A data scientist is developing a natural language understanding (NLU) model to analyze user queries for a chatbot in the financial services industry. The goal is to accurately interpret user intent, such as distinguishing between "transfer funds to my savings" and "what is the interest on my savings?". After an initial analysis, the data scientist observes that a standard stop word removal process is causing misinterpretation of certain queries. What is the most effective next step to address this issue?
Eliminate the stop word removal step entirely from the text preparation pipeline to ensure no words are lost.
Develop a custom stop word list tailored to the financial domain, carefully evaluating the impact of removing each word on intent classification accuracy.
Replace the current standard stop word list with one from a different NLP library, such as switching from NLTK's list to spaCy's list.
Apply stemming and lemmatization to the corpus before the stop word removal step to normalize the tokens.
The correct action is to develop a custom stop word list. Standard stop word lists are generic and often remove words like "to", "on", and "what", which can be critical for understanding context and intent in specific domains like financial queries. By creating a custom list, the data scientist can selectively remove high-frequency, low-information words while preserving those essential for discerning the user's specific intent.
Eliminating stop word removal entirely is not optimal because it forgoes the benefits of reducing data dimensionality and noise from genuinely irrelevant words. Simply switching to another standard list from a different library would likely lead to the same problem, as these lists are also generic and not tailored to a specific domain. Applying stemming or lemmatization before stop word removal does not address the core issue, which is the incorrect removal of context-bearing words, not their morphological form.
Ask Bash
Bash is our AI bot, trained to help you pass your exam. AI Generated Content may display inaccurate information, always double-check anything important.
Why are standard stop word lists not effective for domain-specific applications like financial queries?
Open an interactive chat with Bash
What steps should a data scientist follow to create a custom stop word list for a domain like financial services?
Open an interactive chat with Bash
How does removing stop words reduce data dimensionality in natural language processing (NLP)?