HybridSimilarity Algorithm
A Deep Dive into the HybridSimilarity Algorithm
This article explores the HybridSimilarity algorithm, a sophisticated neural network designed to assess the similarity between text pairs. This hybrid model cleverly integrates lexical, phonetic, semantic, and syntactic comparisons for a comprehensive similarity score.
import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.decomposition import TruncatedSVD from sentence_transformers import SentenceTransformer from Levenshtein import ratio as levenshtein_ratio from phonetics import metaphone import torch import torch.nn as nn class HybridSimilarity(nn.Module): def __init__(self): super().__init__() self.bert = SentenceTransformer('all-MiniLM-L6-v2') self.tfidf = TfidfVectorizer() self.attention = nn.MultiheadAttention(embed_dim=384, num_heads=4) self.fc = nn.Sequential( nn.Linear(1152, 256), nn.ReLU(), nn.LayerNorm(256), nn.Linear(256, 1), nn.Sigmoid() ) def _extract_features(self, text1, text2): # Feature Extraction features = {} # Lexical Analysis features['levenshtein'] = levenshtein_ratio(text1, text2) features['jaccard'] = len(set(text1.split()) & set(text2.split())) / len(set(text1.split()) | set(text2.split())) # Phonetic Analysis features['metaphone'] = 1.0 if metaphone(text1) == metaphone(text2) else 0.0 # Semantic Analysis (BERT) emb1 = self.bert.encode(text1, convert_to_tensor=True) emb2 = self.bert.encode(text2, convert_to_tensor=True) features['semantic_cosine'] = nn.CosineSimilarity()(emb1, emb2).item() # Syntactic Analysis (LSA-TFIDF) tfidf_matrix = self.tfidf.fit_transform([text1, text2]) svd = TruncatedSVD(n_components=1) lsa = svd.fit_transform(tfidf_matrix) features['lsa_cosine'] = np.dot(lsa[0], lsa[1].T)[0][0] # Attention Mechanism att_output, _ = self.attention( emb1.unsqueeze(0).unsqueeze(0), emb2.unsqueeze(0).unsqueeze(0), emb2.unsqueeze(0).unsqueeze(0) ) features['attention_score'] = att_output.mean().item() return torch.tensor(list(features.values())).unsqueeze(0) def forward(self, text1, text2): features = self._extract_features(text1, text2) return self.fc(features).item() def similarity_coefficient(text1, text2): model = HybridSimilarity() return model(text1, text2)
Core Components
The HybridSimilarity model relies on these key components:
- Sentence Transformers: Utilizes pre-trained transformer models for semantic embedding generation.
- Levenshtein Distance: Calculates lexical similarity based on character-level edits.
- Metaphone: Determines phonetic similarity.
- TF-IDF and Truncated SVD: Applies Latent Semantic Analysis (LSA) for syntactic similarity.
- PyTorch: Provides the framework for building the custom neural network with attention mechanisms and fully connected layers.
Detailed Breakdown
1. Model Setup
The HybridSimilarity
class, extending nn.Module
, initializes:
- A BERT-based sentence embedding model (
all-MiniLM-L6-v2
). - A TF-IDF vectorizer.
- A multi-head attention mechanism.
- A fully connected network to aggregate features and generate the final similarity score.
self.bert = SentenceTransformer('all-MiniLM-L6-v2') self.tfidf = TfidfVectorizer() self.attention = nn.MultiheadAttention(embed_dim=384, num_heads=4) self.fc = nn.Sequential( nn.Linear(1152, 256), nn.ReLU(), nn.LayerNorm(256), nn.Linear(256, 1), nn.Sigmoid() )
2. Feature Extraction
The _extract_features
method computes several similarity features:
- Lexical Similarity:
- Levenshtein ratio: Quantifies the number of edits (insertions, deletions, substitutions) to transform one text into another.
