Semantic Grammar as Whitebox Neural Network Architecture

 The code is here: https://github.com/BuckRogers1965/Physics-Unit-Coordinate-System/tree/main/semantics

Traditional Neural Network (Blackbox)

Input → [Hidden Layer 1] → [Hidden Layer 2] → [Hidden Layer N] → Output
         ↓ weights ↓      ↓ weights ↓       ↓ weights ↓
      [unknown repr]   [unknown repr]    [unknown repr]

Your Semantic Grammar (Whitebox)

Input: 'she' + 'walk' + ['tense_present'] + 'the dog'
  ↓
[Semantic Embedding Layer]
  verb_vector = [0.2, 0.0, 0.1, 0.5, 0.0, 0.0, 0.0, 0.0, 0.3, 0.6]
  dimensions: [action, tense, aspect, relation, modality, voice, polarity, mood, intensity, duration]
  ↓
[Transformation Layer]
  Apply axis_jacobians for each modifier:
  tense_present: [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
  result_vector = base_vector + modifications
  ↓
[Interpretation Layer] 
  Decode vector to grammatical features:
  tense = 'present' (from vector[1])
  aspect = 'simple' (from vector[2])
  voice = 'active' (from vector[5])
  polarity = 'affirmative' (from vector[6])
  ↓
[Assembly Layer]
  Apply conjugation rules + word order rules
  Result: "She walks the dog"

Key Differences

Traditional Neural Networks

• Learned representations: Weights discovered through backpropagation
• Opaque: Can't interpret what hidden layers represent
• Data-driven: Structure emerges from training examples
• Black box: Input → magic → output

Your Semantic System

• Designed representations: Each dimension has explicit meaning
• Transparent: Every transformation is interpretable
• Theory-driven: Structure based on linguistic/semantic theory
• White box: Every step can be inspected and understood

Architecture Components

1. Embedding Layer (Explicit Feature Engineering)

# Each verb has a 10D semantic vector with known dimensions
'walk': [action=0.2, tense=0.0, aspect=0.1, relation=0.5, 
         modality=0.0, voice=0.0, polarity=0.0, mood=0.0, 
         intensity=0.3, duration=0.6]

2. Transformation Layer (Interpretable Weight Updates)

# Each modifier is a known transformation
'tense_past': [0.0, -0.8, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
# This specifically moves tense dimension toward "past"

3. Decoding Layer (Rule-based Interpretation)

# Vector values map to linguistic categories
tense = 'past' if vec[1] < -0.3 else 'present' if abs(vec[1]) < 0.3 else 'future'

4. Output Layer (Grammatical Assembly)

# Deterministic rules for sentence construction
if auxiliaries:
    sentence_parts.append(" ".join(auxiliaries))
sentence_parts.append(main_verb_form)

Why This Matters

Interpretability

• Every dimension has semantic meaning
• Every transformation is linguistically motivated
• Every decision point is traceable
• Debugging is straightforward

Generalization

• New verbs just need semantic embeddings
• New modifiers just need axis transformations
• Rules generalize across the entire language

Efficiency

• No training required
• Deterministic output
• Fast inference
• Small memory footprint

Neural Network Parallel

Your system essentially implements what a neural network would learn, but explicitly:

1. Input Embedding: Traditional NNs learn word embeddings; you design semantic embeddings
2. Hidden Layers: Traditional NNs learn feature combinations; you define axis transformations
3. Output Layer: Traditional NNs learn classification; you implement grammatical rules

The Deeper Insight

This suggests that successful neural networks might be discovering something like your semantic dimensions in their hidden representations. The difference is:

• Neural networks: Discover structure through data
• Your system: Encode known structure directly

Your approach is essentially "what if we built the neural network that we want the data to teach us?"

This could be a powerful hybrid approach: use your whitebox architecture as a starting point, then let neural networks fine-tune the representations and transformations from data.

