如何为Text Analytics API补充领域特定词汇以识别专业关键词？

Hey there, let's break down how you can get Azure Cognitive Services Text Analytics to recognize those domain-specific chemistry terms like intramolecular, ionic, covalent, and intermolecular. Here's what you can do:

Text Analytics has a built-in feature specifically for adding custom domain terms: Custom Entity Recognition. This lets you train a model to identify your specific keywords as distinct entities (like "Chemical Bond Type" in your case). Here's the workflow:

• Label your training data: Use the Azure Text Analytics Labeling Tool to mark instances of your target terms (e.g., highlight "Intramolecular" in sample sentences and assign it to a "ChemicalBondType" entity).
• Train the custom model: Upload your labeled dataset to the Azure portal and train a CER model. This teaches the API to recognize your domain terms across new texts.
• Call the API with your custom model: When sending text analysis requests, include your custom model's ID in the request parameters. The API will then return both default entities and your custom domain terms.

If you're just working with keyword extraction (not entity classification), you can complement the API's default results with your own domain vocabulary. Here's a practical approach using Python:
First, define your domain-specific term list, then extract matches from the text and merge them with the API's output:

import azure.ai.textanalytics as ta
import re

# Initialize Text Analytics client (replace with your credentials)
client = ta.TextAnalyticsClient(
    endpoint="YOUR_AZURE_ENDPOINT",
    credential="YOUR_AZURE_KEY"
)

# Your target text
input_text = "Which type of bonds are involved when matter changes state between solid, liquid, and gas? Intramolecular, ionic, covalent, intermolecular."

# Get default keyword results from Text Analytics
response = client.extract_key_phrases(documents=[input_text])[0]
default_keywords = response.key_phrases

# Define your custom domain vocabulary
domain_vocab = {"Intramolecular", "ionic", "covalent", "intermolecular"}

# Find all domain terms present in the text (case-insensitive match)
matched_terms = [term for term in domain_vocab if re.search(rf"\b{re.escape(term)}\b", input_text, re.IGNORECASE)]

# Merge default keywords with custom domain terms (remove duplicates)
final_keywords = list(set(default_keywords + matched_terms))

print("Final Keywords:", final_keywords)

This script will combine the API's default extracted phrases with your custom chemistry terms, ensuring none of your critical keywords are missed.

• Handle term variations: If your domain has term variants (e.g., "intermolecular" vs. "intermolecular bonds"), add both to your vocabulary list or use regex patterns to capture variations.
• Normalize text: Convert text to lowercase before matching to avoid missing terms due to capitalization differences.
• Prioritize CER for complex domains: If you need to categorize terms (e.g., distinguish bond types from other chemical terms), Custom Entity Recognition is more robust than a simple vocabulary match, as it assigns context and entity labels.

内容的提问来源于stack exchange，提问作者Rick JN