absolute synonyms would preserve meaning and communicative effect across the relevant contexts. Matching the referent in one sentence is not enough: the winner and my neighbor can denote the same person without being synonyms. Edmonds and Hirst (2002) describe absolute synonymy as rare. Near-synonyms can differ in register, connotation, collocation, or syntactic frame. The practical consequence is that a thesaurus entry is not a license to substitute freely. A substitution that preserves truth conditions may still change tone, imply a different social relationship, or sound unnatural. Natural language generation systems that ignore these differences produce text that is grammatically correct but pragmatically awkward.

Consider buy and purchase. Both describe acquiring something for money, but purchase is more formal and has documented commercial uses. "She purchased a used bicycle" sounds slightly stilted. "She bought a used bicycle" is neutral. Even idiomatic patterns need checking rather than absolute substitution rules: buy time is conventional, but purchase time can also be used figuratively. In the shared acquisition sense, replacing buy with purchase can make an ordinary sentence sound formal. A machine translation system that ignores the difference may choose a word that is too formal for the surrounding sentence.

Consider big and large. In "a big house" and "a large house," both adjectives can describe the house's physical size. "Big brother" can mean an older brother; "large brother" describes size instead. This does not mean that large lacks figurative or evaluative uses: a person can have a large influence. A generator that treats the two adjectives as interchangeable can produce unnatural text. These differences are not captured by a simple list of synonyms. They are captured by distributional patterns, collocations, and pragmatic knowledge about how speakers use the words in specific situations.

The distributional hypothesis explains why this is difficult. The hypothesis motivates giving similar representations to words used in similar contexts; the resulting vector similarities depend on the corpus, representation, training objective, and similarity measure. But similarity is not identity. Both hot and cold fit the frame “The water was ___,” even though they express opposite ends of the temperature scale. Grave’s 2014 thesis lists both buy and sell among the neighbors of purchase in its distributional thesaurus (Table 2.5). Such vector similarity can reflect relatedness without establishing synonymy. Synonymy requires an additional claim about meaning, and that claim is context-sensitive. Vector similarity can provide evidence of comparable usage environments when the representation and comparison preserve similarities in how words are used. It does not tell us that they are interchangeable in a particular sentence. For that, we need a theory of semantic equivalence, or at least a task-specific criterion for when a substitution is acceptable.

WordNet handles synonymy by fiat. A synset is a set of words that the lexicographers judged to be synonymous in at least one sense. The synset for the financial institution sense includes bank, depository financial institution, banking concern, and banking company. These are not interchangeable in every sentence, but they share a concept. The synset is a useful abstraction, not a claim of full substitutability. It groups words that can be used to express the same concept in some contexts, and it leaves the details of register and collocation to the user. This is a reasonable design choice for a lexical database, but it means that a synset should not be treated as a set of perfect synonyms.

Lexical overlap depends on the sense and context. In some medical services, a client is someone receiving care. A lawyer’s client is receiving legal services, which does not establish a medical role. Car and automobile overlap in everyday speech, but the Oxford Advanced Learner's Dictionary labels automobile North American English. A sense inventory must decide whether such differences are lexical senses or pragmatic nuances. If every register difference became a separate sense, the inventory would explode. If no register difference were represented, applications that need stylistic control would lose information. The design choice depends on the intended use of the inventory and on the level of granularity that annotators can apply reliably.

Synonymy and polysemy are only part of the lexical semantic network. Several other relations appear repeatedly in lexical resources and in downstream inference. These relations are useful because they turn a flat list of senses into a structured graph. The graph supports reasoning, query expansion, and constraint propagation. It also gives WSD systems additional evidence: if a word is used in a particular sense, its neighbors may be constrained by lexical relations. A system that knows dog is a hyponym of animal can infer that a sentence about a dog is also about an animal. A system that knows wheel is a meronym of car can infer that a sentence about a wheel may be relevant to a car. These inferences are not always valid, but they provide useful soft evidence.

• Hyponymy and hypernymy: A hyponym is a more specific term; a hypernym is a more general term. Dog is a hyponym of animal. Car is a hyponym of vehicle. Hyponymy is transitive and supports inheritance: if every dog is an animal, and every animal is an organism, then every dog is an organism.
• Meronymy and holonymy: A meronym is a part, member, or substance of a holonym. Wheel is a part meronym of car. Tree is a member meronym of forest. For a car body made of steel, steel is a substance meronym of that body. The relation is not transitive in the same way as hyponymy.
• Antonymy: Antonyms are opposites. Gradable antonyms like hot and cold admit intermediate degrees. Within the ordinary life-status classification of an organism, alive and dead are complementary alternatives at a given time. Relational antonyms like buy and sell describe the same situation from opposite perspectives.
• Troponymy: A troponym is a manner-specific verb related to a more general verb. Walk is a troponym of move. March is a troponym of walk. WordNet uses troponymy to organize its verb hierarchies.
• Entailment: One predicate may entail another under specified sense and argument mappings. In the ordinary sleep sense, someone who snores is sleeping. WordNet lists buy and pay as an example of lexical entailment. Entailment is a relation between propositions, sometimes encoded between verb senses.
• Causation: A verb can encode causing an event. In its literal life-and-death sense, x kills y means that x causes y to die. Causation links selected verb senses with their argument roles; merely supplying food to someone does not guarantee that they eat it.

These relations form a graph over senses. The graph supports query expansion, textual inference, and lexical simplification. It also gives WSD systems additional evidence: if a word is used in a particular sense, its neighbors may be constrained by lexical relations. The graph is not a complete semantic theory. It is a practical representation that captures many useful regularities while leaving others implicit. A WSD system can use the graph as a source of features, a source of constraints, or a source of background knowledge. The quality of the graph matters, because errors in the relations propagate to the inferences. A missing hypernym link can prevent an inference. A spurious link can produce an incorrect inference. Lexical resources are therefore not neutral background data. They are structured knowledge with their own design choices and limitations.