The work:

python semantictenses08.py 

=== PURE SEMANTIC GRAMMAR TRANSFORMER (Definitive Version) ===

 1. Input: she + walk + ['tense_present'] + the dog

    Output: She walks the dog

 2. Input: she + walk + ['tense_present', 'polarity_neg'] + the dog

    Output: She does not walk the dog

 3. Input: they + run + ['tense_present', 'adv_quickly'] + quickly

    Output: They run quickly quickly

 4. Input: he + eat + ['modal_must', 'tense_present'] + dinner

    Output: He must eat dinner

 5. Input: we + think + ['modal_should', 'tense_present'] + about it

    Output: We should think about it

 6. Input: I + know + ['modal_might', 'polarity_neg'] + the answer

    Output: I might not know the answer

 7. Input: she + dance + ['tense_present', 'aspect_progressive', 'adv_gracefully'] + ballet

    Output: She is dancing ballet gracefully

 8. Input: he + whisper + ['tense_past', 'adv_quietly'] + secrets

    Output: He whisperred secrets quietly

 9. Input: they + shout + ['tense_present'] + loudly

    Output: They shout loudly

10. Input: I + eat + ['adv_always', 'tense_present'] + breakfast

    Output: I always eat breakfast

11. Input: we + run + ['adv_never', 'tense_present'] + marathons

    Output: We never run marathons

12. Input: she + love + ['tense_present', 'adj_beautiful'] + her cat

    Output: She loves her beautiful cat

13. Input: he + make + ['tense_past', 'adj_huge'] + a cake

    Output: He made a huge cake

14. Input: they + drive + ['tense_present', 'adj_red'] + cars

    Output: They drive red cars

15. Input: she + run + ['tense_future', 'adv_very', 'adv_quickly'] + the race

    Output: She will run the race very quickly

16. Input: he + believe + ['modal_cannot'] + the story

    Output: He cannot believe the story

17. Input: we + create + ['tense_present', 'aspect_progressive', 'adj_beautiful'] + art

    Output: We are creating beautiful art

18. Input: I + understand + ['modal_should', 'adv_completely'] + the problem

    Output: I should completely understand the problem

19. Input: everyone + fear + ['tense_present', 'adv_sometimes'] + the dark

    Output: Everyone sometimes fears the dark

20. Input: nobody + enjoy + ['tense_present'] + waiting

    Output: Nobody does not enjoy waiting

21. Input: someone + doubt + ['modal_might', 'tense_present'] + the plan

    Output: Someone might doubt the plan

22. Input: she + walk + ['tense_present', 'aspect_progressive'] + to school

    Output: She is walking to school

23. Input: they + build + ['tense_present', 'aspect_progressive', 'adv_carefully'] + a house

    Output: They are building a house carefully

24. Input: he + eat + ['tense_past', 'adv_extremely', 'adv_quickly'] + the meal

    Output: He ate the meal extremely quickly

25. Input: she + say + ['modal_must', 'adv_sadly'] + goodbye

    Output: She must sadly say goodbye

(venv) jrogers@ryzen7mini:~/Documents/Programs/unifiedfieldtheory$ cat semantictenses08.py

import numpy as np

class SentenceAssembler:

    """

    Takes structured semantic data and assembles a grammatically correct English sentence.

    This internalizes all word order and conjugation logic.

    """

    def __init__(self, subject_data, verb_data, object_phrase, mods_data):

        self.s = subject_data

        self.v = verb_data

        self.o = object_phrase

        self.m = mods_data

    def assemble(self):

        sentence_parts = [self.s['word'].capitalize()]

        modal = self.m['modals'][0] if self.m['modals'] else None

        adverbs = self.m['adverbs']

        

        auxiliaries = []

        main_verb_form = self.v['root']

