In this study, we propose a method designed to extract named entities and relations from unstructured text based on table representations. To extract named entities, the proposed method computes representations for entity mentions and long-range dependencies using contextualized representations without hand-crafted features or complex neural network architectures. To extract relations, it applies a tensor dot product to predict all relation labels simultaneously without considering dependencies among relation labels. These advancements significantly simplify the proposed model and the associated algorithm for the extraction of named entities and relations. Despite its simplicity, the experimental results demonstrate that the proposed approach outperformed the state of the-art methods on multiple datasets. Compared with existing table-filling approaches, the proposed method achieved high performance solely by independently predicting the relation labels. In addition, we found that incorporating dependencies of relation labels into the system obtained little performance gain, indicating the effectiveness and sufficiency of the tensor dot-product mechanism for relation extraction in the proposed architecture. Experimental analyses were also performed to explore the benefits of joint training with named entity recognition in relation extraction in our design. We concluded that joint training with named entity recognition assists relation extraction to improve the span-level representation of entities.

Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations. We propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework. Our encoder is a discriminative neural semantic dependency parser that predicts the latent parse graph of the input sentence. Our decoder is a generative neural model that reconstructs the input sentence conditioned on the latent parse graph. Our model is arc-factored and therefore parsing and learning are both tractable. Experiments show our model achieves significant and consistent improvement over the supervised baseline.

This study introduces a novel approach to the joint extraction of entities and relations by stacking convolutional neural networks (CNNs) on pretrained language models. We adopt table representations to model the entities and relations, casting the entity and relation extraction as a table-labeling problem. Regarding each table as an image and each cell in a table as an image pixel, we apply two-dimensional CNNs to the tables to capture local dependencies and predict the cell labels. The experimental results showed that the performance of the proposed method is comparable to those of current state-of-art systems on the CoNLL04, ACE05, and ADE datasets. Even when freezing pretrained language model parameters, the proposed method showed a stable performance, whereas the compared methods suffered from significant decreases in performance. This observation indicates that the parameters of the pretrained encoder may incorporate dependencies among the entity and relation labels during fine-tuning.

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