@article{Dash_Chowdhury_Gliozzo_Mihindukulasooriya_Fauceglia_2020, title={Hypernym Detection Using Strict Partial Order Networks}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/6263}, DOI={10.1609/aaai.v34i05.6263}, abstractNote={<p>This paper introduces Strict Partial Order Networks (SPON), a novel neural network architecture designed to enforce asymmetry and transitive properties as soft constraints. We apply it to induce hypernymy relations by training with <em>is-a</em> pairs. We also present an <em>augmented</em> variant of SPON that can generalize type information learned for in-vocabulary terms to previously unseen ones. An extensive evaluation over eleven benchmarks across different tasks shows that SPON consistently either outperforms or attains the state of the art on all but one of these benchmarks.</p>}, number={05}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Dash, Sarthak and Chowdhury, Md Faisal Mahbub and Gliozzo, Alfio and Mihindukulasooriya, Nandana and Fauceglia, Nicolas Rodolfo}, year={2020}, month={Apr.}, pages={7626-7633} }