        # 1. Determine the auxiliary verb chain (e.g., "will have been")

        if modal:

            auxiliaries.append(modal)

        elif self.v['tense'] == 'future':

            auxiliaries.append('will')

        if self.v['aspect'] == 'progressive':

            if auxiliaries and auxiliaries[0] in ['will', 'must', 'should', 'might', 'can']:

                auxiliaries.append('be')

            elif not auxiliaries: # Simple progressive tenses

                if self.v['tense'] == 'present':

                    aux = 'am' if self.s['person'] == 1 and self.s['number'] == 1 else \

                          'is' if self.s['person'] == 3 and self.s['number'] == 1 else 'are'

                    auxiliaries.append(aux)

                elif self.v['tense'] == 'past':

                    auxiliaries.append('was' if self.s['number'] == 1 else 'were')

        # 2. Handle negation

        if self.v['polarity'] == 'negative' and 'never' not in adverbs:

            if auxiliaries:

                if auxiliaries[0] == 'can': # Form contraction 'cannot'

                    auxiliaries[0] = 'cannot'

                else:

                    auxiliaries.insert(1, 'not')

            else: # No other auxiliaries, must use 'do'-support

                if self.v['tense'] == 'present':

                    auxiliaries.append('does' if self.s['person'] == 3 and self.s['number'] == 1 else 'do')

                elif self.v['tense'] == 'past':

                    auxiliaries.append('did')

                auxiliaries.append('not')

        # 3. Determine the final form of the main verb

        if self.v['voice'] == 'passive':

            main_verb_form = self.v['form_ed']

            # Add 'be' if not already present for passive voice

            if 'be' not in auxiliaries and 'am' not in auxiliaries and 'is' not in auxiliaries and 'are' not in auxiliaries and 'was' not in auxiliaries and 'were' not in auxiliaries:

                # This is a simplification; full passive logic is very complex

                if self.v['tense'] == 'future': auxiliaries.append('be')

                elif self.v['tense'] == 'past': auxiliaries.append('was' if self.s['number'] == 1 else 'were')

        elif self.v['aspect'] == 'progressive':

            main_verb_form = self.v['form_ing']

        elif auxiliaries:

            main_verb_form = self.v['root'] # After any auxiliary, use the root form

        else: # Simple tenses (no auxiliaries)

            main_verb_form = self.v['form']

        # 4. Assemble the sentence with correct adverb placement

        if auxiliaries:

            sentence_parts.append(" ".join(auxiliaries))

        freq_adverbs = [adv for adv in adverbs if adv in ['always', 'never', 'sometimes', 'often', 'rarely', 'really', 'completely', 'sadly']]

        if freq_adverbs:

            sentence_parts.append(" ".join(freq_adverbs))

        

        sentence_parts.append(main_verb_form)

        if self.o:

            sentence_parts.append(self.o)

        manner_adverbs = [adv for adv in adverbs if adv not in freq_adverbs]

        # Crude handling of adverb-adverb pairs

        if 'very' in manner_adverbs and 'quickly' in manner_adverbs:

             manner_adverbs = [adv for adv in manner_adverbs if adv not in ['very', 'quickly']] + ['very quickly']

        if manner_adverbs:

            sentence_parts.append(" ".join(manner_adverbs))

        return " ".join(sentence_parts)

class ExtendedSemanticSVOTransformer:

    def __init__(self):

        # [action, tense, aspect, relation, modality, voice, polarity, mood, intensity, duration]

        self.verb_embeddings = {

            'walk': np.array([0.2, 0.0, 0.1, 0.5, 0.0, 0.0, 0.0, 0.0, 0.3, 0.6]),

            'run':  np.array([0.4, 0.0, 0.2, 0.6, 0.0, 0.0, 0.0, 0.0, 0.8, 0.4]),

            'eat':  np.array([0.1, 0.0, 0.3, 0.4, 0.0, 0.0, 0.0, 0.0, 0.4, 0.5]),

            'think': np.array([0.0, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.5, 0.8]),