- Jaccard index: Measures the overlap of unique words in both texts.
features['levenshtein'] = levenshtein_ratio(text1, text2) features['jaccard'] = len(set(text1.split()) & set(text2.split())) / len(set(text1.split()) | set(text2.split()))
- Phonetic Similarity:
- Metaphone encoding: Compares phonetic representations.
features['metaphone'] = 1.0 if metaphone(text1) == metaphone(text2) else 0.0
- Semantic Similarity:
- BERT embeddings are generated, and cosine similarity is calculated.
emb1 = self.bert.encode(text1, convert_to_tensor=True) emb2 = self.bert.encode(text2, convert_to_tensor=True) features['semantic_cosine'] = nn.CosineSimilarity()(emb1, emb2).item()
- Syntactic Similarity:
- TF-IDF vectorizes the text, and LSA is applied using
TruncatedSVD
.
- TF-IDF vectorizes the text, and LSA is applied using
tfidf_matrix = self.tfidf.fit_transform([text1, text2]) svd = TruncatedSVD(n_components=1) lsa = svd.fit_transform(tfidf_matrix) features['lsa_cosine'] = np.dot(lsa[0], lsa[1].T)[0][0]
- Attention-based Feature:
- Multi-head attention processes the embeddings, and the average attention score is used.
att_output, _ = self.attention( emb1.unsqueeze(0).unsqueeze(0), emb2.unsqueeze(0).unsqueeze(0), emb2.unsqueeze(0).unsqueeze(0) ) features['attention_score'] = att_output.mean().item()
3. Neural Network Fusion
The extracted features are combined and fed into a fully connected neural network. This network outputs a similarity score (0-1).
import numpy as np from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.decomposition import TruncatedSVD from sentence_transformers import SentenceTransformer from Levenshtein import ratio as levenshtein_ratio from phonetics import metaphone import torch import torch.nn as nn class HybridSimilarity(nn.Module): def __init__(self): super().__init__() self.bert = SentenceTransformer('all-MiniLM-L6-v2') self.tfidf = TfidfVectorizer() self.attention = nn.MultiheadAttention(embed_dim=384, num_heads=4) self.fc = nn.Sequential( nn.Linear(1152, 256), nn.ReLU(), nn.LayerNorm(256), nn.Linear(256, 1), nn.Sigmoid() ) def _extract_features(self, text1, text2): # Feature Extraction features = {} # Lexical Analysis features['levenshtein'] = levenshtein_ratio(text1, text2) features['jaccard'] = len(set(text1.split()) & set(text2.split())) / len(set(text1.split()) | set(text2.split())) # Phonetic Analysis features['metaphone'] = 1.0 if metaphone(text1) == metaphone(text2) else 0.0 # Semantic Analysis (BERT) emb1 = self.bert.encode(text1, convert_to_tensor=True) emb2 = self.bert.encode(text2, convert_to_tensor=True) features['semantic_cosine'] = nn.CosineSimilarity()(emb1, emb2).item() # Syntactic Analysis (LSA-TFIDF) tfidf_matrix = self.tfidf.fit_transform([text1, text2]) svd = TruncatedSVD(n_components=1) lsa = svd.fit_transform(tfidf_matrix) features['lsa_cosine'] = np.dot(lsa[0], lsa[1].T)[0][0] # Attention Mechanism att_output, _ = self.attention( emb1.unsqueeze(0).unsqueeze(0), emb2.unsqueeze(0).unsqueeze(0), emb2.unsqueeze(0).unsqueeze(0) ) features['attention_score'] = att_output.mean().item() return torch.tensor(list(features.values())).unsqueeze(0) def forward(self, text1, text2): features = self._extract_features(text1, text2) return self.fc(features).item() def similarity_coefficient(text1, text2): model = HybridSimilarity() return model(text1, text2)
Practical Application
The similarity_coefficient
function initializes the model and computes the similarity between two input texts.
self.bert = SentenceTransformer('all-MiniLM-L6-v2') self.tfidf = TfidfVectorizer() self.attention = nn.MultiheadAttention(embed_dim=384, num_heads=4) self.fc = nn.Sequential( nn.Linear(1152, 256), nn.ReLU(), nn.LayerNorm(256), nn.Linear(256, 1), nn.Sigmoid() )
This returns a float between 0 and 1, representing the similarity.
Conclusion
The HybridSimilarity algorithm offers a robust approach to text similarity by integrating various aspects of text comparison. Its combination of lexical, phonetic, semantic, and syntactic analysis allows for a more comprehensive and nuanced understanding of text similarity, making it suitable for various applications, including duplicate detection, text clustering, and information retrieval.
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