Enhancing Query Retrieval Precision Through Optimized Embedding Text Selection

Alex Wang
Nov 16, 2024

This paper explores strategies to optimize embedding texts for semantic search, focusing on the impact of normalization, synonyms, alternate phrasing, and typo handling. Using text-embedding-3-large with ChromaDB, we analyze test results to provide recommendations for creating embeddings that enhance retrieval accuracy. Key findings demonstrate the importance of text standardization, incorporation of contextual synonyms, and query preprocessing while cautioning against embedding literal typos. These conclusions are supported by detailed test results, illustrating how semantic models handle variations in user input.

Introduction

Semantic search systems use vector-based representations to match user queries to relevant information based on intent and meaning rather than exact keyword matches. Embedding models such as text-embedding-3-large generate multi-dimensional vectors where similar meanings are represented as closely related points in the vector space. For instance, "breakfast time" and "when is breakfast served" produce vectors that align closely despite differences in phrasing. However, the effectiveness of semantic search depends heavily on how embedding texts are constructed. Factors such as normalization, inclusion of synonyms, and handling of typos can influence retrieval accuracy. This study investigates these factors using a series of tests, providing evidence-based recommendations for embedding text creation.

Methodology

To conduct this study, an embedding model and a vector database were utilized to enable semantic search and evaluate query retrieval precision. Embedding models convert textual data into high-dimensional vector representations, capturing semantic meaning in a format that allows for effective comparison and matching (Mikolov et al., 2013). A vector database is essential for storing these embeddings and performing similarity searches efficiently, enabling the alignment of user queries with relevant embedding texts based on their vector representations (Chhabra, 2023). For this study, the text-embedding-3-large model, which generates 3072-dimensional embeddings, was selected. This model is among the most advanced in capturing nuanced semantic relationships, making it particularly suitable for tasks requiring high precision and contextual understanding (Open AI Embeddings). To manage and query these embeddings, we utilized ChromaDB, a robust vector database designed for efficient storage and retrieval of high-dimensional data (ChromaDB). ChromaDB was chosen for its reliability and support for cosine similarity (Wikipedia)—a standard metric for evaluating semantic similarity—ensuring consistent and accurate results. These tools allowed us to rigorously test and analyze various embedding text selection strategies. This study was designed to evaluate how different text selection strategies influence semantic similarity and query retrieval performance. Each test case explores a distinct aspect of embedding text optimization, examining its impact on accuracy and consistency across a variety of user query scenarios. The four areas were chosen for their direct relevance to improving semantic search retrieval accuracy in practical settings. Each represents a distinct aspect of user query variability: - Text Normalization ensures consistent formatting. - Synonyms address natural variability in word choice. - Typos account for errors in user input. - Natural Conversational Language matches real-world phrasing.

