Exploiting constituent dependencies for tree kernel-based semantic relation extraction

Abstract

This paper proposes a new approach to dynamically determine the tree span for tree kernel-based semantic relation extraction. It exploits constituent dependencies to keep the nodes and their head children along the path connecting the two entities, while removing the noisy information from the syntactic parse tree, eventually leading to a dynamic syntactic parse tree. This paper also explores entity features and their combined features in a unified parse and semantic tree, which integrates both structured syntactic parse information and entity-related semantic information. Evaluation on the ACE RDC 2004 corpus shows that our dynamic syntactic parse tree outperforms all previous tree spans, and the composite kernel combining this tree kernel with a linear state-of-the-art feature-based kernel, achieves the so far best performance.

Incremental Joint Extraction of Entity Mentions and Relations

“…Entity mention extraction (e.g., (Florian et al, 2004;Florian et al, 2006;Florian et al, 2010;Zitouni and Florian, 2008;Ohta et al, 2012)) and relation extraction (e.g., (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) Table 3: 5-fold cross-validation on ACE'04 corpus. Bolded scores indicate highly statistical significant improvement as measured by paired t-test (p < 0.01) usually studied separately.…”

Robust Domain Adaptation for Relation Extraction via Clustering Consistency

“…Kernel-based supervised methods such as dependency tree kernels (Culotta and Sorensen, 2004), subsequence kernels (Bunescu and Mooney, 2006) and convolution tree kernels (Qian et al, 2008) have been rather successful in learning this task. However, purely supervised relation extraction methods assume the availability of sufficient labeled data, which may be costly to obtain for new domains.…”

“…The nodes that represent the argument are also labeled with the entity type, subtype and mention type. We trim the parse tree of a relation instance so that it contains only the most essential tree components based on constituent dependencies (Qian et al, 2008). We also use unigram features and bigram features from a relation instance graph.…”

A Composite Kernel Approach for Detecting Interactive Segments in Chinese Topic Documents

“…To address the issue, several tree-based kernel approaches have been developed. For example, Qian et al [16] defined a set of handcrafted heuristics to identify the informative parts of a constituent parsing tree. The identified sub-trees are then examined by a classification model, which assigns a relation type to the proteins mentioned in a text segment.…”