CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases

By Xiang Ren, Zeqiu Wu, Wenqi He, Meng Qu, Clare R. Voss, Heng Ji, Tarek F. Abdelzaher, Jiawei Han. The Web Conference, 2016

320 citations recorded by the source corpus.

Extracting entities and relations for types of interest from text is important for understanding massive text corpora. Traditionally, systems of entity relation extraction have relied on human-annotated corpora for training and adopted an incremental pipeline. Such systems require additional human expertise to be ported to a new domain, and are vulnerable to errors cascading down the pipeline. In this paper, we investigate joint extraction of typed entities and relations with labeled data heuristically obtained from knowledge bases (i.e., distant supervision). As our algorithm for type labeling via distant supervision is context-agnostic, noisy training data poses unique challenges for the task. We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, and jointly embeds entity mentions, relation mentions, text features and type labels into two low-dimensional spaces (for entity and relation mentions respectively), where, in each space, objects whose types are close will also have similar representations. CoType, then using these learned embeddings, estimates the types of test (unlinkable) mentions. We formulate a joint optimization problem to learn embeddings from text corpora and knowledge bases, adopting a novel partial-label loss function for noisy labeled data and introducing an object "translation" function to capture the cross-constraints of entities and relations on each other. Experiments on three public datasets demonstrate the effectiveness of CoType across different domains (e.g., news, biomedical), with an average of 25% improvement in F1 score compared to the next best method.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/060ccf4b9e7c2f4b867ec52c5a5682eb1384e8fb

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Citations in the corpus

Knowledge Graphs (2020, unknown): “A common trend, for example, is to combine interdependent tasks, jointly performing WSD and EL [361], or NER and EL [329, 381], or NER and RE [437, 578], etc.”
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (2019, methodology): “Joint entity recognition and relation extraction models (Yu and Lam, 2010; Li and Ji, 2014; Miwa and Sasaki, 2014; Ren et al., 2017) have been built to take advantage of the close interaction between these two tasks.”
Improving Entity Linking by Modeling Latent Relations between Mentions (2018, background): “…coreference, relying either on off-the-shelf systems or some simple heuristics ( Lazic et al., 2015), and exploit them in a pipeline fashion, though some (e.g., Cheng and Roth (2013); Ren et al. (2017)) additionally exploit a range of syntactic-semantic relations such as apposition and possessives.”
Domain-specific Knowledge Extraction from the Web of Data (2018, background): “In fact, prior studies have shown that distant supervision methods [Ren et al. 2017] which use a curated KG can enhance the accuracy of the fact extraction from textual data.”
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction (2019, methodology): “Wiki-KBP We use the same version as Dai et al. (2019) adopted, where the training set is from (Liu et al., 2017) and the test set is from (Ren et al., 2017).”
Ultra-Fine Entity Typing (2018, methodology): “Fine-grained NER has received growing attention, and is used in many applications (Gupta et al., 2017; Ren et al., 2017; Yaghoobzadeh et al., 2017b; Raiman and Raiman, 2018).”
A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, methodology): “[227] propose a knowledgeenhanced distribution CoType model for joint extraction Task.”