Joint extraction refers to extracting triples, composed of entities and relations, simultaneously from the text with a single model , but the existing methods rarely work well on sentences with overlapping issue, i.e., the same entity is included in multiple triples. In this paper, we propose a novel  Bidirectional Tree Tagging (BiTT) scheme to label overlapping triples in the text. In a sentence , the triples with the same relation category  are especially represented as two binary trees, each of which is converted into a word-level tags sequence to label each word. Based on our BiTT scheme, we develop an end-to-end classification framework to predict the BiTT tags  . We adopt the Bi-LSTM layers and a pre-trained BERT encoder respectively as its encoder module , and obtain promising results in a public English dataset as well as a Chinese one.The source code is publicly available at URL
Joint extraction refers to extracting triples, composed of entities and relations, simultaneously from the text with a single model . However, most existing methods fail to extract all triples accurately and efficiently from sentences with overlapping issue, i.e., the same entity is included in multiple triples. In this paper, we propose a novel scheme called Bidirectional Tree Tagging (BiTT) to label overlapping triples in text. In BiTT , the triples with the same relation category in a sentence are especially represented as two binary trees, each of which is converted into a word-level tags sequence to label each word. Based on BiTT scheme, we develop an end-to-end extraction framework to predict the BiTT tags and further extract triples efficiently . We adopt the Bi-LSTM and the BERT as the encoder in our framework respectively , and obtain promising results in public English as well as Chinese datasets.
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a public English dataset
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a Chinese one.The source code is publicly available at URL
Chinese datasets.