            'dance': np.array([0.3, 0.0, 0.4, 0.6, 0.0, 0.0, 0.0, 0.0, 0.5, 0.7]),

            'whisper': np.array([0.05, 0.0, 0.0, 0.4, 0.0, 0.0, 0.0, 0.0, 0.1, 0.3]),

            'shout': np.array([0.3, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.9, 0.1]),

            'love': np.array([0.0, 0.0, 0.0, 0.9, 0.0, 0.0, 0.0, 0.0, 0.8, 0.9]),

            'make': np.array([0.3, 0.0, 0.0, 0.5, 0.0, 0.0, 0.0, 0.0, 0.5, 0.6]),

            'drive': np.array([0.3, 0.0, 0.1, 0.6, 0.0, 0.0, 0.0, 0.0, 0.7, 0.5]),

            'believe': np.array([0.0, 0.0, 0.0, 0.9, 0.3, 0.0, 0.0, 0.0, 0.6, 0.9]),

            'create': np.array([0.3, 0.0, 0.0, 0.7, 0.0, 0.0, 0.0, 0.0, 0.7, 0.7]),

            'understand': np.array([0.0, 0.0, 0.0, 0.85, 0.6, 0.0, 0.0, 0.0, 0.6, 0.8]),

            'fear': np.array([0.0, 0.0, 0.0, 0.7, 0.0, 0.0, 0.0, 0.0, 0.6, 0.8]),

            'enjoy': np.array([0.0, 0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.7, 0.6]),

            'doubt': np.array([0.0, 0.0, 0.0, 0.7, -0.5, 0.0, 0.0, 0.0, 0.4, 0.6]),

            'know': np.array([0.0, 0.0, 0.0, 0.95, 0.8, 0.0, 0.0, 0.0, 0.7, 0.95]),

            'say': np.array([0.1, 0.0, 0.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.3, 0.2]),

            'build': np.array([0.4, 0.0, 0.0, 0.6, 0.0, 0.0, 0.0, 0.0, 0.6, 0.8])

        }

        self.subjects = {

            'I': {'word': 'I', 'person': 1, 'number': 1}, 'you': {'word': 'you', 'person': 2, 'number': 1},

            'he': {'word': 'he', 'person': 3, 'number': 1}, 'she': {'word': 'she', 'person': 3, 'number': 1},

            'it': {'word': 'it', 'person': 3, 'number': 0}, 'we': {'word': 'we', 'person': 1, 'number': 2},

            'they': {'word': 'they', 'person': 3, 'number': 2}, 'everyone': {'word': 'everyone', 'person': 3, 'number': 1},

            'nobody': {'word': 'nobody', 'person': 3, 'number': 1, 'polarity': 'negative'},

            'someone': {'word': 'someone', 'person': 3, 'number': 1}

        }

        self.axis_jacobians = {

            'tense_past': np.array([0.0, -0.8, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),

            'tense_present': np.array([0.0, 0.0,  0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),

            'tense_future':    np.array([0.0,  0.9, -0.1, 0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),

            'aspect_progressive': np.array([0.0, 0.0, 0.8, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2]),

            'voice_passive':   np.array([0.0, 0.0, 0.0, -0.2, 0.0, 1.2, 0.0, 0.0, 0.0, 0.0]),

            'polarity_neg':    np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.2, 0.0, 0.0, 0.0]),

            'mood_subjunctive': np.array([0.0, 0.2, 0.1, 0.0, 0.0, 0.0, 0.0, 1.1, 0.0, 0.0]),

            'modal_must':      np.array([0.0, 0.0, 0.0, 0.0, 1.2, 0.0, 0.0, 0.0, 0.2, 0.0]),

            'modal_should':    np.array([0.0, 0.0, 0.0, 0.0, 0.9, 0.0, 0.0, 0.0, 0.0, 0.0]),

            'modal_might':     np.array([0.0, 0.0, 0.0, 0.0, 0.4, 0.0, 0.0, 0.0, -0.2, 0.0]),

            'modal_cannot':    np.array([0.0, 0.0, 0.0, 0.0, 0.8, 0.0, -1.2, 0.0, 0.0, 0.0]),

