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Information-retrieval (IR) based QA
The system relies on the massive information in the collected data resource. Given a user question, information retrieval is used to find relevant passages. Then neural reading comprehension algorithms read these retrieved passages and draw an answer directly from spans of text.
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Two Major Paradigms for QA
Information-retrieval (IR) based QA
Knowledge-based Question Answering (QA)
Long-form Question Answering(QA)
Community Question Answering(QA)
Factoid Questions
Question-Answer Format
Open-Domain Question Answering
Prompt Templates for General-Purpose Question Answering
Importance of Question Answering in NLP
Multiple-Choice Question Answering
Analyzing a Customer Support Automation Task
A software company wants to develop an internal system to help employees quickly find factual information, such as 'What is the company's policy on remote work?' or 'Who is the project manager for Project Phoenix?'. All the necessary information is stored in a highly structured, internal corporate database. Which approach to building this system would be the most direct and efficient for retrieving these specific, factual answers?
Analyzing Query Complexity in Automated Systems
Information-retrieval (IR) based QA
Document(In IR)
Collection(In IR)
Term(In IR)
Query(In IR)
Term Frequency(TF)
Document Frequency(DF)
Document Scoring
BM25
Stop Words
Inverted Index
Using Datastores for Text Retrieval
Retrieval-Augmented Generation (RAG)