Corpus ID: 1255845

Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions

@inproceedings{Clark2016CombiningRS,
  title={Combining Retrieval, Statistics, and Inference to Answer Elementary Science Questions},
  author={Peter Clark and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Oyvind Tafjord and Peter D. Turney and Daniel Khashabi},
  booktitle={AAAI},
  year={2016}
}
What capabilities are required for an AI system to pass standard 4th Grade Science Tests. [...] Key Result We conclude with a detailed analysis, illustrating the complementary strengths of each method in the ensemble. Our datasets are being released to enable further research.Expand
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References

SHOWING 1-10 OF 31 REFERENCES
Exploring Markov Logic Networks for Question Answering
TLDR
A system that reasons with knowledge derived from textbooks, represented in a subset of firstorder logic, called Praline, which demonstrates a 15% accuracy boost and a 10x reduction in runtime as compared to other MLNbased methods, and comparable accuracy to word-based baseline approaches. Expand
Automatic Construction of Inference-Supporting Knowledge Bases
TLDR
This paper describes the work on automatically constructing an inferential knowledge base, and applying it to a question-answering task, and suggests several challenges that this approach poses, and innovative, partial solutions that have been developed. Expand
Learning to Rank Answers to Non-Factoid Questions from Web Collections
TLDR
This work shows that it is possible to exploit existing large collections of question–answer pairs to extract such features and train ranking models which combine them effectively, providing one of the most compelling evidence to date that complex linguistic features such as word senses and semantic roles can have a significant impact on large-scale information retrieval tasks. Expand
Project Halo Update - Progress Toward Digital Aristotle
TLDR
The design and evaluation results for a system called AURA are presented, which enables domain experts in physics, chemistry, and biology to author a knowledge base and that then allows a different set of users to ask novel questions against that knowledge base. Expand
Open question answering over curated and extracted knowledge bases
TLDR
This paper presents OQA, the first approach to leverage both curated and extracted KBs, and demonstrates that it achieves up to twice the precision and recall of a state-of-the-art Open QA system. Expand
Elementary School Science and Math Tests as a Driver for AI: Take the Aristo Challenge!
TLDR
This work is working on a specific version of this challenge, namely having the computer pass Elementary School Science and Math exams, the most difficult requiring significant progress in AI. Expand
My Computer Is an Honor Student - but How Intelligent Is It? Standardized Tests as a Measure of AI
TLDR
It is argued that machine performance on standardized tests should be a key component of any new measure of AI, because attaining a high level of performance requires solving significant AI problems involving language understanding and world modeling - critical skills for any machine that lays claim to intelligence. Expand
Markov logic networks
TLDR
Experiments with a real-world database and knowledge base in a university domain illustrate the promise of this approach to combining first-order logic and probabilistic graphical models in a single representation. Expand
A probabilistic graphical model for joint answer ranking in question answering
TLDR
A probabilistic graphical model is applied for answer ranking in question answering which estimates the joint probability of correctness of all answer candidates, from which the probability of Correctness of an individual candidate can be inferred. Expand
Question Answering Using Enhanced Lexical Semantic Models
TLDR
This work focuses on improving the performance using models of lexical semantic resources and shows that these systems can be consistently and significantly improved with rich lexical semantics information, regardless of the choice of learning algorithms. Expand
...
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