A Brief History of Text Retrieval Methods

Let's take a step back and trace the evolution of text retrieval methods, from the early days of simple string matching to the latest in semantic search.

String Matching

We kick things off with string matching, seemingly a straightforward task as illustrated in the code example above. Yet, the devil is in the details. Matching "Apple" to "apple" requires a text normalization step, converting all text to a standard form, often lowercase. Data cleaning becomes critical, stripping away punctuation or other non-essential characters. Word stemming condenses words to their root form, enabling "running" and "runner" to both match with "run."

Boolean Retrieval & TF-IDF

To counter the rigidity of simple string matching, Boolean retrieval entered the picture. For instance, if a user wanted to find articles about the fruit, not the tech giant, they could use a query like "apple AND fruit OR orchard NOT company." This Boolean logic allows for more nuanced searches, combining (AND), expanding (OR), and excluding (NOT) terms to hone in on the desired information.

TF-IDF, standing for Term Frequency-Inverse Document Frequency, enhances this logical framework by adding a layer of statistical analysis. It assesses the importance of a word within a specific document in relation to a corpus of documents. The Term Frequency (TF) component calculates how often a word appears in a document, indicating its relevance to that document. Conversely, the Inverse Document Frequency (IDF) reduces the significance of words that appear frequently across the corpus, thereby elevating the importance of rarer terms. For example, in a collection of technology articles, a commonly used word like "computer" would have a lower IDF score due to its prevalence. In contrast, a less frequent term like "quantum computing" would be assigned a higher IDF score, signifying its specific importance in the documents where it appears.

But the core issue persisted: these methods were good at matching words but clueless about matching meanings. They were still missing the point by ignoring the intricacies of synonyms, antonyms, and the semantic nuances that make human language rich but elusive.

Latent Semantic Analysis

The lack of semantic understanding gave rise to Latent Semantic Analysis (LSA). LSA used mathematical techniques to cluster documents and terms that are likely to be related based on their co-occurrence. It was a promising stride toward capturing the hidden relationships between words. However, its computational intensity made it impractical for larger datasets, and it still fell short of fully grasping the meaning of text.

Semantic Search

Semantic search marks a significant advancement in how we interact with and retrieve information from large text databases. Unlike traditional text retrieval methods, semantic search operates on the principle of understanding the 'meaning' behind the words, not just matching the words themselves.

This is achieved by converting text into a numerical format known as an embedding, which represents words or phrases in a high-dimensional vector space. In this so-called embedding space, the distance and direction between embeddings correspond to semantic relationships. These embeddings allow for more nuanced and context-aware searches, as they capture the underlying semantics of the text, rather than relying solely on the presence or absence of specific words.

Word Embeddings: The Foundation of Semantic Search

Word embeddings laid the groundwork for semantic search by providing a way to represent individual words as vectors in a high-dimensional space. This approach began with models like:

• word2vec: Developed by Google in 2013, word2vec was groundbreaking for its ability to capture semantic and syntactic word relationships through large-scale neural network training.
• GloVe (Global Vectors for Word Representation): Stanford's contribution to word embeddings in 2014, GloVe, focused on aggregating global word-word co-occurrence statistics from a corpus and then using these statistics to project words into the vector space.
• FastText: In 2015, an extension of word2vec, FastText by Facebook AI Research (FAIR), improved upon previous models by considering subword information, which enhanced the model's ability to understand morphologically rich languages and better handle rare words.

These early models transformed the landscape of text retrieval by providing a way to compare word meanings based on their vector representations. However, they had limitations, particularly in understanding words in context. For example, the word "apple" could refer to the fruit or the tech giant, and traditional word embeddings have no way to differentiate between the two based on the context in which the word appears.

Contextual Embeddings: The Cutting Edge of Semantic Search

Building on the foundation of word embeddings, the field then advanced to contextual embeddings, which marked a significant leap forward. Contextual embedding models understand the meaning of a word in relation to the words around it, granting models the ability to embed entire sentences and paragraphs into an embedding space. This evolution began with the introduction of the Transformer model by Google in 2017, setting the stage for a new era in language understanding.

The subsequent arrival of models like BERT, ELMo, GPT-2, and T5 between 2018 and 2019 represented a pivotal shift. These models, leveraging the Transformer's architecture, demonstrated an unprecedented ability to capture the nuances of language contextually. They moved beyond individual word analysis, considering the entire sentence or larger text segments to generate rich, context-aware embeddings.

This capability was further enhanced in the following years with the advent of advanced models like OpenAI's GPT series, Google's PaLM, and Meta's LLaMA. These models have pushed the boundaries of what's possible in contextual understanding, offering more accurate embeddings for texts of varying lengths, from a single sentence to comprehensive documents.

By leveraging these state-of-the-art contextual embedding models, we can now perform semantic searches that were previously unimaginable. They understand text in a way that closely mirrors human comprehension, recognizing nuances, emotions, and complex relationships within the text.

This course focuses on these cutting-edge techniques, particularly exploring how to utilize and scale contextual embeddings efficiently. As we progress, we'll delve into practical applications and implementations, demonstrating how far we've come from basic string matching to a world where machines can understand and retrieve information with a level of sophistication that rivals human cognition.