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.

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.

The above is the detailed content of HybridSimilarity Algorithm. For more information, please follow other related articles on the PHP Chinese website!