Preemptive Information Extraction using Unrestricted Relation Discovery

By Yusuke Shinyama, Satoshi Sekine. 2006

292 citations recorded by the source corpus.

We are trying to extend the boundary of Information Extraction (IE) systems. Existing IE systems require a lot of time and human effort to tune for a new scenario. Preemptive Information Extraction is an attempt to automatically create all feasible IE systems in advance without human intervention. We propose a technique called Unrestricted Relation Discovery that discovers all possible relations from texts and presents them as tables. We present a preliminary system that obtains reasonably good results.

Subfields: Artificial Intelligence

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Citations in the corpus

Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation (2022, background): “There have been some efforts which focus on relation learning without predefined types, including open RE (Shinyama and Sekine, 2006; Etzioni et al., 2008; Cui et al., 2018; Gao et al., 2020) and continual relation learning (Wang et al.”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, background): “, 2011; Marcheggiani and Titov, 2016), relation clustering (Shinyama and Sekine, 2006; Elsahar et al., 2017; Wu et al., 2019), and data collection (Riloff et al.”
Neural Snowball for Few-Shot Relation Learning (2019, background): “Other work exploits the way to form relation types automatically by clustering semantic patterns of given data (Lin and Pantel, 2001; Shinyama and Sekine, 2006; Yao et al., 2011; Marcheggiani and Titov, 2016; ElSahar et al., 2017).”
Seed-Based Event Trigger Labeling: How far can event descriptions get us? (2015, unknown): “One such IE paradigm, including Pre-emptive IE (Shinyama and Sekine, 2006), On-demand IE (Sekine, 2006; Sekine and Oda, 2007) and Open IE (Etzioni et al., 2005; Banko et al., 2007; Banko et al., 2008), focuses on un-supervised relation and event discovery.”
Open Information Extraction Using Wikipedia (2010, methodology): “An alternative paradigm, Open IE, pioneered by the TextRunner system (Banko et al., 2007) and the “preemptive IE” in (Shinyama and Sekine, 2006), aims to handle an unbounded number of relations and run quickly enough to process Web-scale corpora.”
Distant supervision for relation extraction without labeled data (2009, background): “clusters and simplifies these word strings to produce relation-strings (Shinyama and Sekine, 2006; Banko et al., 2007).”
Filling Knowledge Gaps in Text for Machine Reading (2010, methodology): “Another intermediate point between a collection of domain documents and the general web, reached by restricting processing to the results of a web query, is explored in IE-on-demand (Se-kine 2006; Shinyama and Sekine 2006).”
Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations (2016, background): “, (Shinyama and Sekine, 2006; Yao et al., 2011; Yao et al., 2012; de Lacalle and Lapata, 2013)), including the one introduced in this paper, perform context-sensitive clustering, that is, they treat relations as latent variables and induce them for each entity-pair occurrence individually.”
Relation Extraction Using Distant Supervision (2018, methodology): “Unsupervised relation extraction techniques have also been proposed in the past [60] and further evolved into the subfield of Open Information Extraction ([7], [18], [69]).”
CORE: Context-Aware Open Relation Extraction with Factorization Machines (2015, background): “…KB and surface relations is to cluster the relations: whenever two relations appear in the same cluster, they are treated as synonymous (Hasegawa et al., 2004; Shinyama and Sekine, 2006; Yao et al., 2011; Takamatsu et al., 2011; Min et al., 2012; Akbik et al., 2012; de Lacalle and La-pata, 2013).”
Identifying Relations for Open Information Extraction (2011, unknown): “Other approaches to large-scale IE have included Preemptive IE (Shinyama and Sekine, 2006), OnDemand IE (Sekine, 2006), and weak supervision for IE (Mintz et al.”
Relation Extraction with Matrix Factorization and Universal Schemas (2013, background): “Relational Clustering There is a large body of work aiming to discover latent relations by clustering surface patterns (Hasegawa et al., 2004; Shinyama and Sekine, 2006; Kok and Domingos, 2008; Yao et al., 2011; Takamatsu et al., 2011), or”
Prototypical Representation Learning for Relation Extraction (2021, unknown): “…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; Shinyama & Sekine, 2006; Elsahar et al., 2017; Wu et al., 2019).”
StatSnowball: a statistical approach to extracting entity relationships (2009, background): “Open information extraction (Open IE) [3] is a domain independent extraction paradigm and has been studied in both the natural language document corpus [22] and the Web environment [3] to extract relation tuples.”
Crowdsourcing Inference-Rule Evaluation (2012, background): “Inference rules are an important component in semantic applications, such as Question Answering (QA) (Ravichandran and Hovy, 2002) and Information Extraction (IE) (Shinyama and Sekine, 2006), describing a directional inference relation between two text patterns with variables.”