Comparative Study of Cosine Similarity-based and Neural Network-based Chatbots

Abstract

This white paper explores two distinct approaches to chatbot development: (1) cosine similarity-based retrieval chatbots and (2) neural network-based intent classification chatbots. Both paradigms offer complementary advantages. Cosine similarity excels at text similarity matching with lightweight infrastructure, while neural networks provide greater flexibility by learning user intent patterns. We review underlying principles, system architecture, data pipelines, evaluation strategies, and practical insights derived from prototype implementations.

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1. Introduction

Chatbots are increasingly vital in customer support, information retrieval, and task automation. Approaches to chatbot design can be broadly categorized as retrieval-based or generation-based. This study focuses on retrieval-based (cosine similarity) and classification-based (MLP neural network) methods, with an emphasis on their applicability to domain-specific deployments.

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2. Background

2.1 Cosine Similarity in NLP

• Cosine similarity measures the angle between two vectors, ignoring magnitude. Values range from -1 to 1, with 1 indicating identical orientation.
• In textual applications, vectors are derived from TF-IDF or embeddings, enabling semantic matching.
• Advantage: robust to document length variation.

2.2 Neural Networks for Intent Classification

• Inspired by biological neurons; composed of input, hidden, and output layers.
• Learn decision boundaries through forward propagation and backpropagation.
• Intent classification maps user utterances (X) to predefined intents (Y), e.g., greeting, busy, bye.
• Generalization supported by dropout, normalization, and appropriate weight initialization.

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3. Methods

3.1 Cosine Similarity Pipeline

1. Preprocessing: lowercasing, tokenization, lemmatization, punctuation removal.
2. Vectorization: TF-IDF transformation.
3. Similarity computation: cosine similarity to knowledge base.
4. Retrieval: select top-k most similar sentences.
5. Rule-based augmentation: greetings, time queries, etc.

3.2 Neural Network Pipeline

1. Data preparation: labeled corpus of utterances and intents.
2. Representation: Bag-of-Words encoding, one-hot intent labels.
3. Model: Keras Sequential (Dense-64 → Softmax output).
4. Training: SGD optimizer with momentum and Nesterov acceleration.
5. Evaluation: classification accuracy, confusion matrix.

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4. Evaluation and Validation

4.1 Metrics

• Cosine Similarity Chatbot: precision@k, response relevance, response latency.
• Neural Network Chatbot: accuracy, F1 score, confusion matrix.

4.2 Pilot Evaluation (Proposed)

• Dataset: small-scale conversational corpus (e.g., 500–1000 labeled queries).
• Setup: implement both pipelines under same preprocessing standards.
• Expected outcomes:
  • Cosine similarity: stronger for FAQ-style queries.
  • Neural network: stronger for intent recognition and generalization.

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5. Key Findings

• Cosine Similarity: Simple, interpretable, effective for FAQ retrieval, but limited to known expressions.
• Neural Networks: More flexible, learn hidden patterns, extendable with embeddings (Word2Vec, BERT), but data-hungry.
• Shared Insight: Data quality and preprocessing critically influence both approaches.
• Educational Value: Implementing both clarified that AI systems are grounded in mathematics (linear algebra, probability, calculus).

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6. Practical Considerations

• Data requirements: cosine similarity can function with small corpora; neural networks require larger, labeled datasets.
• Computation: cosine similarity is lightweight; neural networks require GPU acceleration for large-scale training.
• Deployment: hybrid models (retrieval + intent classification) can balance performance and resource efficiency.

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7. Conclusion

Both cosine similarity-based and neural network-based chatbots serve valuable roles in conversational AI. Cosine similarity provides fast and transparent retrieval, while neural networks enable nuanced intent understanding. Future work includes integrating embeddings (Word2Vec, BERT) and exploring hybrid retrieval-classification pipelines for more robust performance.

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8. References (Suggested)

• Jurafsky, D. & Martin, J.H. Speech and Language Processing.
• Mikolov et al. (2013). Word2Vec.
• Hochreiter & Schmidhuber (1997). LSTM.
• Keras Documentation: https://keras.io
• Scikit-learn Documentation: https://scikit-learn.org