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

Francis Bond bond at ieee.org
Thu Jan 5 17:21:18 CET 2017


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> 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>
> 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 -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: text/html Size: 4691 bytes Desc: not available URL: <https://mailman.uib.no/public/corpora/attachments/20170105/dc8a964e/attachment.txt>



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