#3877 NLP for Highly Technical/Engineering Documents
#3877 NLP for Highly Technical/Engineering Documents
#3877 How to apply SpaCy for highly technical/engineering content
Applying spaCy to highly technical or engineering content requires a different strategy than general NLP. Standard models often fail on technical jargon, complex units of measurement, and dense, structured document formats (like specs or ISO standards).
This guide outlines a specialized pipeline ("The Engineering Stack") to handle these challenges.
1. The Architecture: "The Engineering Stack"
Do not rely on a single "out-of-the-box" model. Instead, build a modular pipeline:
| Component | Tool / Library | Purpose |
| 1. Ingestion | spacy-layout (wrapping Docling) | Extract text from PDFs while preserving tables, headers, and sections. |
| 2. Base Model | en_core_web_trf or scispacy | Transformer-based models provide better context for dense technical sentences than small models. |
| 3. Unit Parsing | quantulum3 (wrapper) | specialized library for detecting engineering units (e.g., "50 MPa", "100 ft-lbs"). |
| 4. Entity Ruler | spacy.EntityRuler | Hard-coded rules for fixed patterns (ISO standards, part numbers). |
| 5. Knowledge Base | spacy.EntityLinker + QUDT | Linking vague terms to specific engineering concepts (e.g., mapping "psi" to unit:PoundForcePerSquareInch). |
2. Step-by-Step Implementation
Phase 1: Ingestion (Handling PDFs)
Engineering content rarely comes in clean text files. Use spacy-layout to handle the complex layout of technical PDFs (multi-column, embedded tables).
!pip install spacy-layout doclingimport spacy
from spacy_layout import spaCyLayout
nlp = spacy.load("en_core_web_trf")
layout = spaCyLayout(nlp)
# Process a technical spec PDF
doc = layout("./spec_sheet_2024.pdf")
# Access structured data
print(doc._.tables) # Extracts tables as Pandas DataFrames
for span in doc.spans["layout"]:
if span.label_ == "section_header":
print(f"Section: {span.text}")
Phase 2: Customizing for Engineering Entities
Standard models won't catch "ASTM D638" or "3000 rpm". You need Rule-Based Matching.
A. Matcher for Standards (ISO/ASTM)
Create a pattern to catch standard citations.
from spacy.matcher import Matcher
matcher = Matcher(nlp.vocab)
# Pattern for ISO/ASTM standards (e.g., "ISO 9001", "ASTM D-638")
pattern_standard = [
{"TEXT": {"IN": ["ISO", "ASTM", "DIN", "IEEE"]}},
{"IS_PUNCT": True, "OP": "?"}, # Optional hyphen or space
{"TEXT": {"REGEX": "^[A-Z0-9]+(-[0-9]+)?$"}} # Captures alphanumeric codes like "9001" or "D-638"
]
matcher.add("TECH_STANDARD", [pattern_standard])
doc = nlp("The material must comply with ISO 9001:2015 and ASTM D638.")
matches = matcher(doc)
for match_id, start, end in matches:
print("Found Standard:", doc[start:end].text)
B. Quantities & Units (The quantulum3 approach)
Regex is brittle for units. Use a dedicated library like quantulum3 and wrap it as a spaCy component.
from quantulum3 import parser as q_parser
from spacy.language import Language
@Language.component("quantulum_component")
def quantulum_component(doc):
text = doc.text
quants = q_parser.parse(text)
spans = []
for q in quants:
# Map quantulum result back to spaCy character offsets
# Note: In production, you need robust alignment logic here
span = doc.char_span(q.span[0], q.span[1], label="MEASUREMENT")
if span:
spans.append(span)
# Add to doc.ents (handling overlaps safely)
doc.ents = list(doc.ents) + spans
return doc
nlp.add_pipe("quantulum_component", before="ner")
Phase 3: Domain-Specific Vocabulary (Materials)
For materials science (polymers, alloys), use EntityRuler with a word list from the Materials Project or similar databases.
