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PDDL-the planning domain definition language
TLDR
This manual describes the syntax of PDDL, the Planning Domain Definition Language, the problem-specification language for the AIPS-98 planning competition. Expand
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TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension
TLDR
We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. Expand
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Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations
TLDR
We present MULTIR, a novel approach for multi-instance learning with overlapping relations that combines a sentence-level extraction model with a simple, corpus-level component for aggregating the individual facts. Expand
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Unsupervised named-entity extraction from the Web: An experimental study
TLDR
The KnowItAll system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, scalable manner. Expand
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SpanBERT: Improving Pre-training by Representing and Predicting Spans
TLDR
We present SpanBERT, a pre-training method that is designed to better represent and predict spans of text. Expand
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UCPOP: A Sound, Complete, Partial Order Planner for ADL
TLDR
We describe the ucpop partial order planning algorithm which handles a subset of Pednault's ADL action representation and demonstrate that it is both sound and complete for this representation. Expand
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Fine-Grained Entity Recognition
TLDR
This paper defines a fine-grained set of 112 tags, formulates the tagging problem as multi-class, multi-label classification, describes an unsupervised method for collecting training data, and presents the FIGER implementation. Expand
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An Introduction to Least Commitment Planning
TLDR
We introduce UCPOP, a planner that manages actions with disjunctive precondition, conditional effects, and universal quantification over dynamic universes. Expand
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Web-scale information extraction in knowitall: (preliminary results)
TLDR
This paper introduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner. Expand
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Wrapper Induction for Information Extraction
TLDR
We introduce wrapper induction, a method for automatically constructing wrappers, and identify hlrt, a wrapper class that is e ciently learnable, yet expressive enough to handle 48% of a recently surveyed sample of Internet resources. Expand
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