Machine Reading

By Oren Etzioni, Michele Banko, Michael J. Cafarella. 2006

158 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Long Short-Term Memory-Networks for Machine Reading (2016, background): “Machine reading has been recently recognized as a signiﬁcant milestone for artiﬁcial intelligence (Poon and Domingos, 2010a; Etzioni et al., 2006) leveraging advances in the ﬁelds of natural language processing (NLP), machine learning, and probabilistic reasoning.”
Modeling Biological Processes for Reading Comprehension (2014, background): “, processing large text collections and extracting knowledge bases of facts (Etzioni et al., 2006; Carlson et al., 2010; Fader et al., 2011).”
DELIS Dynamically Evolving , Large-scale Information Systems Integrated Project Member of the FET Proactive Initiative Complex Systems DELIS-TR-594 Yago : A Large Ontology from Wikipedia and WordNet (2007, background): “TextRunner has the even more ambitious goal of extracting all instances of all meaningful relations from Web pages, a paradigm referred to as machine reading [63].”
Advances in natural language processing (2015, background): “PaleoDeepDive, itsapplicationtothefossilrecord,has recentlybeen shown to do a better job at fact extraction from journal articles than the scientist volunteers who maintain the PaleoBiology Database ( 43 ).”
Joint learning of ontology and semantic parser from text (2016, unknown): “One of the ultimate goals of Natural Language Processing (NLP) is machine reading [14], the automatic, unsupervised understanding of text.”
Strategies for lifelong knowledge extraction from the web (2007, background): “Driven by the increasing availability of online information and recent advances in the fields of machine learning and natural language processing, the AI community is increasingly optimistic that knowledge capture from text is within reach [4, 9].”
The Winograd Schema Challenge (2012, background): “…http://trec.nist.gov), and RTE have statistical components at their core; this is true even for natural language programs that emphasize the importance of knowledge representation and reasoning, such as the DARPA Machine Reading Program (Strassel et al. 2010), (Etzioni, Banko, and Cafarella 2006).”
YAGO: A Large Ontology from Wikipedia and WordNet (2008, background): “TextRunner has the even more ambitious goal of extracting all instances of all meaningful relations from Web pages, a paradigm referred to as machine reading [63].”
Open Information Extraction: The Second Generation (2011, unknown): “” [Etzioni et al., 2006].”
Entity Linking meets Word Sense Disambiguation: a Unified Approach (2014, background): “The automatic understanding of the meaning of text has been a major goal of research in computational linguistics and related areas for several decades, with ambitious challenges, such as Machine Reading (Etzioni et al., 2006) and the quest for knowledge (Schubert, 2006).”
The debate over understanding in AI’s large language models (2022, background): “Thus benchmarks or assessments that would be appropriate for measuring human understanding might not be appropriate for assessing such machines [10, 18, 31].”
Dialog state tracking, a machine reading approach using Memory Network (2016, methodology): “We propose to formalize the dialog state tracking task as a machine reading problem [4, 1].”
Neural context embeddings for automatic discovery of word senses (2015, background): “A wide range of applications have been proposed where WSI could be useful, ranging from basic linguistic and lexicographical research (Nasiruddin et al., 2014), machine reading (Etzioni et al., 2006) and information retrieval (V´eronis, 2004).”
Differentiable Reasoning on Large Knowledge Bases and Natural Language (2019, background): “Notably, Natural Language Understanding (NLU) and Machine Reading (MR) aim at building models and systems with the ability to read text, extract meaningful knowledge, and reason with it (Etzioni, Banko, and Cafarella 2006; Hermann et al. 2015; Weston et al. 2015; Das et al. 2017).”