In order to solve the current problems in domain long text classification tasks, namely, length of a document, which makes it difficult for model capture key information, and lack expert knowledge, leads insufficient accuracy, based on knowledge graph convolutional neural network is proposed. BERT used encode text, each word’s corresponding vector as node so that initialized contains rich seman...

Natural language inference (NLI) in the legal domain is task of predicting entailment between premise, i.e. law, and hypothesis, which a statement regarding issue. Current state-of-the-art approaches to NLI with pre-trained models do not perform well domain, presumably due discrepancy level abstraction premise hypothesis convoluted nature language. Some difficulties specific are that 1) tend be...

In this paper, we describe how to exploit a machine-readable dictionary (MRD) and domain-specific text corpus in supporting the construction of domain on-tologies that specify taxonomic and non-taxonomic relationships among given domain concepts. A) In building taxonomic relationships (hierarchically structure) of domain concepts, some hierarchically structure can be extracted from a MRD with m...

In this paper, we describe how to exploit a machine-readable dictionary (MRD) and domain-specific text corpus in supporting the construction of domain ontologies that specify taxonomic and non-taxonomic relationships among given domain concepts. A) In building taxonomic relationships (hierarchically structure) of domain concepts, some hierarchically structure can be extracted from a MRD with ma...