Deciphering Text: The Quest for Contextual Relevance with NLP, LLMs, and AI Language APIs

From sophisticated AI techniques that delve deep into semantic understanding, to the vast array of Python libraries like NLTK and spaCy that offer a toolbox of natural language processing capabilities. Then there's SQL, not traditionally associated with text analysis, yet surprisingly effective for structured textual data querying. Regular expressions (regex) provide a more granular, pattern-based approach to text manipulation, invaluable for tasks requiring precise text extraction and cleaning. The advent of Large Language Models (LLMs) like GPT and BERT has further expanded the horizon, offering unparalleled insights through advanced contextual analysis. With such a diverse toolkit, the challenge isn't just about mastering these technologies but discerning which is the most appropriate for the task at hand, a decision that hinges on the specific needs, scale, and context of the project.

The challenge of comprehending and categorizing textual information is a critical endeavor for many projects. My recent project involved delving into a body of text with the aim to:

• Extract and identify keywords: Analyzing the text to pinpoint key terms that are essential for understanding and tagging the content.

• Categorize keywords: Dividing the identified keywords into two distinct categories:

• Existing Keywords: Keywords that align with pre-defined thesauri chosen by the user at the project's onset.

• Suggested Keywords: Proposing new, contextually relevant keywords that could be added to enhance the thesauri.

The Use Case:

Imagine a scenario where a user inputs text and selects Thesaurus 1 and 2. The goal is to return keywords from the text that match those in the selected thesauri and to recommend additional, pertinent keywords for incorporation into these thesauri, thereby augmenting their value and relevance.

The Many Attempts:

To tackle this complex task, I explored three different technological approaches, each utilizing Python but differing in their underlying methods: Natural Language Processing (NLP), Large Language Models (LLMs), and AI Language Model-based keyword extraction.

Attempt 1: Natural Language Processing (NLP)

The journey began with traditional NLP techniques, structured as follows:

1. Keyword Identification: Extract "important" words from the text.

2. Synonym Discovery: Find synonyms for the extracted keywords.

3. List Compilation: Organize these words into a comprehensive list.

4. Thesaurus Comparison: Compare this list against the selected thesauri to find matches.

5. Result Generation: Return the matched keywords.

Outcome: The NLP attempt led to cluttered results and required significant manual intervention to refine the keyword lists, often losing the original text's context in the process.

Attempt 2: Large Language Models (LLM)

The second approach harnessed the capabilities of Large Language Models:

1. Prompt Construction: Build a prompt to pass the text to an LLM, requesting a list of contextually relevant keywords.

2. Keyword and Thesaurus Integration: Combine the LLM-generated keywords with the thesauri keywords in a new prompt.

3. Thesaurus Matching: Request the LLM to identify keywords within the thesauri that closely match the returned keywords and return these matches.

Outcome: This method's primary challenge was crafting precise prompts and tuning model parameters. Despite these hurdles, the LLM approach proved promising, adeptly capturing the broader themes and topics of the text.

Attempt 3: AI Language Model – Keyword Extraction

The final attempt was the most straightforward, employing an AI Language Model for keyword extraction:

1. Keyword Extraction: Pass the text to the API to receive keywords.

2. Match Identification: Compare the returned keywords with those in the thesauri to identify matches.

Outcome: This method was the most efficient and performant. However, it lacked the ability to grasp the text's context, focusing solely on exact keyword matches.

The Winning Approach

In the pursuit of contextual relevance, the LLM stood out as the clear winner, demonstrating a profound ability to analyze and comprehend the text's larger narratives and themes. This approach facilitated the identification of broad, contextually relevant keywords that aligned well with the thesauri. The primary challenge with LLMs, however, is the variability in results when using public APIs versus privately trained models.

While the AI Language Model excelled in precise keyword extraction, its limited understanding of context curtailed its usefulness beyond exact matches. Conversely, the NLP approach, despite its potential, struggled with maintaining the context and required extensive manual intervention.

The choice between NLP, LLMs, and AI Language APIs depends on the project's specific needs, especially the importance of contextual understanding. Through this exploration, the LLM emerged as a powerful tool for deriving meaningful insights from text, setting a new standard in the field.