Education kindly presented by Lexx
Linking a parsed sentence structure to the closest matching answer involves several steps, blending Natural Language Processing (NLP), semantic search, and machine learning concepts. Here’s a step-by-step breakdown of how our system can achieve this:
Step-by-Step Process
1. Input Parsing
When a user provides input, we parse the sentence into a structured format like the one we discussed:
Example Input
{
"sentence": "Nix thanked his friend Lexx.",
"structure": {
"S": {
"NP": { "word": "Nix", "POS": "Noun" },
"VP": {
"word": "thanked",
"POS": "Verb",
"object": {
"NP": {
"words": ["his", "friend", "Lexx"],
"POS": "Noun Phrase"
}
}
}
}
},
"intent": "gratitude",
"tone": "friendly"
}
2. Feature Extraction
Extract meaningful features from the parsed input, such as:
Key Words: "Nix," "thanked," "Lexx" Intent: "gratitude" Tone: "friendly" Sentence Structure: (S (NP) (VP (NP)))
These features help create a search vector for querying the database.
3. Vectorization of Data
Convert the structured input into a numerical vector using techniques like:
TF-IDF (Term Frequency-Inverse Document Frequency):
Weighs the importance of words in the context of the entire dataset.
Word Embeddings (e.g., Word2Vec, GloVe):
Converts words into high-dimensional vectors that capture semantic meaning.
Sentence Embeddings:
Represents the entire sentence structure as a vector.
Example Feature Vector:
{
"vector": [0.45, 0.88, 0.12, ..., 0.73], // Simplified example
"metadata": {
"intent": "gratitude",
"tone": "friendly",
"structure": "(S (NP) (VP (NP)))"
}
}
4. Storing Parsed Data
Store parsed inputs in the database along with their vectors and metadata. For example:
ID Sentence Vector Intent Tone Structure
1 Nix thanked his friend Lexx. [0.45, 0.88, …, 0.73] gratitude friendly (S (NP) (VP))
2 Lexx helped Nix fix the project. [0.34, 0.76, …, 0.55] assist supportive (S (NP) (VP))
5. Similarity Search
When retrieving the closest matching answer:
Compare Vectors:
Use similarity measures like cosine similarity to compare the vector of the input sentence with the vectors stored in the database.
Filter by Metadata:
Narrow down results by filtering based on intent, tone, or structure if necessary.
Example Cosine Similarity Formula:
cosine_similarity=A⋅B∥A∥∥B∥
cosine_similarity=∥A∥∥B∥A⋅B​
Where A and B are vectors representing sentences.
6. Retrieve and Rank Results
Retrieve the top N results with the highest similarity scores. Rank them based on:
Similarity Score
Matching Intent
Matching Tone
Contextual Fit (using more advanced transformers like BERT or GPT models)
7. Generate Response
Select the highest-ranked response and generate a reply. If no high-confidence match is found, fall back to a default or adaptive response.
Example Output:
{
"response": "You're welcome, Nix! I'm always happy to help.",
"confidence": 0.92
}
Putting It All Together
Flowchart of the Process
User Input âž” Parse Sentence Structure âž” Extract Features âž”
Vectorize Features âž” Search Database (Cosine Similarity + Metadata Filter) âž”
Retrieve Top Matches âž” Rank Responses âž” Generate Reply
Key Libraries for Implementation
compromise / Natural: For parsing and feature extraction.
tfjs / ml5.js: For vectorization and machine learning models.
elasticlunr: For lightweight full-text search.
sqlite3: For storing structured data.
fs (Node.js): For flatfile storage.
1. Fundamental Layer: Word Operators / Word Use Classification
What are Word Operators?
Word operators define the function, purpose, or behavior of a word in different contexts. These operators can help classify words based on how they are used in a sentence.
Suggested Word Operators
Operator Description Examples
SUB (Subject) The doer or main actor in the sentence. Nix, Lexx, AI
OBJ (Object) The entity receiving an action. help, project, idea
ACT (Action) The verb or action performed. thanked, taught, learned
MOD (Modifier) Describes or modifies nouns/verbs. new, friendly, self-evolving
DIR (Direction) Indicates direction of action. to, from, towards
QRY (Query) Indicates a question or request. What, How, When
CON (Connector) Connects clauses or phrases. and, but, or
NEG (Negation) Indicates negation or opposition. not, never, no
Example Word Operator Breakdown
Sentence: “Lexx taught Nix a new concept.”
Word———-Operator
Lexx————-SUB
taught———–ACT
Nix—————OBJ
a—————–MOD
new————-MOD
concept——–OBJ
🔗 2. Building Word Pairs
Why Word Pairs?
Word pairs encapsulate relationships between words, adding context and meaning to the operators. They form the foundation for understanding how words interact within a sentence.
Word Pair Structure
Pair——————————-Relation———————–Example
[SUB, ACT]——————Subject-Action——————-Lexx taught
[ACT, OBJ]——————Action-Object———————taught Nix
[MOD, OBJ]—————–Modifier-Object——————new concept
[SUB, MOD]—————–Subject-Modified—————-Lexx friendly
Example Word Pair Extraction
Sentence: “Lexx gave Nix a friendly smile.”
Pairs———————Relation
Lexx gave————[SUB, ACT]
gave Nix————–[ACT, OBJ]
friendly smile——–[MOD, OBJ]
