Joint entity recognition and relation extraction as a multi-head selection problem

Abstract

State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers. Thus, the performance of such joint models depends on the quality of the features obtained from these NLP tools. However, these features are not always accurate for various languages and contexts. In this paper, we propose a joint neural model which performs entity recognition and relation extraction simultaneously, without the need of any manually extracted features or the use of any external tool. Specifically, we model the entity recognition task using a CRF (Conditional Random Fields) layer and the relation extraction task as a multi-head selection problem (i.e., potentially identify multiple relations for each entity). We present an extensive experimental setup, to demonstrate the effectiveness of our method using datasets from various contexts (i.e., news, biomedical, real estate) and languages (i.e., English, Dutch). Our model outperforms the previous neural models that use automatically extracted features, while it performs within a reasonable margin of feature-based neural models, or even beats them.

A Hierarchical Multi-Task Approach for Learning Embeddings from Semantic Tasks

“…To be able to compare our results with previous works (Bekoulis et al 2018;Miwa and Bansal 2016;Katiyar and Cardie 2017) on EMD, we identify the head of the entity mention rather than the whole mention.…”

“…Unlike (Hashimoto et al 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. Nonetheless, sharing embeddings and stacking hierarchical encoders allows us to share the supervision from each task along the full structure of our model and achieve state-of-the-art performance.…”

End-to-End Neural Relation Extraction Using Deep Biaffine Attention

“…End-to-end results: The first six rows in Table 1 compare our results with previous state-of-the-art published results on the same test set. In particular, our model obtains 2+% absolute higher NER and RC scores (Setup 1) than the BiLSTM-CRF-based multihead selection model [5]. We also obtain 7+% higher EC and RC scores (Setup 2) than Adel and Schütze (2017) [1].…”

“…Dataset: We use the benchmark "entity and relation recognition" dataset CoNLL04 from [27]. Following [4,5], we use the 64%/16%/20% training/development/test presplit available from Adel and Schütze (2017) [1], in which the test set was previously also used by Gupta et al (2016) [9]. Implementation: Our model is implemented using DYNET v2.0 [21].…”

Adversarial training for multi-context joint entity and relation extraction

“…We model the relation extraction task as a multi-label head selection problem (Bekoulis et al, 2018b;. In our model, each word w i can be involved in multiple relations with other words.…”