Verb Edge

Verb Edge is the Edge type in a GEUL stream that represents predication and actions. It classifies 13,767 WordNet verbs into 10 Primitives and 68 Sub-primitives, then generates a 16-bit codebook via Sub-primitive-level Huffman coding.

Sub-documents

Document	Description
Semantic Role	16 Semantic Roles (4-bit encoding)
Qualifier	14 qualifiers including evidentiality, mood, tense, aspect

Verb Hierarchy

10 Primitive (top-level semantic categories)
 ├── BE          ├── PERCEIVE    ├── FEEL
 ├── THINK       ├── CHANGE      ├── CAUSE
 ├── MOVE        ├── COMMUNICATE ├── TRANSFER
 └── SOCIAL
  → 68 Sub-primitive (intermediate classification)
    → 559 Root Verb (root verbs)
      → 13,767 Leaf Verb (all WordNet verbs)

Primitives (major categories) serve only as conceptual groupings with no bit allocation
68 Sub-primitives receive frequency-based variable-length codes
Higher-frequency verb groups get shorter codes (4 to 8 bits)

Verb Edge Packet Types

All three packet types – Tiny, Short, and Full – share the same 16-bit verb body in their last word.

	Tiny	Short	Full
Words	2 (32bit)	3 (48bit)	5 (80bit)
Participants	16 patterns	512 patterns	19-bit flags
Qualifiers	7 patterns	3,640 patterns	27 bits
Verb body	16bit	16bit	16bit
Expected ratio	90%	7%	3%

Average packet size: 0.9x2 + 0.07x3 + 0.03x5 = 2.16 words

Tiny Verb Edge (2 words)

1st WORD:  [Prefix 5bit] [Target×Pattern 11bit]
2nd WORD:  [Verb body 16bit]

Target x Pattern: 18 Target x 113 patterns = 2,034 combinations
16 participant patterns x 7 qualifier patterns = 112 + 1 reserved = 113
Coverage ~90%

Short Verb Edge (3 words)

1st WORD:  [Prefix 6bit] [Type 1bit=0] [ParticipantPattern 9bit]
2nd WORD:  [Target×QualifierPattern 16bit]
3rd WORD:  [Verb body 16bit]

Full Verb Edge (5 words)

1st WORD:  [Prefix 6bit] [Type 1bit=1] [TargetParticipant 5bit] [ParticipantFlags 4bit]
2nd+3rd:   [ParticipantFlags 15bit] [Qualifier 17bit]
4th WORD:  [Qualifier 10bit] [Reserved 6bit]
5th WORD:  [Verb body 16bit]

16-bit Verb Body

┌─────────────────────────┬────────────────────────────┐
│   sub_primitive code    │     DFS index within tree  │
│   (4-8 bits, Huffman)   │     (8-12 bits)            │
└─────────────────────────┴────────────────────────────┘

sub_primitive code: 4-8 bits variable (Huffman code)
DFS index: Identifies individual verbs within the Sub-primitive

Code Length Distribution

Code length	Count	Total verbs	Ratio
4 bits	4	6,388	46.4%
5 bits	4	2,479	18.0%
6 bits	8	2,321	16.9%
7 bits	16	1,786	13.0%
8 bits	36	813	5.9%

DFS Index Bit Calculation

Sub-primitive verb count	Bits needed
1~256	8 bits
257~512	9 bits
513~1024	10 bits
1025~2048	11 bits
2049~4096	12 bits

Example: CHANGE-TRANSFORM = 0000 (4 bits) + 3,063 verbs (12 bits) = 16 bits.

Average Code Length

Average = Sum(code_length x verb_count) / total_verbs ≈ 5.14 bits

Method	Average bits
Fixed 7-bit (68 entries)	7.00
Huffman coding	5.14
Savings	1.86 bits (27%)

Primitive Major Categories (10)

Primitive	Meaning	Sub-primitive count	Verb count
BE	State/existence	8	899
PERCEIVE	Perception/cognition	4	218
FEEL	Emotion	6	204
THINK	Thought	6	769
CHANGE	Change	8	3,358
CAUSE	Causation/action	14	3,739
MOVE	Movement	6	2,182
COMMUNICATE	Communication	6	586
TRANSFER	Transfer	4	530
SOCIAL	Social action	6	387

Highest-frequency Sub-primitives (4-bit codes)

Sub-primitive	Code	Verb count	Ratio	Examples
CHANGE-TRANSFORM	`0000`	3,063	22.2%	“change”, “become”
CAUSE-USE	`0001`	1,358	9.9%	“use”, “employ”
MOVE-DISPLACE	`0010`	1,025	7.4%	“move”, “shift”
MOVE-GO	`0011`	942	6.8%	“go”, “travel”

The top 4 Sub-primitives account for 46.4% of all verbs.

Design Philosophy

Why Huffman Coding

CHANGE-TRANSFORM (22.2%) is overwhelmingly high-frequency
27% reduction in average bit count compared to fixed-bit allocation
Top 4 Sub-primitives account for 46.4% of the total

Why Remove Primitive Bits

Before: Primitive 3 bits + Sub_primitive 4 bits = 7 bits fixed
After: Sub_primitive direct coding = 4-8 bits variable
Up to 4-bit savings for high-frequency verbs

Maintaining Semantic Grouping

Primitive classification is retained for human readability and as a semantic clustering hint during LLM training.

Semantic Role

Defines 16 Participants representing semantic roles within events. Encodes core roles like Agent, Theme, and Recipient through to adjunct roles like Cause and Purpose using 4-bit encoding.

Qualifier

Semantic qualifiers for Verb Edge. Encodes grammatical and pragmatic information of predications across 14 categories including evidentiality, mood, modality, tense, aspect, politeness, polarity, and confidence.