Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information

By Guodong Zhou, Min Zhang, Donghong Ji, Qiaoming Zhu. 2007

221 citations recorded by the source corpus.

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Citations in the corpus

Distant supervision for relation extraction without labeled data (2009, methodology): “…a wide variety of lexical, syntactic, and semantic features, and use supervised classiﬁers to label the relation mention holding between a given pair of entities in a test set sentence, optionally combining relation mentions (Zhou et al., 2005; Zhou et al., 2007; Sur-deanu and Ciaramita, 2007).”
Visual Relationship Detection with Language Priors (2016, methodology): “ual relationship detection, we introduce a new dataset that contains 5000 images with 37;993 relationships. Existing datasets that contain relationships were designed 1 In natural language processing [2,3,4,5], relationships are dened as hsubject - predicate - objecti. In this paper, we dene them as”
Action Genome: Actions As Compositions of Spatio-Temporal Scene Graphs (2019, background): “Scene graphs are a formal representation for image information [36, 43] in a form of a graph, which is widely used in knowledge bases [13, 27, 89].”
CORE: Context-Aware Open Relation Extraction with Factorization Machines (2015, background): “Information such as bag-of-words, part-ofspeech tags, entity types, or parse trees have been integrated into many existing systems (Mintz et al., 2009; Zhang et al., 2012; Takamatsu et al., 2011; Zhou et al., 2007; de Lacalle and Lapata, 2013; Akbik et al., 2012).”
Visual Relationship Detection with Internal and External Linguistic Knowledge Distillation (2017, background): “Relationships are commonly defined as tuples consisting of a subject (subj), predicate (pred) and object (obj) [31, 8, 1].”
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning (2017, methodology): “Therefore, we propose contextsensitive PTK (cPTK) by considering the propagation paths from the root of the tree to the roots of subtrees, which shares similar intuition with the context-sensitive tree kernel (Zhou et al., 2007).”
Relation Extraction: Perspective from Convolutional Neural Networks (2015, methodology): “…Chan and Roth, 2010; Sun et al., 2011; Nguyen and Grishman, 2014) and the kernel-based method (Zelenko et al., 2003; Culotta and Sorensen, 2004; Bunescu and Mooney, 2005a; Bunescu and Mooney, 2005b; Zhang et al., 2006; Zhou et al., 2007; Qian et al., 2008; Nguyen et al., 2009; Sun and Han, 2014).”
Deep learning for extracting protein-protein interactions from biomedical literature (2017, background): “Such information was reported to be useful (Zhou et al., 2007).”
Incremental Joint Extraction of Entity Mentions and Relations (2014, background): “, (Reichartz et al., 2009; Sun et al., 2011; Jiang and Zhai, 2007; Bunescu and Mooney, 2005; Zhao and Grishman, 2005; Culotta and Sorensen, 2004; Zhou et al., 2007; Qian and Zhou, 2010; Qian et al., 2008; Chan and Roth, 2011; Plank and Moschitti, 2013)) have drawn much attention in recent years but ”
StatSnowball: a statistical approach to extracting entity relationships (2009, methodology): “, in supervised learning methods [27, 8, 9, 26] and bootstrapping systems [5, 1].”
Semi-supervised Relation Extraction with Large-scale Word Clustering (2011, background): “…structured sentence representations (sequence, dependency or parse tree) to capture the similarities between different relation instances (Zelenko et al., 2003; Bunescu and Mooney, 2005a; Bunescu and Mooney, 2005b; Zhao and Grishman, 2005; Zhang et al., 2006; Zhou et al., 2007; Qian et al., 2008).”
Combining Neural Networks and Log-linear Models to Improve Relation Extraction (2015, methodology): “…and Zhai, 2007; Chan and Roth, 2010; Sun et al., 2011) and the kernel-based method (Zelenko et al., 2003; Culotta and Sorensen, 2004; Bunescu and Mooney, 2005a; Bunescu and Mooney, 2005b; Zhang et al., 2006; Zhou et al., 2007; Qian et al., 2008; Nguyen et al., 2009; Plank and Moschitti, 2013).”