The Four Areas Explored

1. Text Normalization Description: - This test examined the impact of lowercasing all text and removing unnecessary punctuation from embedding texts and queries. - The goal was to determine whether normalization improves retrieval consistency by reducing variations caused by formatting inconsistencies. Reason for Inclusion: - Real-World Relevance: Users often enter queries with mixed casing or punctuation errors. - Literature Reference: Normalization is a well-documented preprocessing technique in natural language processing (NLP) to enhance model performance. Expected Outcome: - Normalized text should reduce the semantic variability introduced by capitalization and punctuation. Other Possibilities: - Exploring more advanced normalization techniques, such as lemmatization or stemming, was not included due to the semantic nature of embeddings, where word context is preserved better than in keyword-based methods. These could be subjects for future studies. 2. Synonyms and Alternate Phrasing Description: - Embedding texts were expanded to include synonyms and alternate phrasings for common queries, e.g., "breakfast time" and "what time is breakfast." Reason for Inclusion: - User Query Variability: Users often use synonyms or rephrase their queries in natural language. - Literature Reference: The semantic capabilities of embeddings rely on capturing related meanings (Mikolov et al., 2013). Expected Outcome: - Embeddings with synonyms and alternate phrasings should reduce distance scores for varied queries, improving retrieval accuracy. Other Possibilities: - Contextual embeddings trained on specific domains could improve synonym recognition. This study used pre-trained embeddings, which may lack domain-specific context. Future studies could evaluate fine-tuned embeddings. 3. Common Typos Description: - Tests were conducted to evaluate how well the model handled common user typos, e.g., "breakfat time" instead of "breakfast time." Reason for Inclusion: - Prevalence of Errors: Typographical errors are common in user queries, particularly in mobile typing environments. - Robustness Testing: Assessing whether embeddings inherently tolerate such errors is critical for system reliability. Expected Outcome: - Embeddings should match closely to the correct text without explicitly including typos, leveraging semantic similarity to handle minor variations. Other Possibilities: - Explicitly embedding typos was excluded due to concerns over database size and redundancy. Query preprocessing, such as typo correction using spelling correction libraries (SymSpell), could complement embeddings and will be explored in future work. 4 . Concise, Conversational Language Description: - Embedding texts were written in conversational, natural language to mimic real-world query phrasing, e.g., "how do I get WiFi?" instead of technical or verbose descriptions. Reason for Inclusion: - Real-World Usability: Users often phrase queries naturally, expecting responses in a conversational tone. - Literature Reference: Conversational AI systems emphasize natural language for user-friendliness (Radford et al., 2018). Expected Outcome: - Embedding texts written in conversational language should align closely with user queries, improving retrieval precision. Other Possibilities: - Including formal or technical phrasings for specialized domains (e.g., medical terminology) could improve retrieval in professional settings. This study prioritized everyday conversational queries to focus on hospitality use cases. This methodology leverages state-of-the-art embedding models and focuses on practical, real-world variability in user input. By addressing key challenges through normalization, synonyms, typos, and conversational language phrasing, the study aims to provide actionable insights for improving semantic search precision in hospitality applications. Future work will expand on contextual and domain-specific adaptations to further enhance the robustness of semantic retrieval systems. Results and Analysis

Results and Analysis

1. Text Normalization

Objective: To determine if text normalization (e.g., converting to lowercase, removing punctuation) improves retrieval accuracy by reducing variability caused by formatting differences.

Test Results: 1. Query: "Wi-Fi" - Distance Scores: - 0.0 for 'Wi-Fi' - 0.134 for 'wi-fi' - 0.240 for 'WiFi' - 0.312 for 'wifi' 2. Query: "breakfast hours" - Distance Scores: - 0.0 for 'breakfast hours' - 0.052 for 'Breakfast Hours' 3. Query: breakfast time? - Distance Scores: - 0.08813, {'embedding_text': 'breakfast time'} - 0.18912, {'embedding_text': 'what time is breakfast'} - 0.20969, {'embedding_text': 'breakfast serving time'} - 0.24073, {'embedding_text': 'what are breakfast times'} 4. Query: breakfast time - Distance Scores: - 0.0, {'embedding_text': 'breakfast time'} - 0.15923, {'embedding_text': 'breakfast serving time'} - 0.17118, {'embedding_text': 'what time is breakfast'} - 0.18837, {'embedding_text': 'breakfast hours'} - 0.20495, {'embedding_text': 'what are breakfast times'}

2. Synonyms and Alternate Phrasing

Objective: To evaluate the impact of including synonyms and alternate phrasings in embedding texts...

3. Typo Handling

Objective: To assess whether literal typo embeddings are necessary...

4. Concise, Conversational Language

Objective: To evaluate the effectiveness of conversational language phrasing in embeddings...

Conclusion and Recommendations

This study demonstrates that optimizing embedding text selection through normalization, synonyms, typo preprocessing, and natural language phrasing significantly enhances semantic search performance...

Future Work

Future work should explore advanced areas, such as multilingual query handling, domain-specific fine-tuning, and dynamic embedding strategies...

References