Lexical Semantics, Compositional Meaning and Discourse

Introduction

Natural language understanding requires more than recognizing words or producing grammatically acceptable sentences. A system must determine what words mean in context, how meanings combine, who did what to whom, what unstated information is intended, and how individual sentences form coherent discourse. These problems constitute the semantic and discourse layers of Natural Language Processing (NLP).

Lexical semantics studies the meaning of words and lexical units. Compositional semantics explains how the meanings of parts combine to produce the meaning of a larger expression. Discourse semantics examines relations across sentences, including coherence, topic development, temporal progression and reference. Pragmatics investigates how speakers communicate meanings that are not fully encoded in linguistic form. Ontologies and knowledge graphs provide structured representations that allow NLP systems to connect language with entities, relations and world knowledge.

Contemporary language models have substantially improved contextual interpretation, but they do not eliminate these theoretical issues. They may recognize frequent patterns while still confusing word senses, resolving pronouns incorrectly, missing implied meanings, or producing statements unsupported by a knowledge base. A rigorous understanding of semantics and discourse is therefore essential for building reliable question-answering, information extraction, dialogue, summarization, translation and reasoning systems.

Learning Objectives

After completing this chapter, a learner should be able to:

1. Distinguish lexical meaning, sense, reference and denotation.
2. Explain polysemy, homonymy, synonymy, antonymy and lexical relations.
3. Apply word-sense disambiguation methods and evaluate their limitations.
4. Analyse semantic roles and predicate–argument structures.
5. Explain compositionality, scope, negation and quantification.
6. Distinguish entailment, contradiction and neutrality.
7. Resolve coreference and discourse deixis using linguistic and computational evidence.
8. Analyse pragmatic inference and discourse relations.
9. Describe the role of ontologies and knowledge graphs in contextual language understanding.
10. Critically evaluate current neural and knowledge-enhanced approaches.

1. Lexical Semantics: Meaning at the Word Level

1.1 Word forms, lemmas, senses and references

A word form is an observed surface expression, such as flies, flew or flying. A lemma is an abstract dictionary form, such as fly. A lemma may have multiple senses, each representing a relatively distinct interpretation. In The bat flew from the cave, bat denotes an animal; in The player swung the bat, it denotes sporting equipment.

A sense is not identical to a physical object. The expression the morning star and the evening star can refer to the same planet while presenting different senses. The distinction between sense and reference is important in information extraction and entity linking. Sense concerns the conceptual or linguistic interpretation; reference concerns the entity, event or situation identified in a particular context.

Denotation is the set of entities or situations to which an expression applies. The denotation of birds may be a set of birds, whereas the denotation of run is more naturally associated with events or eventualities. In computational semantics, expressions are therefore often assigned different semantic types, such as entities, truth values, properties and relations.

1.2 Lexical relations

Words are organized through several relations:

These relations support query expansion, taxonomy induction, recommendation and semantic search. They are not always symmetric. If a sparrow is a bird, a bird is not necessarily a sparrow. Similarly, car and vehicle are related by hyponymy, but substituting one for the other can change truth conditions.

1.3 Polysemy and homonymy

Polysemy occurs when one lexical form has related senses, as in paper meaning a material, an academic article or a newspaper. Homonymy occurs when identical forms have unrelated meanings, as in bank meaning a financial institution or the side of a river. The distinction is theoretically useful but often difficult to apply consistently because relatedness is gradual.

Word-sense inventories are used in resources such as WordNet, which organizes senses into synsets and links them through semantic relations. However, inventories may be too coarse for some applications and too fine for others. A legal information system may need to distinguish senses that a general dictionary merges, while a broad search engine may benefit from treating them as one cluster.

1.4 Distributional meaning

The distributional hypothesis states that linguistic expressions occurring in similar contexts tend to have related meanings. In a vector-space model, a word is represented by a vector derived from its contextual distribution. Similarity may be estimated using cosine similarity:

Here, u and v are word vectors, u · v is their dot product, and ||u|| and ||v|| are their Euclidean lengths. The measure is applicable to non-zero vectors and is commonly used to compare embedding directions. It does not, by itself, distinguish genuine semantic similarity from topical association. For example, doctor and hospital may be close because they co-occur, even though they are not synonyms.

