# Discriminative Information Retrieval for Knowledge Discovery

Source: [Discriminative Information Retrieval for Knowledge Discovery](<https://www.sota2.com/research/paper/68fa62d41e209131ce9fb6a8>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:27:18.446Z

Authors: Tongfei Chen, Benjamin Van Durme

Published: 2016-10-06T15:04:10.000Z

## Abstract

We propose a framework for discriminative Information Retrieval (IR) atop linguistic features, trained to improve the recall of tasks such as answer candidate passage retrieval, the initial step in text-based Question Answering (QA). We formalize this as an instance of linear feature-based IR (Metzler and Croft, 2007), illustrating how a variety of knowledge discovery tasks are captured under this approach, leading to a 44% improvement in recall for candidate triage for QA.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1610.01901>)

[PDF](<https://arxiv.org/pdf/1610.01901.pdf>)