Corpus Wide Argument Mining—A Working Solution


  • Liat Ein-Dor IBM Research AI
  • Eyal Shnarch IBM Research AI
  • Lena Dankin IBM Research AI
  • Alon Halfon IBM Research AI
  • Benjamin Sznajder IBM Research AI
  • Ariel Gera IBM research AI
  • Carlos Alzate IBM Research AI
  • Martin Gleize IBM Research AI
  • Leshem Choshen IBM Research AI
  • Yufang Hou IBM Research AI
  • Yonatan Bilu IBM Research AI
  • Ranit Aharonov IBM Research AI
  • Noam Slonim IBM Research AI



One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a first end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates.




How to Cite

Ein-Dor, L., Shnarch, E., Dankin, L., Halfon, A., Sznajder, B., Gera, A., Alzate, C., Gleize, M., Choshen, L., Hou, Y., Bilu, Y., Aharonov, R., & Slonim, N. (2020). Corpus Wide Argument Mining—A Working Solution. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 7683-7691.



AAAI Technical Track: Natural Language Processing