Adversarial Multitask Learning for Technology Entity Recognition

When reading scholar papers, the first thing researchers want to know is which tasks and processes the papers describe, which materials they use, etc.In this paper, these concepts are referred as technology entities. Technology entity recognition (TER) is the basis for carrying out subsequent high-level technology analysis works, such as technical foresight, technology roadmap, and technological innovation. However, the challenges TER faces are much greater than that of normal named entity recognition (NER). Those challenges include the difficulty in feature extraction, the lack of annotation data, and the differences between different domains. To deal with the first challenge, we use a deep neural network to extract features from text. For the other two challenges, we propose an adversarial multitask learning method. The existing knowledge from a big dataset on a source domain is transferred to implement TER on a target domain with only a small number of labeled samples. The experiments show that the proposed method significantly outperforms comparison systems.