Identifying Relations for Open Information Extraction

By Anthony Fader, Stephen Soderland, Oren Etzioni. 2011

636 citations recorded by the source corpus.

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In Natural Language Processing

Citations in the corpus

Semantic Parsing on Freebase from Question-Answer Pairs (2013, positive): “were extracted from ClueWeb09 using the ReVerb open IE system (Fader et al., 2011).”
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (2019, neutral): “Other work in information extraction can also be applied to knowledge base construction with open-text entities (Soderland et al., 2010; Etzioni et al., 2011; Fader et al., 2011; Mausam et al., 2012; Fan et al., 2010; Cui et al., 2018), but these methods typically extract explicitly stated text rela”
Modeling Biological Processes for Reading Comprehension (2014, neutral): “Work in this ﬁeld has focused mostly on macro-reading, i.e., processing large text collections and extracting knowledge bases of facts (Et-zioni et al., 2006; Carlson et al., 2010; Fader et al., 2011).”
Semantic Parsing via Paraphrasing (2014, neutral): “As another reference point, out of 500,000 relations extracted by the ReVerb Open IE system (Fader et al., 2011), only about 10,000 can be aligned to Freebase (Be-rant et al., 2013).”
A semantic matching energy function for learning with multi-relational data (2013, neutral): “We also compare with lists returned by ReVerb (Fader et al. 2011) (an information extraction tool having extracted information from millions of web pages,12 to be compared with our 50k Wikipedia articles + knowledge bases).”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, neutral): “There have been many efforts for open relation learning, including pattern extraction (Banko et al., 2007; Fader et al., 2011; Mausam et al., 2012; Del Corro and Gemulla, 2013; Angeli et al., 2015; Petroni et al., 2015; Stanovsky and Da-gan, 2016; Mausam, 2016; Cui et al., 2018), relation discovery…”
Semantic Parsing for Single-Relation Question Answering (2014, neutral): “For instance, due to the variety of entity mentions in the real world, the parallel corpus derived from the WikiAnswers data and ReVerb KB may not contain enough data to train a robust entity linking model.”
Information Extraction over Structured Data: Question Answering with Freebase (2014, neutral): “Note the scale difference: although ReVerb was also extracted from ClueWeb09, there were only 15 million triples to intersect with the relations, while we had 1.2 billion alignment pairs.”
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning (2016, neutral): “Open Information Extraction Existing work in open IE has used external sources from the web to improve extraction accuracy and coverage (Agichtein and Gravano, 2000; Etzioni et al., 2011; Fader et al., 2011; Wu and Weld, 2010).”
A Review of Relational Machine Learning for Knowledge Graphs (2015, neutral): “Unstructured No ReVerb [32], OLLIE [33], PRISMATIC [34]”
Using Structured Events to Predict Stock Price Movement: An Empirical Investigation (2014, positive): “We extract the predicate verb P of a sentence based on the dependency parser of Zhang and Clark (2011), and then ﬁnd the longest sequence of words P v , such that P v starts at P and satis-ﬁes the syntactic and lexical constraints proposed by Fader et al. (2011).”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, neutral): “Beyond KnowItAll [7], other toolkits, such as TextRunner [6], ReVerb [60], leverage semi-supervised designs for collecting relational information.”
Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors (2013, positive): “tion answering (Siri), information retrieval (Google Knowledge Graph), or generally providing inference over structured knowledge to users. Much work has focused on extending existing knowledge bases [5, 6, 2] using patterns or classiﬁers applied to large corpora. We introduce a model that can accur”
Prototypical Representation Learning for Relation Extraction (2021, neutral): “…(Levy et al., 2017; Han et al., 2018b; Gao et al., 2019; Soares et al., 2019; Ye & Ling, 2019) and open-domain scenarios (Banko et al., 2007; Fader et al., 2011; Mausam et al., 2012; Del Corro & Gemulla, 2013; Angeli et al., 2015; Stanovsky & Dagan, 2016; Mausam, 2016; Cui et al., 2018;…”
Fine-Grained Entity Recognition (2012, neutral): “ReVerb (Fader, Soderland, and Etzioni 2011) patterns are mostly multi-word expressions composed of a verb and a noun, with the noun carrying the semantic content of the whole expression.”
Reasoning With Neural Tensor Networks for Knowledge Base Completion (2013, neutral): “However, not all common knowledge that is obvious to people is expressed in text [5, 6, 2, 7].”
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation (2018, neutral): “…sample of entities which are related by p 2) a sample of sentences in which those entities co-occur and 3) the most frequent natural language strings which join entities related by p according to a OpenIE triple database (Schmitz et al., 2012; Fader et al., 2011) extracted from a large text corpus.”
Knowledge vault: a web-scale approach to probabilistic knowledge fusion (2014, neutral): “Knowledge bases that use a fixed ontology should be contrasted with open information extraction (Open IE) approaches, such as Reverb [12], which work at the lexical level.”