High-recall Retrieval Research Articles

SALτ: efficiently stopping TAR by improving priors estimates

In high recall retrieval tasks, human experts review a large pool of documents with the goal of satisfying an information need. Documents are prioritized for review through an active learning policy, and the process is usually referred to as Technology-Assisted Review (TAR). TAR tasks also aim to stop the review process once the target recall is achieved to minimize the annotation cost. In this paper, we introduce a new stopping rule called SALτR\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$_\ au ^R$$\\end{document} (SLD for Active Learning), a modified version of the Saerens–Latinne–Decaestecker algorithm (SLD) that has been adapted for use in active learning. Experiments show that our algorithm stops the review well ahead of the current state-of-the-art methods, while providing the same guarantees of achieving the target recall.

An effective High Recall Retrieval method

The High Recall Retrieval (HRR) problem is one of the fundamental tasks for many applications such as patent retrieval, legal search, medical search, marketing research, charging and collecting tax, and literature review, etc. Given the data set obtained by the user’s query, the HRR problem is defined as finding the full set of relevant documents while less review effort will be required. It is very expensive to review a lot of documents since most of the reviewers are experts in the specific fields such as patent attorneys, lawyers, marketing, and medical professionals. However, the existing HRR methods have been far from satisfactory to make them enumerate all relevant documents. This is due to the fact that not only the sheer volume of documents inevitably including noises (non-relevant documents) but also the threshold measurements have been inadequately adopted. To deal with these problems, we propose a novel solution to efficiently find all the relevant documents among a large set of results. It consists of two steps: (a) to effectively classify the entire documents and (b) to select the representative documents in each class. We formalized the problem and theoretically verified the upper-bound of our method. In the experiments, our method is more efficient than the state-of-the-art query expansion methods.

Relevance maximization for high-recall retrieval problem: finding all needles in a haystack

High-recall retrieval problem, aiming at finding the full set of relevant documents in a huge result set by effective mining techniques, is particularly useful for patent information retrieval, legal document retrieval, medical document retrieval, market information retrieval, and literature review. The existing high-recall retrieval methods, however, have been far from satisfactory to retrieve all relevant documents due to not only high-recall and precision threshold measurements but also a sheer minimize the number of reviewed documents. To address this gap, we generalize the problem to a novel high-recall retrieval model, which can be represented as finding all needles in a giant haystack. To compute candidate groups consisting of k relevant documents efficiently, we propose dynamic diverse retrieval algorithms specialized for the patent-searching method, in which an effective dynamic interactive retrieval can be achieved. In the various types of datasets, the dynamic ranking method shows considerable improvements with respect to time and cost over the conventional static ranking approaches.

Active Bucket Categorization for High Recall Video Retrieval

<?Pub Dtl=""?> There are large amounts of digital video available. High recall retrieval of these requires going beyond the ranked results, which is the common target in high precision retrieval. To aid high recall retrieval, we propose Active Bucket Categorization, which is a multicategory interactive learning strategy which extends MediaTable <citerefgrp xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><citeref refid="ref1"/> </citerefgrp> , our multimedia categorization tool. MediaTable allows users to place video shots into buckets: user-assigned subsets of the collection. Our Active Bucket Categorization approach augments this by unobtrusively expanding these buckets with related footage from the whole collection. In this paper, we propose an architecture for active bucket-based video retrieval, evaluate two different learning strategies, and show its use in video retrieval with an evaluation using three groups of nonexpert users. One baseline group uses only the categorization features of MediaTable such as sorting and filtering on concepts and fast grid preview, but no online learning mechanisms. One group uses on-demand passive buckets. The last group uses fully automatic active buckets which autonomously add content to buckets. Results indicate a significant increase in the number of relevant items found for the two groups of users using bucket expansions, yielding the best results with fully automatic bucket expansions, thereby aiding high recall video retrieval significantly.