I am a 4th year PhD student in Computer Science at Columbia University, working under Professor Kathleen McKeown on Natural Language Processing. My research is focused on causal relations and persuasive influence. I am interested in the linguistic variety used in expressing causal relations and how causal relations are framed in argumentative contexts. I am also concerned with the study of persuasive text and how parties in a dialogue generate arguments in context to convince others.

Most of my projects can be found on GitHub or Bitbucket, including my work on automatically labeling causal relations using parallel corpora and identifying the semantic types of argumentative components used in a persuasive discussion forum.

Some of my class projects include detecting causal relations using recursive neural networks and improving detection of sentiment in Twitter using discourse connectives.

I participated in the BeST project for TAC KBP 2016 (detecting target-specific belief and sentiment), where I focused on Spanish sentiment detection.

I am in charge of maintaining the Columbia NLP website.

In a previous life, I worked for the Department of Defense as a cryptanalytic developer.


Christopher Hidey and Kathleen McKeown.
"Persuasive Influence Detection: The Role of Argument Sequencing."
In Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018.
[pdf] [code] [slides]

Christopher Hidey, Elena Musi, Alyssa Hwang, Smaranda Muresan, Kathleen McKeown.
"Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum."
In Proceedings of the 4th Workshop on Argument Mining. EMNLP. 2017.
[pdf] [data]

Christopher Hidey and Kathleen McKeown.
"Identifying Causal Relations Using Parallel Wikipedia Articles."
In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. 2016.
[pdf] [code] [talk]


E-mail: chidey@cs.columbia.edu
Office: 725 CEPSR
Linked In: [christopher-hidey]
Github: [chridey]
Bitbucket: [chridey]