Extracting Relations with Integrated Information Using Kernel Methods

By Shubin Zhao, Ralph Grishman. Annual Meeting of the Association for Computational Linguistics, 2005

367 citations recorded by the source corpus.

Entity relation detection is a form of information extraction that finds predefined relations between pairs of entities in text. This paper describes a relation detection approach that combines clues from different levels of syntactic processing using kernel methods. Information from three different levels of processing is considered: tokenization, sentence parsing and deep dependency analysis. Each source of information is represented by kernel functions. Then composite kernels are developed to integrate and extend individual kernels so that processing errors occurring at one level can be overcome by information from other levels. We present an evaluation of these methods on the 2004 ACE relation detection task, using Support Vector Machines, and show that each level of syntactic processing contributes useful information for this task. When evaluated on the official test data, our approach produced very competitive ACE value scores. We also compare the SVM with KNN on different kernels.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/6f8f3c79d38ffb64f54adfdcfe2b43dd5b59692a

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Citations in the corpus

Decoding Wikipedia Categories for Knowledge Acquisition (2008, methodology): “The evaluation shows that this approach leads to the extraction of high quality relations, more numerous and more qualitative than what we would get by mining for the same relations in the Wikipedia articles using patterns, as it is commonly done in text-based relation extraction (Hearst, 1992; Berl”
Open Information Extraction Using Wikipedia (2010, background): “Deep features are derived from parse trees with the hope of training better extractors (Zhang et al., 2006; Zhao and Grishman, 2005; Bunescu and Mooney, 2005; Wang, 2008).”
A Multi-Channel Deep Neural Network for Relation Extraction (2020, background): “The problem for parsing tree based systems is that their performance is often hurt by inaccurate chunking or parsing [27].”
Exploiting Shallow Linguistic Information for Relation Extraction from Biomedical Literature (2006, background): “A further extension is proposed by Zhao and Grishman (2005).”
Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information (2007, background): “8 There might be some typing errors for the performance reported in Zhao and Grishman(2005) since P, R and F do not match.”
Towards Understanding Gender Bias in Relation Extraction (2019, methodology): “Early on, learning algorithms for relation extraction models were typically categorized as supervised, including feature-based methods (Kambhatla, 2004; Zhou et al., 2005; Zhao and Grishman, 2005) and kernel-”
Semi-supervised Relation Extraction with Large-scale Word Clustering (2011, methodology): “…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).”
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 ”
Multi-Task Transfer Learning for Weakly-Supervised Relation Extraction (2009, background): “(2005) and Zhao and Grishman (2005) studied various fea-”
More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020, neutral): “Due to the wide use of support vector machines (SVM), kernel-based methods have been widely explored, which design kernel functions for SVM to measure the similarities between relation representations and textual instances (Culotta and Sorensen, 2004; Bunescu and Mooney, 2005; Zhao and Grishman, 200”