Chapter 5 Polysemy Burden Assessment

The polysemy burden identified in the SDA Design Standard (2019) serialised figures corpus is documented here as part of the evaluation in Chapter 5. Polysemy, the capacity of a single term to carry multiple distinct meanings, constitutes a structural hazard for automated standards interpretation and compliance verification. Three distinct forms of polysemy are reported. WordNet lexical polysemy concerns general-language sense counts. Dimensional polysemy occurs where the same concept carries different quantitative values across SDA categories. Categorical absence polysemy occurs where entities are specified in some categories but absent from others. The analysis draws on 56 unique canonical entities extracted from 189 figure-based design requirement triples.

WordNet Lexical Polysemy

Lexical polysemy is measured at the granularity of the canonical term inventory underlying the corpus analysis (1,236 terms). Each of the 56 canonical entities is resolved to the syntactic head of its concept phrase; degree markers (“minimum”, “maximum”), quantity qualifiers (“of 2200 mm”), and trailing prepositional attachments are stripped before lookup, so that a left modifier or a qualifier token can never supply the sense count for a compound. Under this head-anchored measurement, 26 of the 56 entities have their full name or cleaned head term present in the inventory: 13 are polysemous and 13 are monosemous. The remaining 30 entities carry head terms that fall below the inventory’s frequency threshold; these are reported as not measured rather than imputed, consistent with the dual-reporting norm adopted throughout Chapter 5. The polysemy burden among the measured entities is not uniformly distributed; a small number of entities carry disproportionately high sense counts.

Table A5-PB.1: Polysemous entities in the serialised vocabulary (all 13, head-anchored measurement)

Source: Entity Registry v2 (head-anchored polysemy records), sorted by WordNet sense count descending; foundational primitive per the term-to-foundational-mapping deposited with the data package (see Data availability above).

The entity Tap is the clearest illustration of the polysemy problem. WordNet records 20 senses spanning the physical fixture (“water_faucet.n.01”), the action of striking (“rap.n.02”), wire-tapping (“wiretap.n.01”), and tap dancing (“tapdance.v.01”). In the SDA context, only the plumbing fixture sense is relevant, but an automated system without domain-specific disambiguation would need to evaluate all 20 candidates. The compound Tap_And_Water_Source illustrates the corrected method’s behaviour: its head term resolves to “water source”, which the inventory records as monosemous, so the compound no longer inherits the 20-sense burden of its left conjunct; that inheritance was an artefact of the defective fallback.

The entity Door presents a different polysemy profile: only 5 WordNet senses, but 28 occurrences across the corpus with 20 distinct surface variants. Referential scope is the primary burden for Door: it variously denotes the physical barrier, the doorway opening, the circulation space, and the attached hardware. The serialisation schema resolves all 20 surface forms to a single canonical entity. Underlying referential polysemy remains a challenge for finer-grained semantic analysis. Overall, the head-anchored analysis confirms that domain-specific sense disambiguation is required for the 13 measured-polysemous entities, half of the measurable vocabulary, while the 30 below-threshold entities require inventory extension before any sense-level claim can be made about them. Dimensional polysemy is examined in the next section: the same entity there requires different quantitative values depending on the applicable SDA design category.

Dimensional Polysemy

Dimensional polysemy occurs when the same conceptual entity requires different quantitative values depending on the SDA design category. Five cases of dimensional polysemy are identified in the figure-based corpus.

Table A5-PB.2: Dimensional polysemy cases by entity and SDA category

Source: Figures-Channel Polysemy Analysis, dimensional-polysemy records (see Data availability above). IL = Improved Liveability; Robust; FA = Fully Accessible; HPS = High Physical Support.

These 5 cases are structurally significant because they represent requirements where the same entity in the same spatial context carries materially different dimensional specifications. Graduated dimensional polysemy is illustrated by the entity Door. Improved Liveability and Robust categories require 820 mm minimum clear opening; Fully Accessible requires 900 mm; High Physical Support requires 950 mm. The 130 mm range is substantive: it determines whether a wheelchair passes with one-sided or two-sided clearance. A compliance system must resolve the applicable category before evaluating the dimensional requirement. Failure to do so produces systematic over-specification or under-specification errors.

A structural asymmetry is also confirmed by the dimensional polysemy cases. Improved Liveability and Robust share identical dimensional values; so do Fully Accessible and High Physical Support. This pairing pattern is confirmed independently by the deontic force distribution in Appendix: Chapter 5 Predicate Coverage and Deontic Force. It suggests the four-category SDA classification effectively operates as a two-tier system at the quantitative level. In summary, the five cases require that any compliance system resolve category context before evaluating dimensional conformance. Categorical absence polysemy is examined next: entities there appear in some categories but are entirely absent from others.

Categorical Absence Polysemy

Categorical absence polysemy occurs when an entity is specified in some SDA categories but absent from others. The analysis identifies 36 such cases. Thirty-four of 36 absence cases follow a single pattern: entities appear in Fully Accessible and High Physical Support but are absent from Improved Liveability and Robust.

Three bathroom entities are absent from lower-tier categories: WC_Pan, Hand_Wash_Basin, and Shower. Spatial clearance entities include Knee_And_Toe_Clearance_Zone and Encroachment-Free_Zone. Kitchen specifications (Cooktop, Kitchen_Bench, Pantry, and Drawer-Style_Dishwasher) are absent from lower-tier categories. Bedroom specifications (Bed, Bedroom) are likewise absent from Improved Liveability and Robust requirements. These absences reflect the SDA framework’s graduated approach to physical support prescription.

Washing_Machine displays the inverse pattern: present in Improved Liveability and Robust, absent from Fully Accessible and High Physical Support. Higher-support categories prescribe alternative laundry configurations in its place. Minimum_900_Mm_Wide_Continuous_Accessible_Path is exclusive to High Physical Support, absent from all three other categories.

Conditional polysemy is recorded in one additional case. WC_Pan in the context of “DIM +/- DIM from the rear wall” carries identical values (800 mm +/- 10 mm) for both FA/HPS and an unspecified category. Polysemy is present but produces no divergent requirements in this degenerate case. Overall, the 36 absence cases define each SDA category’s regulatory reach. Automated systems must represent absence explicitly rather than treating it as a missing value.

Aggregate Polysemy Burden and Implications

Forty-two polysemy cases are identified across all three forms: 5 dimensional, 1 conditional, and 36 categorical absence. The affected corpus contains 189 design requirement triples. A polysemy incidence rate of 22.2% results: approximately one in five requirements is subject to context-dependent semantic variation.

Domain-specific sense disambiguation is required for the 13 lexically polysemous entities identified by the head-anchored measurement before WordNet-based or embedding-based analysis can be applied reliably; a further 30 entities are unmeasured at term granularity and must not be treated as either burdened or clear. The serialisation schema addresses this through canonical entity resolution, but downstream systems must recognise that general-purpose NLP tools will systematically over-count semantic complexity.

Category context resolution is required by the 5 dimensional polysemy cases before any automated compliance check evaluates dimensional conformance. The 36 categorical absence cases define each category’s regulatory boundaries. Automated systems must distinguish “this entity is not required” from “this entity was not found.” The serialisation schema records which categories each entity’s requirements apply to, making the distinction explicit. Taken together, the three polysemy forms confirm that polysemy is a structural feature of the SDA standard. The schema must represent it explicitly. This polysemy burden evidence supports the artefact design decisions in Chapter 5.