A new metric for patent retrieval evaluation

Patent retrieval is generally considered to be a recall-oriented information retrieval task that is growing in importance. Despite this fact, precision based scores such as mean average precision (MAP) remain the primary evaluation measures for patent retrieval. Our study examines different evaluation measures for the recall-oriented patent retrieval task and shows the limitations of the current scores in comparing different IR systems for this task. We introduce PRES, a novel evaluation metric for this type of application taking account of recall and user search effort. The behaviour of PRES is demonstrated on 48 runs from the CLEF-IP 2009 patent retrieval track. A full analysis of the performance of PRES shows its suitability for measuring the retrieval effectiveness of systems from a recall focused perspective taking into account the expected search effort of patent searchers

PRES: A score metric for evaluating recall-oriented information retrieval applications

Information retrieval (IR) evaluation scores are generally designed to measure the effectiveness with which relevant documents are identified and retrieved. Many scores have been proposed for this purpose over the years. These have primarily focused on aspects of precision and recall, and while these are often discussed with equal importance, in practice most attention has been given to precision focused metrics. Even for recalloriented IR tasks of growing importance, such as patent retrieval, these precision based scores remain the primary evaluation measures. Our study examines different evaluation measures for a recall-oriented patent retrieval task and demonstrates the limitations of the current scores in comparing different IR systems for this task. We introduce PRES, a novel evaluation metric for this type of application taking account of recall and the user’s search effort. The behaviour of PRES is demonstrated on 48 runs from the CLEF-IP 2009 patent retrieval track. A full analysis of the performance of PRES shows its suitability for measuring the retrieval effectiveness of systems from a recall focused perspective taking into account the user’s expected search effort

Building simulated queries for known-item topics: an analysis using six european languages

There has been increased interest in the use of simulated queries for evaluation and estimation purposes in Information Retrieval. However, there are still many unaddressed issues regarding their usage and impact on evaluation because their quality, in terms of retrieval performance, is unlike real queries. In this paper, we focus on methods for building simulated known-item topics and explore their quality against real known-item topics. Using existing generation models as our starting point, we explore factors which may influence the generation of the known-item topic. Informed by this detailed analysis (on six European languages) we propose a model with improved document and term selection properties, showing that simulated known-item topics can be generated that are comparable to real known-item topics. This is a significant step towards validating the potential usefulness of simulated queries: for evaluation purposes, and because building models of querying behavior provides a deeper insight into the querying process so that better retrieval mechanisms can be developed to support the user

Generating queries from user-selected text

People browsing the web or reading a document may see text passages that describe a topic of interest, and want to know more about it by searching. Manually formulating a query from that text can be difficult, however, and an effec-tive search is not guaranteed. In this paper, to address this scenario, we propose a learning-based approach which gener-ates effective queries from the content of an arbitrary user-selected text passage. Specifically, the approach extracts and selects representative chunks (noun phrases or named entities) from the content (a text passage) using a rich set of features. We carry out experiments showing that the se-lected chunks can be effectively used to generate queries both in a TREC environment, where weights and query structure can be directly incorporated, and with a “black-box ” web search engine, where query structure is more limited

Combinatoric Models of Information Retrieval Ranking Methods and Performance Measures for Weakly-Ordered Document Collections

This dissertation answers three research questions: (1) What are the characteristics of a combinatoric measure, based on the Average Search Length (ASL), that performs the same as a probabilistic version of the ASL?; (2) Does the combinatoric ASL measure produce the same performance result as the one that is obtained by ranking a collection of documents and calculating the ASL by empirical means?; and (3) When does the ASL and either the Expected Search Length, MZ-based E, or Mean Reciprocal Rank measure both imply that one document ranking is better than another document ranking? Concepts and techniques from enumerative combinatorics and other branches of mathematics were used in this research to develop combinatoric models and equations for several information retrieval ranking methods and performance measures. Empirical, statistical, and simulation means were used to validate these models and equations. The document cut-off performance measure equation variants that were developed in this dissertation can be used for performance prediction and to help study any vector V of ranked documents, at arbitrary document cut-off points, provided that (1) relevance is binary and (2) the following information can be determined from the ranked output: the document equivalence classes and their relative sequence, the number of documents in each equivalence class, and the number of relevant documents that each class contains. The performance measure equations yielded correct values for both strongly- and weakly-ordered document collections