            'adv_quickly':     np.array([0.1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.3, -0.4]),

            'adv_quietly':     np.array([-0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -0.3, 0.0]),

            'adv_always':      np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8]),

            'adv_never':       np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, -1.0, 0.0, 0.0, 0.0]),

            'adv_gracefully':  np.array([0.0, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0, 0.0, 0.2, 0.0]),

            'adv_very':        np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.6, 0.0]),

            'adv_sadly':       np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]),

            'adv_completely':  np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.8, -0.5])

        }

        self.irregular_forms = {

            'run': {'past': 'ran', 'past_participle': 'run'},

            'eat': {'past': 'ate', 'past_participle': 'eaten'},

            'make': {'past': 'made', 'past_participle': 'made'},

            'think': {'past': 'thought', 'past_participle': 'thought'},

            'know': {'past': 'knew', 'past_participle': 'known'},

            'say': {'past': 'said', 'past_participle': 'said'},

            'drive': {'past': 'drove', 'past_participle': 'driven'},

            'be': {'present_1s': 'am', 'present_3s': 'is', 'present_plural': 'are', 'past_s': 'was', 'past_plural': 'were', 'past_participle': 'been'}

        }

    def _get_verb_form(self, verb, form_type, person=None, number=None):

        """Main internal conjugation engine."""

        # Check for irregular forms first

        if form_type in self.irregular_forms.get(verb, {}):

            return self.irregular_forms[verb][form_type]

        

        # Rule-based regular verb conjugation

        if form_type == 'past' or form_type == 'past_participle':

            if verb.endswith('e'): return verb + 'd'

            if verb.endswith('y') and len(verb) > 1 and verb[-2] not in 'aeiou': return verb[:-1] + 'ied'

            # Simple consonant doubling

            if len(verb) > 1 and verb[-2] in 'aeiou' and verb[-1] not in 'aeiouwx' : return verb + verb[-1] + 'ed'

            return verb + 'ed'

            

        if form_type == 'present_3s':

            if verb.endswith(('s', 'sh', 'ch', 'x', 'z')): return verb + 'es'

            if verb.endswith('y') and len(verb) > 1 and verb[-2] not in 'aeiou': return verb[:-1] + 'ies'

            return verb + 's'

            

        if form_type == 'ing':

            if verb.endswith('e') and verb not in ['be', 'see']: return verb[:-1] + 'ing'

            if verb.endswith('ie'): return verb[:-2] + 'ying'

            if len(verb) > 1 and verb[-2] in 'aeiou' and verb[-1] not in 'aeiouwx' : return verb + verb[-1] + 'ing'

            return verb + 'ing'

        

        return verb # Return root form by default

    def generate_svo(self, subj_word, verb_root, obj_phrase, mods):

        if subj_word not in self.subjects or verb_root not in self.verb_embeddings:

            return f"[Input error: {subj_word} or {verb_root} unknown]"

        vec = np.copy(self.verb_embeddings[verb_root])

        for mod in mods:

            if mod in self.axis_jacobians:

                vec += self.axis_jacobians[mod]

        

        subject_data = self.subjects[subj_word]

        person, number = subject_data['person'], subject_data['number']

        

        tense = 'past' if vec[1] < -0.3 else 'present' if abs(vec[1]) < 0.3 else 'future'

        aspect = 'progressive' if vec[2] > 0.4 else 'simple'

        voice = 'passive' if vec[5] > 0.6 else 'active'

        polarity = 'negative' if 'polarity' in subject_data or vec[6] < -0.6 else 'affirmative'

        simple_present_form = verb_root if person != 3 or number != 2 else self._get_verb_form(verb_root, 'present_3s', person, number)

        if person != 3 or number != 1: simple_present_form = verb_root

        else: simple_present_form = self._get_verb_form(verb_root, 'present_3s', person, number)

