Subsequence Kernels for Relation Extraction

By Razvan C. Bunescu, Raymond J. Mooney. Neural Information Processing Systems, 2005

558 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5be027109ec592032bfeb05f1e11b45306106bd8

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1. Xu Han
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Citations in the corpus

• An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature (2014, methodology): “In addition, one challenge posed by the biological domain is that current systems for parsing do not perform as well on the biomedical narrative as on the newspaper corpora on which they were originally trained [22].”
• Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (2019, background): “Supervised RE models (Ze-lenko et al., 2003; GuoDong et al., 2005; Mooney and Bunescu, 2006) require adequate amounts of annotated data for training which is time-consuming.”
• Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling (2015, methodology): “ designed 16 types of features including POS, WordNet, FrameNet, dependency parse features, etc. Among them, syntactic features are considered to bring significant improvements in extraction accuracy (Bunescu and Mooney, 2005a). Earlier attempts to encode syntactic information are mainly kernel-based”
• More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020, unknown): “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”
• DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction (2018, background): “The goal of relation extraction system is to predict relation between entity pair in a sentence (Zelenko et al., 2003; Bunescu and Mooney, 2005; GuoDong et al., 2005).”
• Recognizing Textual Entailment Using Sentence Similarity based on Dependency Tree Skeletons (2007, methodology): “Since all spine differences SDs are either empty or CCS sequences, we can utilize subsequence kernel methods to represent features implicitly, cf. ( Bunescu and Mooney, 2006 ).”
• Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language (2014, methodology): “Recently, kernels over strings and trees have been effectively applied to a variety of problems in text learning and NLP (Lodhi, Saunders, Shawe-Taylor, Cristianini, & Watkins, 2002; Zelenko, Aone, & Richardella, 2003; Collins, 2002; Bunescu & Mooney, 2005).”
• Deep Learning with Minimal Training Data: TurkuNLP Entry in the BioNLP Shared Task 2016 (2016, methodology): “One popular and proven method for relation extraction is to use three groups of features, based on the observation that the words preceding the first entity, the words between the entities, and those after the second entity serve different roles in deciding whether or not the entities are related (B”
• FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (2018, methodology): “Many works have been proposed for this task, including kernel methods (Zelenko et al., 2002; Mooney and Bunescu, 2006), embedding methods (Gormley et al.”
• A Comprehensive Benchmark of Kernel Methods to Extract Protein–Protein Interactions from Literature (2010, methodology): “Accordingly, precision is often low, but can be improved by additional filtering steps, such as aggregation of single PPI at the corpus level [19], removal of sentences matching certain lexico-syntactic patterns [40], or requiring the occurrence of an additional ‘‘interaction word’’ from a fixed lis”
• Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks (2015, background): “extraction and yield relatively high performance (Bunescu and Mooney, 2006; Zelenko et al., 2003; Zhou et al., 2005).”