StatSnowball: a statistical approach to extracting entity relationships

By Jun Zhu, Zaiqing Nie, Xiaojiang Liu, Ji-rong Wen. 2009

304 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Information Extraction

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Citations in the corpus

Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, background): “, 2019), and data collection (Riloff et al., 1999; Etzioni et al., 2005; Pantel and Pennacchiotti, 2006; Rozenfeld and Feldman, 2008; Nakashole et al., 2011; Zhu et al., 2009; Gao et al., 2020).”
Neural Snowball for Few-Shot Relation Learning (2019, methodology): “cts by iterative pattern expansion from web pages.Agichtein and Gravano(2000) propose Snowball to improve such iterative mechanism with better pattern extraction and evaluation methods. Based on that,Zhu et al. (2009) adopt statistical methods for better pattern selection.Batista et al.(2015) use wo”
DeepDive: Declarative Knowledge Base Construction (2016, methodology): “Within this space, there are three styles of systems that based on classiﬁcation-based frameworks [2,3,6,12,45], maximum a posteriori (MAP) [21,29, 40], and probabilistic graphical models [10, 35, 49].”
YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia: Extended Abstract (2013, methodology): “These include KnowItAll and its successor TextRunner [4,5], DBpedia [3], the Omnivore system [48], work on distilling Web tables and lists into facts [49–51], the ReadTheWeb project [52] the StatSnowball methods used for building EntityCube [53] and its follow-up project Probase [54], WikiNet [36], ”
DeepDive: Web-scale Knowledge-base Construction using Statistical Learning and Inference (2012, background): “It has recently received tremendous interest from academia, e.g., CMU’s NELL [2] and MPI’s YAGO [7,9], and from industry, e.g., IBM’s DeepQA [5] and Microsoft’s EntityCube [16].”
Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference (2012, methodology): “, 2011), Elementary uses a probabilistic logic language called Markov logic (Richardson & Domingos, 2006) that has been applied to a wide range of text-related applications (Poon & Domingos, 2007; Suchanek et al., 2009; Zhu et al., 2009; Andrzejewski, Livermore, Zhu, Craven, & Recht, 2011).”
Incremental Knowledge Base Construction Using DeepDive (2015, methodology): “ledge Base Construction (KBC) KBC has been an area of intense study over the last decade, moving from pattern matching [24] and rule-based systems [33,34] to systems that use machine learning for KBC [6{8, 10, 17, 18, 30, 38, 44, 51, 59, 63]. Many groups have studied how to improve the quality of sp”
Building a Large-scale Multimodal Knowledge Base for Visual Question Answering (2015, background): “Knowledge Bases Most knowledge base work in the NLP and database communities focuses on organizing and retrieving only textual information in a structured representation [2, 8, 21, 41].”
Machine Translation Detection from Monolingual Web-Text (2013, background): “…(Nie et al., 1999; Shi et al., 2006; Ishisaka et al., 2009; Jiang et al., 2009) corpus that can be used for training statistical models for NLP tools, as well as for building a large-scale knowledge-base (Suchanek et al., 2007; Zhu et al., 2009; Fader et al., 2011; Nakashole et al., 2012).”
Reasoning about Object Affordances in a Knowledge Base Representation (2014, methodology): “Similar to this work, StatSnowball [38] and Elementary [26] use Markov Logic Networks [28] as the underlying knowledge representation and perform statistical inference for knowledge base construction.”
Identifying Relations for Open Information Extraction (2011, unknown): “(Banko et al., 2007; Wu and Weld, 2010; Zhu et al., 2009).”
Open Information Extraction: The Second Generation (2011, methodology): “Several Open IE systems have been proposed before now, including TEXTRUNNER [Banko et al., 2007], WOE [Wu and Weld, 2010], and StatSnowBall [Zhu et al., 2009].”
Building a Large-scale Multimodal Knowledge Base System for Answering Visual Queries (2015, background): “Existing text-based KB representations [15, 38, 58] fail to incorporate continuous visual features in a probabilistic framework, which hinders us from expressing richer multimodal data.”
Quantifying Similarity between Relations with Fact Distribution (2019, background): “…and their facts, including open information extraction (Brin, 1998; Agichtein and Gravano, 2000; Ravichandran and Hovy, 2002; Banko et al., 2007; Zhu et al., 2009; Etzioni et al., 2011; Saha et al., 2017) and relation extraction (Riedel et al., 2013; Liu et al., 2013; Zeng et al., 2014; Santos…”