PreProcessing of Content for a RAG application
As we discussed in our last post we are going to build a chat application which will take context from the document provided and answer users query based on that .
Today we need to understand how important is the Pre-Processing for the content we have received from the document or any other source
Text preprocessing is a crucial step in natural language processing (NLP) workflows, impacting the performance and effectiveness of downstream tasks such as text classification, sentiment analysis, and language modelling. In this article, we explore and compare several common text preprocessing strategies, highlighting their trade-offs and considerations.
1. Lowercasing
Lowercasing involves converting all text to lowercase. In the context of a conversational chat app, lowercasing is often crucial for maintaining consistency. It ensures that the language model treats words in a case-insensitive manner, improving the model’s ability to recognize and understand user input. For example, without lowercasing, the model might treat “Hello” and “hello” as distinct words, potentially leading to inconsistencies in responses.
Original: “The Quick Brown Fox Jumps Over the Lazy Dog’s Fence.”
Lowercased: “the quick brown fox jumps over the lazy dog’s fence.”
www.asktopdf.com
Consideration for QA Chat App
In a QA-based conversational chat app, lowercasing is beneficial for standardizing user input and facilitating a smoother interaction. Users may not always adhere to the consistent casing, and lower casing helps mitigate this variability.
2. HTML Tag Removal
HTML tag removal focuses on extracting the main content from HTML-formatted text. In a conversational chat app, user inputs might not contain HTML tags, but if you’re dealing with mixed-format text, removing HTML tags ensures that the language model focuses solely on the textual content, disregarding any formatting or metadata.
Original: “The Quick Brown Fox Jumps Over the Lazy Dog’s Fence. Read more at https://example.com."
Without HTML Tags: “The Quick Brown Fox Jumps Over the Lazy Dog’s Fence. Read more at .”
www.asktopdf.com
Consideration for QA Chat App
For a QA-based chat app, HTML tag removal may not be as critical unless user inputs involve rich text formatting. However, it contributes to a cleaner input representation for the language model, allowing it to concentrate on the user’s intended message.
3. Stopword Removal
Stopword removal entails filtering out common words (stopwords) that may not carry significant meaning. In a conversational chat app, stopwords such as “and,” “the,” or “in” might not contribute substantially to understanding user queries. Removing these stopwords can reduce noise and improve the efficiency of the language model.
Original: “the quick brown fox jumps over the lazy dog’s fence.”
Stopword Removed: “quick brown fox jumps lazy dog’s fence.”
www.asktopdf.com
Consideration for QA Chat App
For question-answer-based chat applications, stopword removal is valuable in streamlining the input. It helps the model focus on content-carrying words, potentially enhancing the accuracy of question interpretation and answer generation.
4. Tokenization
Tokenization involves breaking text into individual words or tokens. In the context of a conversational chat app, tokenization is a fundamental step for structuring user queries. Each token represents a unit of meaning, making it easier for the language model to process and understand user inputs.
Original: “quick brown fox jumps lazy dog’s fence.”
Tokenized: [“quick”, “brown”, “fox”, “jumps”, “lazy”, “dog’s”, “fence”]
www.asktopdf.com
Consideration for QA Chat App
Tokenization is crucial for building a coherent conversational flow. It aids in identifying key components of a question, allowing the language model to comprehend user intent and formulate appropriate responses.
5. Stemming
Stemming aims to reduce words to their base or root form. In a conversational chat app, stemming can be beneficial for consolidating variations of words, ensuring that the model recognizes different forms of a word as equivalent.
Original: “quick brown fox jumps lazy dog’s fence.”
Stemmed: “quick brown fox jump lazi dog’s fence.”
www.asktopdf.com
Consideration for QA Chat App
For a QA-based chat app, stemming may aid in capturing the core meaning of user queries. However, caution is required to prevent over-stemming, which could lead to the loss of important nuances in language.
It’s important to note that the choice of strategy depends on the specific requirements and nature of the application being developed. These strategies can be used in combination with each other but it depend on the type of application you are building like if you go for Tokenization you may have to lose the formatting of the document up to some extent.
Here’s the Pros and Cons table for each of the text preprocessing strategies discussed earlier in the context of building a question-answer-based conversational chat application using a Language Model (LLM):
This table summarizes the advantages and disadvantages of each strategy, emphasizing the trade-offs involved in their application within the context of a conversational chat application. The selection of these strategies should align with the specific needs and characteristics of the application you are building.
This is the Step-1 in building a chat app like asktopdf.com
Read about Step-2 here