Static embeddings assign one vector to a word type, making them weak at representing polysemy. Contextual encoders generate representations influenced by surrounding text, so bank in river bank and bank loan can receive different representations. Nevertheless, contextual similarity is not equivalent to a symbolic sense inventory, and model representations may reflect social stereotypes or corpus-specific biases.

2. Word-Sense Disambiguation

Word-sense disambiguation (WSD) selects the intended sense of an ambiguous expression in context. Consider:

• The crane lifted the steel beam.
• The crane stood beside the marsh.

The first sentence supports the machinery sense; the second supports the bird sense.

WSD systems traditionally used four strategies:

1. Knowledge-based methods, which compare context with dictionary definitions, glosses or lexical relations.
2. Supervised methods, which learn from manually sense-labelled examples.
3. Semi-supervised methods, which combine a small labelled set with large unlabelled corpora.
4. Contextual neural methods, which use transformer representations and classification or similarity objectives.

A simplified supervised formulation predicts the sense with the largest posterior probability:

where w is the target word, C is its context, s ranges over candidate senses, and ŝ is the selected sense. The formulation is general; the probability may be estimated by a classifier, a language model or a knowledge-based scoring function.

WSD is evaluated with accuracy, precision, recall and F1 when labelled test data are available. Evaluation is complicated by domain shift, annotation disagreement and mismatched sense inventories. A model trained on news may perform poorly on biomedical text because domain-specific senses and terminology differ.

3. Semantic Roles and Predicate–Argument Structure

Syntax identifies structural relations such as subject and object. Semantics asks what those participants contribute to an event. Semantic roles provide a general vocabulary for this analysis.

Common roles include:

• Agent: intentional initiator of an event.
• Patient: entity affected by an event.
• Theme: entity moved, located or involved in an event.
• Experiencer: entity that perceives or feels something.
• Instrument: means by which an action occurs.
• Recipient: entity receiving something.
• Source: origin of movement or transfer.
• Goal: endpoint of movement or transfer.
• Location: place associated with an event.
• Time: temporal specification.

In Maya opened the door with a key, Maya is the Agent, the door is the Patient, and a key is the Instrument. In The key opened the door, the key is not necessarily an intentional Agent; it functions as an enabling cause or Instrument. This illustrates why semantic roles cannot always be read directly from grammatical subject and object positions.

Predicate–argument structure represents a predicate together with its required and optional participants. The verb give commonly requires a giver, a thing given and a recipient. The sentence Ravi gave the researcher a dataset can be represented informally as:

Such structures support semantic role labelling, information extraction, event detection and question answering. PropBank labels roles relative to individual predicates, whereas resources such as FrameNet organize meanings into broader conceptual frames. PropBank is often practical for corpus annotation; FrameNet provides richer lexical-semantic theory but may be more difficult to scale.

4. Compositional Meaning

The principle of compositionality states that the meaning of a complex expression depends on the meanings of its parts and the way they are syntactically combined. It does not imply that every expression is fully predictable from its words. Idioms such as spill the beans require stored or construction-specific knowledge.

4.1 Typed composition

A noun such as scientist can denote a set of entities. An adjective such as young can modify that set, producing the subset of young scientists. A transitive verb such as admire denotes a relation between an admirer and an admired entity. Composition combines these meanings according to grammatical structure and semantic type.

For example, in The student read the article, the noun phrases identify participants and read supplies a reading event. A semantic parser may derive an event representation containing:

The representation abstracts away from the exact surface order while preserving the central meaning.

4.2 Scope, negation and quantification

Meaning depends not only on lexical items but also on the scope of operators. Compare:

• Every student read a paper.
• A paper was read by every student.

The first sentence may allow different papers for different students; the second more strongly suggests one paper, although context can affect interpretation. Negation also creates scope distinctions: The system did not detect every error usually means that at least one error was missed, whereas The system detected not every error is similar but more marked.

