Open Language Learning for Information Extraction

By Mausam, Michael Schmitz, Stephen Soderland, R. Bart, Oren Etzioni. 2012

857 citations recorded by the source corpus.

Subfields: Natural Language Processing, Information Extraction

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Citations in the corpus

Semantic Parsing on Freebase from Question-Answer Pairs (2013, background): “Finally, although Freebase has thousands of properties, open information extraction (Banko et al., 2007; Fader et al., 2011; Masaum et al., 2012) and associated question answering systems (Fader et al.”
FEVER: a Large-scale Dataset for Fact Extraction and VERification (2018, methodology): “textual sources — including Wikipedia — which were processed with an Open Information Extraction system (Mausam et al., 2012).”
Compositional Semantic Parsing on Semi-Structured Tables (2015, background): “In parallel, open information extraction (Wu and Weld, 2010; Masaum et al., 2012) and knowledge base population (Ji and Grishman, 2011) extract information from web pages and compile them into structured data.”
Crowdsourcing Multiple Choice Science Questions (2017, methodology): “• indicators for 2-step connections between entities in a⇤ and a0 via a KB based on OpenIE triples (Mausam et al., 2012) extracted from pages in Simple Wikipedia about anatomical structures;”
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (2019, background): “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”
Position-aware Attention and Supervised Data Improve Slot Filling (2017, background): “, 2012), where a training set is formed by projecting the relations in an existing knowledge base onto textual instances that contain the entities that the relation connects; and third, Open IE (Fader et al., 2011; Mausam et al., 2012), which views its goal as producing subject-relationobject triple”
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective (2018, background): “Ollie [134], ReVerb [135], and ReNoun [136] and open information extraction systems that apply patterns to Web text.”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, background): “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 Dagan, 2016; Mausam, 2016; Cui et al., 2018), relation discovery (”
Solving Hard Coreference Problems (2019, methodology): “v) ≡ S(pred u(m = u,a =a u)|predd v(m =u,a =a v),cn). Our method is related, but different from the proposal in Balasubramanian et al. (2012), who suggested to extract triples using an OpenIE system (Mausam et al., 2012). We extracted triples by starting from a mention, then extract the predicate an”
A Review of Relational Machine Learning for Knowledge Graphs (2015, background): “Unstructured No ReVerb [32], OLLIE [33], PRISMATIC [34]”
NaturalLI: Natural Logic Inference for Common Sense Reasoning (2014, methodology): “For our database of facts, we run the Ollie OpenIE system (Mausam et al., 2012) over Wikipedia,3 Simple Wikipedia,4 and a random 5% of CommonCrawl.”
Knowledgeable Reader: Enhancing Cloze-Style Reading Comprehension with External Commonsense Knowledge (2018, background): “, 2016) or domain-specific KBs collected with Information Extraction approaches (Fader et al., 2011; Mausam et al., 2012; Bhutani et al., 2016).”
Generating Coherent Event Schemas at Scale (2013, methodology): “(Mausam et al., 2012).”
Rule-Based Information Extraction is Dead! Long Live Rule-Based Information Extraction Systems! (2013, background): “Authors hid rules behind euphemisms such as “de-pendency restrictions” (Mausam et al., 2012), “en-tity type constraints” (Yao et al., 2011), or “seed dic-tionaries” (Putthividhya and Hu, 2011).”
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020, background): “…(1) Open information extraction (Open IE) , as shown in Figure 8, extracts relation phrases and arguments (entities) from text (Banko et al., 2007; Fader et al., 2011; Mausam et al., 2012; Del Corro and Gemulla, 2013; Angeli et al., 2015; Stanovsky and Dagan, 2016; Mausam, 2016; Cui et al., 2018).”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, background): “In addition, there is OLLIE [61] which supports the discover of non-verbal triples.”
Controlling Information Aggregation for Complex Question Answering (2018, background): “This is especially true when reasoning over knowledge graphs built using shallow semantic representations as Open Information Extraction (OIE) triples (Mausam et al. 2012).”
Knowledge vault: a web-scale approach to probabilistic knowledge fusion (2014, methodology): “This literature can be clustered into 4 main groups: (1) approaches such as YAGO [37], YAGO2 [18], DBpedia [3], and Freebase [4], which are built on Wikipedia infoboxes and other structured data sources; (2) approaches such as Reverb [11], OLLIE [24], and PRISMATIC [12], which use open information (”