# Question Answering over Unstructured Data without Domain Restrictions

Source: [Question Answering over Unstructured Data without Domain Restrictions](<https://www.sota2.com/research/paper/68fa5f9c1e209131ce9ead38>)

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

Data updated: 2025-10-25T19:21:46.312Z

Authors: Jochen L. Leidner

Published: 2002-07-14T21:04:43.000Z

## Abstract

Information needs are naturally represented as questions. Automatic Natural-Language Question Answering (NLQA) has only recently become a practical task on a larger scale and without domain constraints.   This paper gives a brief introduction to the field, its history and the impact of systematic evaluation competitions.   It is then demonstrated that an NLQA system for English can be built and evaluated in a very short time using off-the-shelf parsers and thesauri. The system is based on Robust Minimal Recursion Semantics (RMRS) and is portable with respect to the parser used as a frontend. It applies atomic term unification supported by question classification and WordNet lookup for semantic similarity matching of parsed question representation and free text.

## 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/cs/0207058>)

[PDF](<https://arxiv.org/pdf/cs/0207058.pdf>)