Peter Clark

Allen Institute for AI
https://allenai.org/team/peterc
Publications36
h-index28
Citations6,157
Highly Influential Citations677
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Rule Induction with CN2: Some Recent Improvements
TLDR
Improvements to the CN2 algorithm are described, including the use of the Laplacian error estimate as an alternative evaluation function and it is shown how unordered as well as ordered rules can be generated. Expand
The CN2 induction algorithm
TLDR
A description and empirical evaluation of a new induction system, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present, is presented. Expand
The CN2 Induction Algorithm
TLDR
A description and empirical evaluation of a new induction system, CN2, designed for the efficient induction of simple, comprehensible production rules in domains where problems of poor description language and/or noise may be present. Expand
The Sixth PASCAL Recognizing Textual Entailment Challenge
TLDR
This paper presents the Sixth Recognizing Textual Entailment (RTE-6) challenge, as the traditional Main Task was replaced by a new task, similar to the RTE-5 Search Pilot, in which TextualEntailment is performed on a real corpus in the Update Summarization scenario. Expand
SciTaiL: A Textual Entailment Dataset from Science Question Answering
TLDR
A new dataset and model for textual entailment, derived from treating multiple-choice question-answering as an entailment problem, is presented, and it is demonstrated that one can improve accuracy on SCITAIL by 5% using a new neural model that exploits linguistic structure. Expand
Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
TLDR
A new question set, text corpus, and baselines assembled to encourage AI research in advanced question answering constitute the AI2 Reasoning Challenge (ARC), which requires far more powerful knowledge and reasoning than previous challenges such as SQuAD or SNLI. Expand
Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering
TLDR
A new kind of question answering dataset, OpenBookQA, modeled after open book exams for assessing human understanding of a subject, and oracle experiments designed to circumvent the knowledge retrieval bottleneck demonstrate the value of both the open book and additional facts. Expand
QASC: A Dataset for Question Answering via Sentence Composition
TLDR
This work presents a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question, and presents a two-step approach to mitigate the retrieval challenges. Expand
Answer Extraction as Sequence Tagging with Tree Edit Distance
TLDR
A linear-chain Conditional Random Field based on pairs of questions and their possible answer sentences, learning the association between questions and answer types is constructed, casting answer extraction as an answer sequence tagging problem for the first time. Expand
SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge
We present the results of the Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge, aiming to bring together researchers in educational NLP technology and textual… Expand
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