# Create a materials entity ruler
ruler = nlp.add_pipe("entity_ruler", before="ner")
# Load patterns (you can scrape these from MatWeb or internal DBs)
patterns = [
{"label": "MATERIAL", "pattern": "Titanium Grade 5"},
{"label": "MATERIAL", "pattern": [{"LOWER": "stainless"}, {"LOWER": "steel"}, {"LOWER": "304"}]},
{"label": "MATERIAL", "pattern": "Polypropylene"}
]
ruler.add_patterns(patterns)
3. Advanced Strategy: Knowledge Graphs
Engineering data is highly relational (e.g., Material X has_property Yield Strength Y).
Use spacy-llm to extract relationships if you don't have labeled training data.3
# config.cfg snippet for spacy-llm
[components.llm]
factory = "llm"
task = "spacy.REL.v1"
save_io = false
[components.llm.task]
labels = ["HAS_STRENGTH", "COMPLIES_WITH", "MADE_OF"]
label_definitions = {
"HAS_STRENGTH": "The specific yield or tensile strength of a material",
"COMPLIES_WITH": "The regulatory standard the object adheres to"
}
This allows you to prompt an LLM (like GPT-4 or a local Llama) to "fill in" the relationships between the entities your spaCy pipeline detected.
4. Recommended Resources
Models:
en_core_sci_lg(from scispacy) is the best starting point if your engineering domain overlaps with chemistry/bio. Otherwise, stick toen_core_web_trf.Ontologies: Look at QUDT (Quantities, Units, Dimensions, Types) for a rigid hierarchy of engineering units to use in your Entity Linker.
Annotation: Use Prodigy (from the spaCy team) if you need to train a custom model.
4 It enables "human-in-the-loop" training, which is essential when defining subtle boundaries (e.g., distinguishing between "identifying a bolt" vs "specifying a bolt").
Next Step
Would you like me to generate a specific JSON pattern file for a particular engineering domain (e.g., Civil Engineering materials or Electrical Engineering components) that you can load directly into a spaCy EntityRuler?
I am processing technical/engineering documents sight-unseen. I do not know their content before processing.
Processing "sight-unseen" documents requires a Generalized Extraction Strategy. You cannot rely on a vocabulary list (e.g., a list of specific pump parts) because you don't know the domain yet.
Instead, you must rely on syntactic structure and universal engineering patterns (the "shapes" of data) rather than the data itself.
Here is a robust architectural approach for a Domain-Agnostic Engineering Pipeline.
1. The Strategy: "Structure Over Vocabulary"
When you don't know what the document is about, you look for how it is written. Engineering documents, regardless of whether they are Civil, Aerospace, or Software, share these universal traits:
IDs & Codes: Alphanumeric strings (Part numbers, Serial numbers).
References: Citations to external authority (ISO, IEEE, MIL-STD).
Constraints: Modal verbs indicating requirements ("shall", "must").
Quantities: Numbers followed by units.
2. The "Universal" Pipeline Implementation
We will build a pipeline that extracts these four universals without needing to know if the document is about rockets or toasters.
Step A: The "Shape" Detector (Regex Entity Ruler)
This is your primary tool. We define entities by their shape using spacy.EntityRuler.
import spacy
from spacy.language import Language
nlp = spacy.load("en_core_web_trf") # Use Transformer for accuracy
# 1. Create the Entity Ruler
ruler = nlp.add_pipe("entity_ruler", before="ner")
# 2. Define "Universal" Patterns
patterns = [
# --- Catch Standard References (ISO 9001, MIL-STD-882, IEEE 12207) ---
# Look for all-caps acronyms followed by numbers
{
"label": "TECH_STD",
"pattern": [
{"IS_UPPER": True, "LENGTH": {">=": 3}}, # e.g., "ISO"
{"IS_PUNCT": True, "OP": "?"}, # Optional hyphen
{"TEXT": {"REGEX": "^[A-Z0-9]+[-.]?[0-9]+$"}} # e.g., "9001" or "D-638"
]
},
# --- Catch Part Numbers / Serial IDs ---
# Look for complex alphanumeric strings mixed with hyphens
{
"label": "TECH_ID",
"pattern": [{"TEXT": {"REGEX": "^[A-Z0-9]{2,}-[A-Z0-9-]{3,}$"}}]
},
# --- Catch Requirements (Modal Verbs) ---
# This labels the specific verb, we will expand to sentence level later
{
"label": "REQ_MODAL",
"pattern": [{"LOWER": {"IN": ["shall", "must", "required", "mandatory"]}}]
}
]
ruler.add_patterns(patterns)
Step B: GLiNER (The "Zero-Shot" Extractor)
Since you don't know the domain, standard NER is weak. GLiNER (Generalist and Lightweight NER) is a modern breakthrough. It allows you to query for entities by definition without training.
