[Corpora-List] methods to identify the direction of causation

Ken Litkowski ken at clres.com
Thu Jan 5 19:14:29 CET 2017


In addition to these good references, there's a paper from COLING 2016, CATENA: Causal and Temporal Relation Extraction from Natural Language Texts <https://www.aclweb.org/anthology/C/C16/C16-1007.pdf>, by Paramita Mirza and Sara Tonelli. This is interesting to me since it makes use of PDEP <http://www.clres.com/db/TPPEditor.html> preposition data to facilitate this extraction. That is, prepositions can be of some help in identifying the direction of causation ("death from virus").

On 1/5/2017 11:21 AM, Francis Bond wrote:
> There is a lot of recent work on a very large scale by NICT in Japan.
>
> Here is a recent summary:
>
> WISDOM X, DISAANA and D-SUMM: Large-scale NLP Systems for Analyzing
> Textual Big Data, Junta Mizuno, Masahiro Tanaka, Kiyonori Ohtake,
> Jong-Hoon Oh, Julien Kloetzer, Chikara Hashimoto and Kentaro Torisawa,
> In the Proceedings of the 26th International Conference on
> Computational Linguistics (COLING 2016) (Demo Track), Osaka, Japan,
> December, 2016.
>
> and there is a lot more here:
>
> http://www2.nict.go.jp/direct/publications-e.html
>
> On Thu, Jan 5, 2017 at 8:04 AM, Xu, Jiajin <ustcxujj at gmail.com
> <mailto:ustcxujj at gmail.com>> wrote:
>
> Dear Marco,
>
> I just fished out the following references on my hard drive, which
> I hope will be of some interest to you. They might be somewhat
> dated though. Also, please excuse me for not editing the
> references in any standard bibliographical style.
>
> 1. Text Mining for Causal Relations Roxana Girju and Dan Moldovan
> <http://dl.acm.org/citation.cfm?id=708596>
> 2. Blanco, E., N. Castell, D. Moldovan. 2008. Causal relation
> extraction.
> 3. Garcia, D. 1997. COATIS, an NLP system to locate expressions
> of actions connected by causality links.
> 4. Girju, R. 2003. Automatic detection of causal relations for
> question answering.
> 5. McNorgan, C., et al. 2007. Feature-feature causal relations
> and statistical co-occurrences in object concepts.
> 6. Suppes, P. 1970. A Probabilistic Theory of Causality.
> Amsterdam: North-Holland Publishing Company.
> 7. Swanson, D. 1991. Migraine and magnesium: Eleven neglected
> connections.
>
> Best,
>
> Jiajin XU
> Ph.D., Professor
> National Research Centre for Foreign Language Education
> Beijing Foreign Studies University
> Beijing 100089
> China
> http://www.bfsu-corpus.org
>
>
> On Thu, Jan 5, 2017 at 8:23 PM, Marco Baroni
> <marco.baroni at unitn.it <mailto:marco.baroni at unitn.it>> wrote:
>
> Dear all,
>
> With a few colleagues, we're working at the problem of
> identifying the prototypical relation of causation between
> words/concepts (e.g., virus causes death, rather than vice-versa).
>
> We are framing this as an out-of-context, concept-level task
> (that is, we're not trying to identify the cause relation as
> it is expressed in specific sentences, but as it holds
> canonically between word pairs).
>
> This is a new area for me, so I'd be grateful for pointers to
> papers that have proposed corpus-based methods to address this
> task.
>
> Thanks in advance,
>
> Marco
>
>
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>
> --
> Francis Bond <http://www3.ntu.edu.sg/home/fcbond/>
> Division of Linguistics and Multilingual Studies
> Nanyang Technological University
>
>
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-- Ken Litkowski TEL.: 301-482-0237 CL Research EMAIL: ken at clres.com 9208 Gue Road Home Page: http://www.clres.com Damascus, MD 20872-1025 USA Blog: http://www.clres.com/blog

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