Going out on a limb: Joint Extraction of Entity Mentions and Relations without Dependency Trees

By Arzoo Katiyar, Claire Cardie. 2017

244 citations recorded by the source corpus.

We present a novel attention-based recurrent neural network for joint extraction of entity mentions and relations. We show that attention along with long short term memory (LSTM) network can extract semantic relations between entity mentions without having access to dependency trees. Experiments on Automatic Content Extraction (ACE) corpora show that our model significantly outperforms feature-based joint model by Li and Ji (2014). We also compare our model with an end-to-end tree-based LSTM model (SPTree) by Miwa and Bansal (2016) and show that our model performs within 1% on entity mentions and 2% on relations. Our fine-grained analysis also shows that our model performs significantly better on Agent-Artifact relations, while SPTree performs better on Physical and Part-Whole relations.

Subfields: Artificial Intelligence

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Citations in the corpus

A Hierarchical Multi-task Approach for Learning Embeddings from Semantic Tasks (2018, methodology): “2017) and other previous work (Katiyar and Cardie 2017; Bekoulis et al. 2018; Augenstein, Ruder, and Søgaard 2018), we do not learn label embeddings, meaning that the (supervised) output/prediction of a layer is not directly fed to the following layer through an embedding learned during training.”
Entity-Relation Extraction as Multi-Turn Question Answering (2019, methodology): “For evaluation, we report micro-F1 scores, precision and re-call on entities and relations (Tables 7) as in Li and Ji (2014); Miwa and Bansal (2016); Katiyar and Cardie (2017); Zhang et al. (2017).”
A Novel Cascade Binary Tagging Framework for Relational Triple Extraction (2019, background): “…is regarded as correct only if the relation and the heads of both subject and object are all correct (Li and Ji, 2014; Miwa and Bansal, 2016; Katiyar and Cardie, 2017; Zheng et al., 2017; Zeng et al., 2018; Takanobu These two versions have been selectively adopted and preprocessed in many…”
Joint entity recognition and relation extraction as a multi-head selection problem (2018, methodology): “Unlike previous work on joint models (Katiyar & Cardie, 2017), we are able to predict multiple relations considering the classes as independent and not mutually exclusive (the probabilities do not necessarily sum to 1 for different classes).”
A Frustratingly Easy Approach for Entity and Relation Extraction (2021, methodology): “…as well as links to previous entities, Zhang et al. (2017a); Wang and Lu (2020) adopt a table-ﬁlling approach proposed in (Miwa and Sasaki, 2014); Katiyar and Cardie (2017) and Zheng et al. (2017) employ sequence tagging-based approaches; Sun et al. (2019) and Fu et al. (2019) propose…”
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “Katiyar and Cardie [167] proposed a joint extraction framework with an attention-based LSTM network.”
A Joint Neural Model for Information Extraction with Global Features (2020, methodology): “Some recent efforts develop joint neural models to perform extraction of two IE subtasks, such as entity and relation extraction (Zheng et al., 2017; Katiyar and Cardie, 2017; Bekoulis et al., 2018; Fu et al., 2019; Luan et al., 2019; Sun et al., 2019) and event and temporal relation extraction (Han”
Scene Graph Parsing as Dependency Parsing (2018, methodology): “More broadly, our task also relates to entity and relation extraction, e.g. Katiyar and Cardie (2017), but there object attributes are not handled.”