Why use this? You can ask it to find "System Component" or "Hazard Condition," and it generalizes well to unseen text.
!pip install gliner-spacyfrom gliner_spacy.pipeline import GlinerSpacy
# Add GLiNER wrapper to spaCy
# labels are what we are looking for in the "unknown" text
nlp.add_pipe("gliner_spacy", config={
"gliner_model": "urchade/gliner_medium-v2.1",
"chunk_size": 250,
"labels": ["System Component", "Hazard", "Test Condition", "Software Module"],
"style": "ent"
})
text = "The Flux-Capacitor assembly shall sustain 1.21 GW without thermal runaway."
doc = nlp(text)
# GLiNER will likely catch "Flux-Capacitor assembly" as a System Component
# and "thermal runaway" as a Hazard.
for ent in doc.ents:
print(ent.text, ent.label_)
Step C: Noun Phrase Chunking (Topic Discovery)
If you are processing a document blind, you need to calculate "Term Frequency-Inverse Document Frequency" (TF-IDF) specifically on Noun Chunks. This tells you what the document is about.
def extract_technical_concepts(doc):
# Filter for complex noun phrases that are likely technical terms
concepts = []
for chunk in doc.noun_chunks:
# Logic: Ignore chunks that are just pronouns or simple stopwords
if len(chunk) > 1 and not chunk[0].is_stop:
# Clean punctuation
clean_text = chunk.text.strip().lower()
concepts.append(clean_text)
return concepts
# If "hydraulic actuator" appears 50 times, it's the subject of the document.
3. Context Classification (Is it a Requirement?)
In engineering, context is king. A sentence describing a bolt is different from a sentence requiring a bolt.
Use spaCy's dependency parser to detect Requirement Context.
def classify_sentence_type(sent):
# Check for requirement modals
has_modal = any(token.text.lower() in ["shall", "must"] for token in sent)
# Check for conditional logic (if/then) indicating logic/process
has_condition = any(token.dep_ == "mark" and token.text.lower() == "if" for token in sent)
if has_modal:
return "REQUIREMENT"
elif has_condition:
return "LOGIC/CONDITION"
else:
return "DESCRIPTION"
for sent in doc.sents:
category = classify_sentence_type(sent)
# Now you can store this sentence in a DB tagged by its type
4. Summary of the "Blind" Stack
| Stage | Tool | Goal in "Blind" Context |
| 1. Taxonomy | GLiNER | Zero-shot detection of "Components" vs "Hazards" without training. |
| 2. IDs | Regex EntityRuler | Extracting part numbers, ISO standards, and serials based on shape. |
| 3. Subject | Noun Chunking | Analyzing word frequency to auto-generate a "Title" or "Topic" for the doc. |
| 4. Intent | Dependency Matcher | determining if a sentence is a Requirement vs. a Statement of Fact. |
5. Handling "Dirty" Inputs
Since you are sight-unseen, you will encounter "dirty" text (OCR errors, weird spacing).
Preprocessing Tip: Before feeding text to spaCy, normalize the whitespace. PDF extraction often adds line breaks in the middle of sentences.
Code:
text = " ".join(text.split())(Simple but effective for re-flowing text).
Next Step
Would you like a Python script that implements the GLiNER + Regex hybrid approach to process a sample text and output a structured JSON summary of the "Unknown" document?
From <https://gemini.google.com/app/> Google Gemini 3.0 Pro (Thinking)
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