Open AccessProceedings Article
Another Sys Called Qanda.
Eric Breck,John D. Burger,Lisa Ferro,Warren R. Greiff,Marc Light,Inderjeet Mani,Jason D. M. Rennie +6 more
- 01 Jan 2000
TL;DR: A measure of candidate confusability which measures the effectiveness of a set of features for reducing the choices that a ranking system has to make: a coarse form of perplexity is developed.
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Abstract: 2 Principal contact: This year our primary goal was to improve on the performance of our TREC-8 system. In addition to improving the system directly, we worked on a number of tools to aid our development. We continued our work on a tool for automatic scoring of system responses, a “judge” program. We designed a tool for doing regression testing of question answering systems. We developed a measure of candidate confusability which measures the effectiveness of a set of features for reducing the choices that a ranking system has to make: a coarse form of perplexity. Finally, we performed preliminary work on a method for generating supervised training data.
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Citations
Splitting Complex Temporal Questions for Question Answering Systems
Estela Saquete,Patricio Martínez-Barco,Rafael Muñoz,José Luis Vicedo +3 more
- 21 Jul 2004
TL;DR: A multi-layered Question Answering (Q.A.) architecture suitable for enhancing current Q.A. capabilities with the possibility of processing complex questions, which are questions whose answer needs to be gathered from pieces of factual information scattered in different documents.
A Discourse-Based Approach for Arabic Question Answering
Jawad Sadek,Farid Meziane +1 more
- 04 Nov 2016
TL;DR: The Text Parser presented here considers the sentence as the basic unit of text and incorporates a set of heuristics to avoid computational explosion, and reached a significant improvement over the baseline with a Recall of 68% and MRR of 0.62.
Towards answer extraction: an application to technical domains
Fabio Rinaldi,James Dowdall,Michael Hess,Diego Mollá,Rolf Schwitter +4 more
- 21 Jul 2002
TL;DR: This paper presents one such system (ExtrAns) which works by transforming documents and queries into a semantic representation called Minimal Logical Form (MLF) and derives the answers by logical proof from the documents.
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•Book
Advances in Natural Language Processing: 4th International Conference, EsTAL 2004, Alicante, Spain, October 20-22, 2004. Proceedings
Rafael Muñoz,Jos Luis Vicedo,Particio Martnez-Barco +2 more
- 12 Oct 2004
TL;DR: In this paper, the authors compare and evaluate two approaches of a multilayered QA system applied to temporality, using a Bleu-Inspired algorithm and shallow NLP.
13
Answer Extraction in Technical Domains
Fabio Rinaldi,Michael Hess,Diego Mollá,Rolf Schwitter,James Dowdall,Gerold Schneider,Rachel Fournier +6 more
- 17 Feb 2002
TL;DR: The problems faced in AE are discussed and a system that can exactly pinpoint those parts of documents that contain the information requested, rather than return a set of relevant documents is presented.
9
References
A maximum entropy approach to natural language processing
TL;DR: A maximum-likelihood approach for automatically constructing maximum entropy models is presented and how to implement this approach efficiently is described, using as examples several problems in natural language processing.
Robust temporal processing of news
Inderjeet Mani,George Wilson +1 more
- 03 Oct 2000
TL;DR: An annotation scheme for temporal expressions, and a method for resolving temporal expressions in print and broadcast news, based on both hand-crafted and machine-learnt rules are described.
•Proceedings Article
How to Evaluate Your Question Answering System Every Day ... and Still Get Real Work Done
Eric Breck,John D. Burger,Lisa Ferro,Lynette Hirschman,David House,Marc Light,Inderjeet Mani +6 more
- 01 May 2000
TL;DR: Qaviar as mentioned in this paper is an automated evaluation system for question answering applications, which uses the stemmed content words in the humangenerated answer key to count the answer correct if it exceeds a given recall threshold.
•Posted Content
How to Evaluate your Question Answering System Every Day and Still Get Real Work Done
Eric Breck,John D. Burger,Lisa Ferro,Lynette Hirschman,David House,Marc Light,Inderjeet Mani +6 more
TL;DR: Qaviar, an experimental automated evaluation system for question answering applications, determined that the answer correctness predicted by Qaviar agreed with the human 93% to 95% of the time.
•Proceedings Article
A Sys Called Qanda.
Eric Breck,John D. Burger,Lisa Ferro,David House,Marc Light,Inderjeet Mani +5 more
- 01 Jan 1999
TL;DR: The question answering system was built to experiment with natural language processing technologies such as shallow parsing, named entity tagging, and coreference chaining because it felt that the small number of terms in the questions coupled with the short length of the answers would make NLP technologies clearly beneficial.