# Evaluation of Diversification Techniques for Legal Information Retrieval +
## 4 Related Work
### Query Result Diversification  
Result diversification approaches have been proposed as a means to tackle ambiguity and redundancy, in various problems and settings, e.g., diversifying historical archives [19], diversifying user comments on news articles [20], diversifying microblog posts [21; 22], diversifying image retrieval results [23], diversifying recommendations [24], utilizing a plethora of algorithms and approaches, e.g., learning algorithms [25], approximation algorithms [26], page rank variants [27] and conditional probabilities [28].  
Users of (web) search engines typically employ keyword-based queries to express their information needs. These queries are often underspecified or ambiguous to some extent [29]. Different users who pose exactly the same query may have very different query intents. Simultaneously, the documents retrieved by an IR system may reflect superfluous information. Search result diversification aims to solve this problem, by returning diverse results that can fulfill as many different information needs as possible. Published literature on search result diversification is reviewed in [30; 31].  
The maximal marginal relevance criterion (MMR), presented in [3], is one of the earliest works on diversification and aims at maximizing relevance while minimizing similarity to higher ranked documents. Search results are re-ranked as the combination of two metrics, one measuring the similarity among documents and the other the similarity between documents and the query. In [4], a set of diversification axioms is introduced, and it is proven that it is not possible for a diversification algorithm to satisfy all of them. Additionally, since there is no single objective function suitable for every application domain, the authors propose three diversification objectives, which we adopt in our work. These objectives differ in the level where the diversity is calculated, e.g., whether it is calculated per separate document or on the average of the currently-selected documents.  
In another approach, researchers utilized explicit knowledge so as to diversify search results. In [32], the authors proposed a diversification framework, where the different aspects of a given query are represented in terms of sub-queries, and documents are ranked based on their relevance to each sub-query, while in [33] the authors proposed a diversification objective that tries to maximize the likelihood of finding a relevant document in the \(top-k\) positions given the categorical information of the queries and documents. Finally, the work described in [34] organizes user intents in a hierarchical structure and proposes a diversification framework to explicitly leverage the hierarchical intent.  
The key difference between these works and the ones utilized in this paper is that we do not rely on external knowledge, e.g., taxonomy, query logs to generate diverse results. Queries are rarely known in advance, thus probabilistic methods to compute external information are not only expensive to compute, but also have a specialized domain of applicability. Instead, we evaluate methods that rely only on implicit knowledge of the legal corpus utilized and on computed values, using similarity (relevance) and diversity functions in the data domain.