7 Advanced Feature Engineering Tricks for Text Data Using LLM Embeddings

7 Advanced Feature Engineering Tricks for Text Data Using LLM Embeddings

This article outlines seven advanced strategies to enrich text data for machine learning models by leveraging LLM-generated embeddings (e.g., from Sentence Transformers). These techniques combine semantic and lexical features to improve performance in tasks like classification, clustering, and similarity detection.

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1. Combining TF-IDF and Embedding Features

• Purpose: Merge lexical (TF-IDF) and semantic (LLM) features to capture both word frequency and contextual meaning.
• Implementation:
  • Use TfidfVectorizer to extract TF-IDF features.
  • Generate embeddings using a pre-trained model (e.g., all-MiniLM-L6-v2).
  • Concatenate and scale features before training a classifier (e.g., logistic regression).
• Impact: Boosts model accuracy by combining lexical and semantic signals.
• Example Code:

  from sklearn.datasets import fetch_20newsgroups
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sentence_transformers import SentenceTransformer
  import numpy as np
  model = SentenceTransformer("all-MiniLM-L6-v2")
  data = fetch_20newsgroups(subset='train', categories=['sci.space', 'rec.autos'])
  texts, y = data.data[:500], data.target[:500]
  tfidf = TfidfVectorizer(max_features=300).fit_transform(texts).toarray()
  emb = model.encode(texts)
  X = np.hstack([tfidf, StandardScaler().fit_transform(emb)])
  clf = LogisticRegression(max_iter=1000).fit(X, y)
  print("Accuracy:", clf.score(X, y))

• Best Practices: Use StandardScaler on embeddings to normalize their range.

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2. Topic-Aware Embedding Clusters

• Purpose: Create compact topic meta-features by clustering embeddings.
• Implementation:
  • Use K-Means to cluster embeddings into topics.
  • Encode cluster labels with OneHotEncoder and concatenate with original embeddings.
• Impact: Adds interpretability by grouping similar texts into topics.
• Example Code:

  from sklearn.cluster import KMeans
  from sklearn.preprocessing import OneHotEncoder
  texts = ["Tokyo Tower is a popular landmark.", "Sushi is a traditional Japanese dish."]
  emb = model.encode(texts)
  topics = KMeans(n_clusters=2).fit_predict(emb)
  topic_ohe = OneHotEncoder().fit_transform(topics.reshape(-1, 1))
  X = np.hstack([emb, topic_ohe])
  print(X.shape)

• Pitfalls: Poorly chosen n_clusters may lead to overfitting or loss of semantic meaning.

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3. Semantic Anchor Similarity Features

• Purpose: Measure similarity between text and predefined “anchor” sentences.
• Implementation:
  • Encode anchor sentences and compute cosine similarity with text embeddings.
• Impact: Helps models learn relationships between text and key concepts.
• Example Code:

  from sklearn.metrics.pairwise import cosine_similarity
  anchors = ["space mission", "car performance"]
  anchor_emb = model.encode(anchors)
  texts = ["The rocket launch was successful.", "The car handled well on the track."]
  emb = model.encode(texts)
  sim_features = cosine_similarity(emb, anchor_emb)
  print(sim_features)

• Use Case: Useful for classification tasks with predefined categories (e.g., sentiment labels).

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4. Meta-Feature Stacking via Auxiliary Classifier

• Purpose: Use an auxiliary classifier to generate meta-features from embeddings.
• Implementation:
  • Train a classifier (e.g., logistic regression) on embeddings.
  • Use its predicted probabilities as a meta-feature.
• Impact: Augments embeddings with discriminative signals for downstream tasks.
• Example Code:

  from sklearn.linear_model import LogisticRegression
  X_train, X_test, y_train, y_test = train_test_split(emb, y, test_size=0.5)
  meta_clf = LogisticRegression(max_iter=1000).fit(X_train, y_train)
  meta_feature = meta_clf.predict_proba(emb)[:, 1].reshape(-1, 1)
  X_aug = np.hstack([StandardScaler().fit_transform(emb), meta_feature])
  print("Augmented shape:", X_aug.shape)

• Recommendations: Ensure the auxiliary model is trained on a separate dataset to avoid overfitting.

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5. Embedding Compression and Nonlinear Expansion

• Purpose: Reduce dimensionality (via PCA) and expand features nonlinearly (via polynomial features).
• Implementation:
  • Apply PCA to compress embeddings.
  • Use PolynomialFeatures to create interactions between compressed dimensions.
• Impact: Captures nonlinear patterns while maintaining efficiency.
• Example Code:

  from sklearn.decomposition import PCA
  from sklearn.preprocessing import PolynomialFeatures
  pca = PCA(n_components=2).fit_transform(emb)
  poly = PolynomialFeatures(degree=2).fit_transform(pca)
  print("After polynomial expansion:", poly.shape)

• Pitfalls: High-degree polynomials may overfit; use cross-validation to tune parameters.

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6. Relational Learning with Pairwise Contrastive Features

• Purpose: Highlight similarity/dissimilarity between text pairs.
• Implementation:
  • Compute absolute difference and element-wise product of embeddings for paired texts.
• Impact: Effective for tasks requiring pairwise comparisons (e.g., semantic similarity).
• Example Code:

  pairs = [("The car is fast.", "The vehicle moves quickly.")]
  emb1 = model.encode([p[0] for p in pairs])
  emb2 = model.encode([p[1] for p in pairs])
  X_pairs = np.hstack([np.abs(emb1 - emb2), emb1 * emb2])
  print("Pairwise feature shape:", X_pairs.shape)

• Best Practices: Use large datasets to avoid bias in pairwise comparisons.

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7. Cross-Modal Fusion

• Purpose: Combine LLM embeddings with handcrafted linguistic features (e.g., punctuation ratio).
• Implementation:
  • Calculate features like word count and punctuation ratio.
  • Concatenate with embeddings.
• Impact: Adds domain-specific signals to semantic representations.
• Example Code:

  import re
  punct_ratio = np.array([len(re.findall(r"[^\w\s]", t)) / len(t) for t in texts]).reshape(-1, 1)
  X = np.hstack([emb, lengths, punct_ratio])
  print("Final feature matrix shape:", X.shape)

• Use Case: Useful for tasks requiring both semantic and syntactic analysis (e.g., sentiment analysis).

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Working Example (Code-Related)

from sentence_transformers import SentenceTransformer
from sklearn.decomposition import PCA
from sklearn.preprocessing import PolynomialFeatures
import numpy as np

# Load model and text data
model = SentenceTransformer("all-MiniLM-L6-v2")
texts = ["Mars mission 2024!", "New electric car model launched."]
emb = model.encode(texts)

# Compress embeddings and expand nonlinearly
pca = PCA(n_components=2).fit_transform(emb)
poly = PolynomialFeatures(degree=2, include_bias=False).fit_transform(pca)
print("After polynomial expansion:", poly.shape)

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Recommendations (Code-Related)

• When to Use: Apply these techniques when raw embeddings alone are insufficient for downstream tasks (e.g., low accuracy in classification).
• Best Practices:
  • Always scale embeddings before combining with other features.
  • Use cross-validation to tune hyperparameters (e.g., PCA components, polynomial degree).
  • Avoid overfitting by using separate validation sets for auxiliary models.
• Pitfalls:
  • Over-reliance on handcrafted features may limit model generalization.
  • High-dimensional feature spaces can increase computational costs.