Quantifier interpretation can be represented using logical notation. The sentence “Every analyst reviewed a report” has a common reading approximated by:

Here, ∀ means “for every,” ∃ means “there exists,” and the formula states that each analyst reviewed at least one report. This does not entail that all analysts reviewed the same report. A different quantifier scope would be:

which states that there is one report reviewed by every analyst. Formal representations make ambiguity explicit, but practical interpretation requires discourse context and world knowledge.

4.3 Distributional and symbolic composition

Neural models compose meaning through learned transformations over contextual representations. Symbolic systems compose explicit logical or typed structures. Neural methods handle variation and incomplete information well, while symbolic methods offer transparency, constraint enforcement and more reliable variable binding. Hybrid systems seek to combine both: neural components identify entities and relations, and symbolic components check consistency or perform deductive reasoning.

5. Entailment, Contradiction and Semantic Inference

Textual entailment holds when a hypothesis must be true given a text. If the text says, “The laboratory purchased three microscopes,” it entails, “The laboratory purchased equipment,” assuming microscopes are equipment. It does not entail, “The microscopes were expensive.”

Natural Language Inference (NLI) commonly classifies a pair as:

• Entailment: the hypothesis follows from the premise.
• Contradiction: the hypothesis conflicts with the premise.
• Neutrality: the premise does not determine the hypothesis.

Entailment can be viewed as a relation between propositions, but practical systems must handle lexical variation, negation, quantifier scope, temporal information and presupposition. A model may rely on superficial cues, such as lexical overlap, rather than genuine inference. Robust evaluation therefore includes adversarial examples and out-of-distribution data.

Important distinctions include:

• The vehicle stopped does not entail The vehicle was permanently unusable.
• Some participants arrived does not ordinarily entail Some participants did not arrive.
• Amira stopped smoking conventionally suggests that Amira smoked previously; this is a presupposition-like inference rather than ordinary entailment in every logical treatment.

6. Coreference and Discourse Reference

Coreference occurs when different expressions refer to the same entity. In Leena submitted the report because she had finished the analysis, she refers to Leena. Anaphora is a broader relation in which an expression depends on an antecedent; not every anaphoric relation is strict identity. In The car crashed, and the engine caught fire, the engine is associated with the car but denotes a part, not the same entity.

Coreference resolution usually involves:

1. Mention detection.
2. Candidate generation.
3. Compatibility scoring.
4. Clustering mentions into entities.

Useful features include grammatical agreement, recency, syntactic constraints, semantic type, discourse prominence and world knowledge. A system should not resolve pronouns by recency alone. In The trophy does not fit in the suitcase because it is too large, it plausibly refers to the trophy; in because it is too small, it more plausibly refers to the suitcase. Physical knowledge is required.

Reference may be endophoric, referring within the text, or exophoric, referring to the external situation. Deictic expressions such as here, there, now, tomorrow and I depend on speaker, time and location. Dialogue systems must model these parameters explicitly.

7. Pragmatics and Implicature

Semantics concerns relatively encoded meaning; pragmatics concerns meaning inferred from use and context. The same sentence can perform different actions depending on circumstances. Can you open the window? is grammatically a question about ability but commonly functions as a request.

Grice’s cooperative framework describes conversational expectations through maxims of quantity, quality, relation and manner. A speaker who says, “Some reviewers approved the paper,” may implicate that not all reviewers approved it, although the literal meaning is compatible with all reviewers approving it. Such implicatures are defeasible: the speaker can cancel the inference by adding, “In fact, all of them did.”

Pragmatic interpretation uses:

• Speaker and listener goals.
• Shared knowledge.
• Physical and social context.
• Conversation history.
• Presuppositions and implicatures.
• Politeness, indirectness and social norms.

NLP systems face difficulty because pragmatic meanings are often culturally variable and under-specified. A conversational agent that interprets every utterance literally may be unhelpful or socially inappropriate. Systems used in healthcare, education or public services require particular caution because incorrect assumptions about intent can have serious consequences.

