Passage retrieval methods refer to techniques and algorithms used to retrieve relevant passages or segments of text from a larger document or corpus. These methods are commonly employed in information retrieval systems and question-answering systems, where the goal is to locate specific information within a large amount of text.
Passage retrieval methods typically involve the following steps:
- Document indexing: The documents in the corpus are processed and indexed to facilitate efficient retrieval. This indexing step creates data structures that allow for quick access to document information.
- Query processing: When a user submits a query, it is processed and analyzed to determine the user’s information needs. This may involve parsing the query, identifying relevant terms or keywords, and applying various techniques like stemming or lemmatization to improve retrieval accuracy.
- Passage ranking: The indexed documents are then ranked based on their relevance to the query. Various ranking algorithms can be employed, such as TF-IDF (Term Frequency-Inverse Document Frequency), BM25 (Best Match 25), or neural network-based approaches like BERT (Bidirectional Encoder Representations from Transformers). These algorithms assign a relevance score to each document based on the query terms and their occurrences within the document.
- Passage selection: Once the documents are ranked, the passages or segments within the top-ranked documents that are deemed most relevant to the query are selected as the retrieved passages. These passages can range in length from short sentences to longer paragraphs, depending on the requirements of the application.
Passage retrieval methods aim to provide precise and concise information to users by extracting relevant content from a larger document collection. By focusing on retrieving specific passages instead of entire documents, these methods enable more efficient and targeted information retrieval, particularly in scenarios where users are seeking answers to specific questions or seeking relevant information within a large text corpus.