# Multi Stage Retrieval for Web Search During Crisis
## 2 Related Works
### Text Retrieval  
Text retrieval aims to satisfy a user's information need, typically expressed as a query, by identifying and ranking relevant documents from a large collection [14]. The general architecture of such systems involves document processing, the creation of an efficient index, query processing, a matching or ranking function to score documents against the query, and finally, the presentation of results. The inverted index is a central component linked to the performance of traditional information retrieval (IR) systems. It is a data structure that maps terms to the documents containing them. Significant research has focused on optimizing these inverted indexes for both speed and storage, employing techniques such as self-indexing to provide fast access [15] and sophisticated compression strategies coupled with optimized document ordering to manage vast document collections effectively [16]. Early retrieval models, such as the Boolean model, which rely on set theory and Boolean operators, offer precise but often rigid retrieval with no inherent ranking of documents. The Vector Space Model (VSM) [17] was a step forward, representing documents and queries as vectors in a multi-dimensional space where each dimension corresponds to a term [18]. Term weights, often calculated using TF-IDF (term frequency-inverse document frequency), reflect the importance of terms both locally within a document and globally across the collection. Relevance is then typically assessed using similarity measures like cosine similarity, allowing for partial matching and ranked retrieval. Concurrently, probabilistic models offered a different theoretical perspective, aiming to rank documents based on their estimated probability of relevance to a query. The probability ranking principle (PRP) [19] states that optimal retrieval is achieved by ranking documents in decreasing order of their probability of relevance, given all available evidence. The Okapi BM25 ranking function [20; 21] builds directly on these probabilistic foundations. It calculates document scores based on query term presence, considering term frequency saturation (recognizing that repeated terms offer diminishing returns), inverse document frequency (IDF) to emphasize rarer, more informative terms, and document length normalization. The computational efficiency of BM25 is the result of extensive algorithmic engineering for core search operations like list traversal and score accumulation, particularly for in-memory systems [22]. Beyond core term weighting, traditional IR also explored other lexical and structural cues to enhance ranking. For example, the proximity of query terms within documents or the structural zone in which terms appear (e.g., title, abstract) [23]. Given its efficiency, BM25 is well-suited as an initial high-recall retrieval stage in a multi-stage pipeline. This allows for rapid scanning of large document sets to form a broad candidate pool. However, BM25's effectiveness is primarily driven by exact term matches, so it inherently struggles to understand semantic meaning, synonyms, or paraphrased expressions. This limitation is particularly pronounced with short or ambiguous queries or when dealing with diverse and noisy vocabularies, such as those found in social media during crises, potentially leading to lower precision or missing relevant documents that use different terminology [4]. To address such limitations, recent advancements in Natural Language Processing (NLP) and deep learning have led to the development of transformer-based retrieval models [24]. These models leverage deep contextual embeddings to capture semantic relationships, converting queries and documents into high-dimensional vector representations that allow for more effective semantic matching. Transformers are also widely employed as rerankers [25], where they process query-document pairs from an initial candidate set to provide a more accurate final ranking.