8. Discourse Structure and Coherence

A discourse is not merely a sequence of independent sentences. It has structure created by discourse relations. Common relations include:

Discourse coherence depends on entity continuity, lexical cohesion, rhetorical structure and plausible relations. Rhetorical Structure Theory represents spans of text as related nuclei and satellites. The Penn Discourse Treebank and similar resources annotate connectives and discourse relations, supporting discourse parsing and argument mining.

Topic segmentation identifies shifts in subject matter. Discourse parsing predicts relations among clauses or sentences. Summarization benefits from both because a good summary should preserve central claims, evidence and causal or contrastive organization rather than selecting sentences by frequency alone.

9. Ontologies and Knowledge Graphs

An ontology specifies concepts, properties, relations and constraints in a domain. A medical ontology may represent diseases, symptoms, drugs and treatment relations. A knowledge graph stores entities and relations, often as triples:

For example:

A triple may include provenance, time, confidence and source. Knowledge graphs support entity linking, semantic search, recommendation, question answering and fact checking.

Ontologies differ from simple taxonomies. A taxonomy primarily organizes “is-a” relations; an ontology can also specify part–whole relations, constraints, attributes and event types. The Web Ontology Language and description logics provide formal tools for representing classes and relationships, although industrial systems often combine formal ontologies with probabilistic extraction.

A typical knowledge-enhanced NLP pipeline is:

1. Detect mentions in text.
2. Link mentions to canonical entities.
3. Extract relations and events.
4. Normalize dates, quantities and types.
5. Store facts with provenance.
6. Query or reason over the graph.
7. Present evidence and uncertainty to users.

Knowledge graphs do not automatically guarantee truth. They may contain outdated, conflicting or culturally incomplete information. Open-world reasoning means that failure to store a fact does not necessarily imply that the fact is false. Provenance and temporal validity are therefore essential in high-stakes applications.

10. Applied Case: A Clinical Literature Assistant

Background

Consider an illustrative system designed to help researchers search clinical literature. It receives the query: “Find studies showing that Drug A reduces severe migraine episodes in adolescents without increasing liver toxicity.”

Problem

The system must identify the correct sense of episode, distinguish adolescents from adults, interpret the relation between a drug and an outcome, resolve negation, compare evidence across studies and avoid treating association as causation. The phrase without increasing liver toxicity also introduces a safety constraint.

Analysis

A purely keyword-based search may retrieve articles containing migraine, Drug A and liver, even when Drug A increases toxicity or is studied only in adults. Semantic role labelling can identify Drug A as the treatment and migraine episodes as the outcome. Temporal and comparative interpretation are needed to determine whether the drug reduces episodes relative to a control group. An ontology can normalize synonyms such as adolescent, teenager and age-range descriptors.

Approach

A robust pipeline combines:

• Contextual WSD for terms such as episode and toxicity.
• Entity linking for drugs, diseases and population groups.
• Event extraction for treatment, outcome and adverse-event relations.
• Negation and uncertainty detection.
• Coreference resolution across abstracts.
• Knowledge-graph storage with study design, population, dosage and publication date.
• NLI or rule-based comparison between the query and extracted claims.
• Evidence ranking based on study design and provenance rather than lexical similarity alone.

The system should distinguish “Drug A was associated with fewer episodes” from “Drug A caused fewer episodes.” It should also report when a study does not evaluate liver toxicity rather than infer safety from silence.

Outcome

The resulting assistant can return studies with structured explanations: population, intervention, comparator, outcome, adverse events, confidence and supporting passages. Human researchers remain responsible for clinical interpretation. The system is a retrieval and synthesis aid, not an autonomous medical decision-maker.

Learning Points

The case demonstrates that contextual understanding requires several layers simultaneously. Lexical semantics identifies relevant senses; compositional semantics interprets scope and negation; discourse analysis connects claims across sentences; ontologies normalize domain concepts; and provenance-aware knowledge graphs support auditable retrieval.

