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

Paramita paramita135 at gmail.com
Fri Jan 6 11:21:59 CET 2017


Hi Marco,

This paper by Radinsky et al. "Learning Causality for News Events Prediction" (http://dl.acm.org/citation.cfm?id=2187958) is probably what you're looking for. They started with mining causal relations between specific events from news, but then applied a generalization method to obtain causality between more general concepts to be able to predict future events.

In terms of resources, if you're looking more into commonsense concepts, ConceptNet (http://conceptnet5.media.mit.edu/) currently contains causation knowledge under the relations: Causes, CausesDesire and MotivatedByGoal. For example, 'virus' can be seen to cause 'disease' and 'infection' there ( http://conceptnet5.media.mit.edu/web/c/en/virus).

Best, Paramita

On Thu, Jan 5, 2017 at 8:12 PM, <corpora-request at uib.no> wrote:


>
> Date: Thu, 5 Jan 2017 19:59:22 +0100
> From: Marco Baroni <marco.baroni at unitn.it>
> Subject: Re: [Corpora-List] methods to identify the direction of
> causation
> To: Ken Litkowski <ken at clres.com>
> Cc: corpora at uib.no
>
> Thanks for the further advice.
>
> To clarify, what I'm looking for is papers that look specifically at the
> relation between out-of-context "types", not sentence-based "instances".
>
> For example, Mirza and Tonelli paper classify "compliance"-"inspections" as
> a causal pair, because in a particular sentence *complying* to some
> regulations caused the *inspections*.
>
> While this is arguably the more interesting case (finding what causes what
> in a specific situation), we are right now working on a model that only
> looks at prototypical relations between concepts, independently from
> specific situations, as in the "virus"/"death" example, where, if you ask
> subjects out-of-context, they will tell you that it's the virus that causes
> death and not vice versa, even if you can think of specific situations
> where, say, it's a death that caused the spreading of a virus, etc.
>
> Thanks again.
>
> Best,
>
> Marco
>
>
>
> On Thu, Jan 5, 2017 at 7:14 PM, Ken Litkowski <ken at clres.com> wrote:
>
> > 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> 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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> >>
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> >>
> >
> >
> > --
> > Francis Bond <http://www3.ntu.edu.sg/home/fcbond/>
> > Division of Linguistics and Multilingual Studies
> > Nanyang Technological University
> >
> >
> > _______________________________________________
> > UNSUBSCRIBE from this page: http://mailman.uib.no/options/corpora
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> >
> >
> > --
> > Ken Litkowski TEL.: 301-482-0237 <(301)%20482-0237>
> > 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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>
>
> --
> Marco Baroni
> Center for Mind/Brain Sciences (CIMeC)
> University of Trento
> http://clic.cimec.unitn.it/marco
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