Multi-label classification (MLC), which assigns multiple labels to each instance, is crucial domains from computer vision text mining. Conventional methods for MLC require huge amounts of labeled data capture complex dependencies between labels. However, such datasets are expensive, or even impossible, acquire. Worse yet, these pre-trained models can only be used the particular label set covere...

In 2015, the United Nations launched SUSTAINABLE DEVELOPMENT GOALS (SDGs) in collaboration with civil society and firms, recognizing that leading firms have potential to innovate bold solutions at scale achieve global sustainability. Exploring impact of SDGs’ launch on through lens normative pressure, we apply computer-aided text analysis language used sustainability reports 164 large corporati...

JEL Classification: L14

As an important part of information extraction, relationship extraction aims to extract the relationships between given entities from natural language text. Based on pretraining model R-BERT, this paper proposes entity method that integrates dependency path and model, which generates a parse tree by parsing, obtains pair via entity, uses exclude such as modifier chunks useless in sentences. Thi...

In this work, we propose a novel approach to extract sentiment-bearing expression features derived from dependency structures. Rather than directly use dependency relations generated by a parser, we propose a set of heuristic rules to detect both explicit and implicit negations in the text. Then, three patterns are defined to support generalized sentiment-bearing expressions. By altering existi...

A central topic in natural language processing is the design of lexical and syntactic features suitable for the target application. In this paper, we study convolution dependency tree kernels for automatic engineering of syntactic and semantic patterns exploiting lexical similarities. We define efficient and powerful kernels for measuring the similarity between dependency structures, whose surf...