11. Evaluation, Limitations and Governance

Evaluation should be task-specific. WSD may use sense accuracy; semantic role labelling may use span and role F1; coreference may use mention-cluster metrics; NLI may use accuracy and calibration; knowledge extraction may use precision, recall and provenance quality. Exact-match scores alone are inadequate for open-ended generation.

Major limitations include:

• Annotation ambiguity: experts may disagree about senses, discourse relations or coreference.
• Domain shift: meanings change across law, medicine, finance and ordinary conversation.
• Bias: corpora may encode stereotypes and unequal representation.
• World-knowledge gaps: language models may miss physical, social or temporal constraints.
• Hallucination: fluent systems can produce unsupported relations or citations.
• Granularity mismatch: a coarse ontology may merge distinctions needed by users.
• Temporal instability: entities, policies and scientific knowledge change.

Best practices include documenting data sources, preserving provenance, evaluating subgroup performance, testing negation and counterfactuals, separating evidence from inference, exposing uncertainty, and requiring human review in high-impact settings. Privacy protection is essential when discourse data contain names, health information or private conversations. Governance should define who may access extracted facts, how corrections are made and how users can challenge an automated interpretation.

12. Contemporary Directions

Current research increasingly combines large language models with retrieval, structured decoding, ontologies and external tools. Retrieval-augmented systems can ground answers in documents, while graph-augmented models can represent entities and relations more explicitly. Neuro-symbolic methods attempt to combine flexible language representations with logical constraints.

Important research questions remain. How should a model represent uncertainty over word senses? Can discourse models track long-range reference reliably? How can systems distinguish learned correlation from entailment? How should pragmatic and culturally dependent interpretations be evaluated? What forms of explanation genuinely help users verify a claim?

Progress will depend not only on larger models but also on better benchmarks, richer annotations, multilingual resources, temporal knowledge representation and evaluation grounded in real professional workflows.

Chapter Summary

Lexical semantics explains how words acquire and relate to meanings through senses, reference, denotation and lexical relations. Word-sense disambiguation uses context, lexical resources and neural representations, but remains sensitive to domain and annotation choices. Semantic roles and predicate–argument structures identify participants in events beyond grammatical subject and object labels.

Compositional semantics explains how meanings combine and how scope, negation and quantification create alternative interpretations. Entailment, contradiction and neutrality support textual inference, while coreference and deixis connect expressions across discourse and context. Pragmatics adds speaker goals, implicatures, presuppositions and social meaning. Discourse relations provide coherence through cause, contrast, elaboration, evidence and temporal organization.

Ontologies and knowledge graphs connect linguistic expressions to structured concepts and entities. Effective NLP systems combine neural contextual modelling with symbolic structure, provenance and uncertainty. Reliable deployment requires careful evaluation, bias assessment, privacy protection, human oversight and explicit distinction between extracted evidence and inferred conclusions.

Key Terms

Lexical semantics: The study of meaning and relations among words and lexical units.

Sense: A particular interpretation of a word or expression.

Reference: The entity, event or situation identified by an expression in context.

Denotation: The set or domain of entities to which an expression applies.

Polysemy: Multiple related senses associated with one lexical form.

Homonymy: Identical forms with unrelated meanings.

Word-sense disambiguation: Selection of the intended sense of an ambiguous expression.

Distributional hypothesis: The view that contextual distribution provides evidence about meaning.

Semantic role: The function a participant performs in an event or state.

Predicate–argument structure: A predicate together with its semantic participants.

Compositionality: Dependence of complex meaning on parts and their combination.

Scope: The portion of an expression affected by an operator such as negation or a quantifier.

Entailment: A relation in which one statement follows from another.

Coreference: A relation in which expressions identify the same entity.

Deixis: Context-dependent reference involving parameters such as speaker, time or place.

Pragmatics: The study of meaning inferred from language use and context.

Discourse relation: A coherence relation connecting clauses or larger text spans.

Ontology: A structured account of concepts, properties and relations in a domain.

Knowledge graph: A graph of entities and typed relations, often represented as triples.

Provenance: Information about the source, time and reliability of a represented fact.