        

        verb_data = {

            'root': verb_root,

            'form': simple_present_form if tense == 'present' else self._get_verb_form(verb_root, 'past'),

            'form_ing': self._get_verb_form(verb_root, 'ing'),

            'form_ed': self._get_verb_form(verb_root, 'past_participle'),

            'tense': tense, 'aspect': aspect, 'voice': voice, 'polarity': polarity

        }

        adjectives = [m[4:] for m in mods if m.startswith('adj_')]

        full_object = obj_phrase

        if adjectives and obj_phrase:

            obj_parts = obj_phrase.split()

            determiners = ['a', 'an', 'the', 'her', 'his', 'my', 'their', 'our']

            if obj_parts[0].lower() in determiners:

                obj_parts.insert(1, " ".join(adjectives))

                full_object = " ".join(obj_parts)

            else:

                full_object = f"{' '.join(adjectives)} {obj_phrase}"

        mods_data = {

            'adverbs': [m[4:] for m in mods if m.startswith('adv_')],

            'modals': [m[6:] for m in mods if m.startswith('modal_') and m != 'modal_cannot'] + (['can'] if 'modal_cannot' in mods else [])

        }

        assembler = SentenceAssembler(subject_data, verb_data, full_object, mods_data)

        return assembler.assemble()

# === DEMO ===

if __name__ == "__main__":

    model = ExtendedSemanticSVOTransformer()

    print("=== PURE SEMANTIC GRAMMAR TRANSFORMER (Definitive Version) ===\n")

    examples = [

        ('she', 'walk', 'the dog', ['tense_present']),

        ('she', 'walk', 'the dog', ['tense_present', 'polarity_neg']),

        ('they', 'run', 'quickly', ['tense_present', 'adv_quickly']),

        ('he', 'eat', 'dinner', ['modal_must', 'tense_present']),

        ('we', 'think', 'about it', ['modal_should', 'tense_present']),

        ('I', 'know', 'the answer', ['modal_might', 'polarity_neg']),

        ('she', 'dance', 'ballet', ['tense_present', 'aspect_progressive', 'adv_gracefully']),

        ('he', 'whisper', 'secrets', ['tense_past', 'adv_quietly']),

        ('they', 'shout', 'loudly', ['tense_present']),

        ('I', 'eat', 'breakfast', ['adv_always', 'tense_present']),

        ('we', 'run', 'marathons', ['adv_never', 'tense_present']),

        ('she', 'love', 'her cat', ['tense_present', 'adj_beautiful']),

        ('he', 'make', 'a cake', ['tense_past', 'adj_huge']),

        ('they', 'drive', 'cars', ['tense_present', 'adj_red']),

        ('she', 'run', 'the race', ['tense_future', 'adv_very', 'adv_quickly']),

        ('he', 'believe', 'the story', ['modal_cannot']),

        ('we', 'create', 'art', ['tense_present', 'aspect_progressive', 'adj_beautiful']),

        ('I', 'understand', 'the problem', ['modal_should', 'adv_completely']),

        ('everyone', 'fear', 'the dark', ['tense_present', 'adv_sometimes']),

        ('nobody', 'enjoy', 'waiting', ['tense_present']),

        ('someone', 'doubt', 'the plan', ['modal_might', 'tense_present']),

        ('she', 'walk', 'to school', ['tense_present', 'aspect_progressive']),

        ('they', 'build', 'a house', ['tense_present', 'aspect_progressive', 'adv_carefully']),

        ('he', 'eat', 'the meal', ['tense_past', 'adv_extremely', 'adv_quickly']),

        ('she', 'say', 'goodbye', ['modal_must', 'adv_sadly'])

    ]

    for i, (subj, verb, obj, mods) in enumerate(examples, 1):

        print(f"{i:2d}. Input: {subj} + {verb} + {mods} + {obj}")

        result = model.generate_svo(subj, verb, obj, mods)

        print(f"    Output: {result}")

        if i < len(examples): print()