Review Questions

Short Answer Questions

1. Distinguish sense, reference and denotation with an example.
2. What is the difference between polysemy and homonymy?
3. Explain the role of semantic roles in event understanding.
4. Define textual entailment and contrast it with contradiction.
5. Why is provenance important in a knowledge graph?

Long Answer Questions

1. Explain major approaches to word-sense disambiguation and discuss their limitations.
2. Describe compositionality with particular reference to quantifier scope and negation.
3. Compare semantic role labelling, predicate–argument structure and discourse representation.
4. Explain how pragmatics, implicature and presupposition affect conversational NLP.
5. Discuss how ontologies and knowledge graphs can improve domain-specific question answering.

Analytical / Application Questions

1. Analyse the possible interpretations of “Every researcher reviewed a paper” and represent two readings using quantified notation.
2. Design a pipeline for resolving coreference and extracting treatment–outcome relations from clinical abstracts. Identify likely failure points.
3. A chatbot interprets “Can you send the report today?” only as a question about ability. Diagnose the semantic and pragmatic limitations of this behaviour and propose improvements.

Multiple Choice Questions with Answers

Question 1

Which relation holds between “sparrow” and “bird”?

A. Synonymy

B. Hyponymy

C. Antonymy

D. Meronymy

Correct Answer: B. Hyponymy

Question 2

What is the main purpose of word-sense disambiguation?

A. To detect sentence boundaries

B. To select the intended meaning of an ambiguous expression

C. To convert speech into text

D. To identify only grammatical subjects

Correct Answer: B. To select the intended meaning of an ambiguous expression

Question 3

In “Maya opened the door with a key,” what semantic role does “a key” most typically have?

A. Instrument

B. Experiencer

C. Recipient

D. Goal

Correct Answer: A. Instrument

Question 4

Which formula represents the reading that each analyst may have reviewed a different report?

A. ∃y (Report(y) ∧ ∀x (Analyst(x) → Reviewed(x, y)))

B. ∀x (Analyst(x) → ∃y (Report(y) ∧ Reviewed(x, y)))

C. ∀x (Report(x) → ∃y (Analyst(y) ∧ Reviewed(x, y)))

D. ∃x (Analyst(x) ∧ ∀y (Report(y) → Reviewed(x, y)))

Correct Answer: B. ∀x (Analyst(x) → ∃y (Report(y) ∧ Reviewed(x, y)))

Question 5

What does coreference resolution attempt to identify?

A. Words with identical spelling only

B. Expressions that refer to the same entity

C. All discourse connectives

D. The sentiment of a document

Correct Answer: B. Expressions that refer to the same entity

Question 6

Which statement is an example of an implicature that may be cancelled?

A. “Some reviewers approved” may suggest that not all approved

B. “The report exists” entails that it is a document

C. “The car moved” contains a verb

D. “The red file” contains an adjective

Correct Answer: A. “Some reviewers approved” may suggest that not all approved

Question 7

Which relation most directly connects “The server failed” with “The service became unavailable as a result”?

A. Cause or result

B. Synonymy

C. Part–whole

D. Deixis

Correct Answer: A. Cause or result

Question 8

What is a knowledge graph triple conventionally composed of?

A. Subject, predicate and object

B. Article, paragraph and sentence

C. Agent, patient and tense only

D. Sense, synonym and antonym

Correct Answer: A. Subject, predicate and object

Question 9

Why can lexical overlap be insufficient for natural language inference?

A. Overlap always prevents contradiction

B. Two texts may share words while expressing different or conflicting propositions

C. Inference never uses context

D. Lexical overlap is unrelated to language

Correct Answer: B. Two texts may share words while expressing different or conflicting propositions

Question 10

Which practice best supports trustworthy clinical NLP?

A. Treating absence of evidence as evidence of safety

B. Removing all source information from extracted facts

C. Preserving provenance and distinguishing evidence from inference

D. Using only the most frequent lexical interpretation

Correct Answer: C. Preserving provenance and distinguishing evidence from inference