[eas_cs_seminars] 29th November 2016

[Luca Rossi]

Dear all,

This is just a reminder of tomorrow's talk from Yulan titled "Unsupervised
Event Extraction and Storyline Generation from Text". The talk will be held
from 2pm to 3pm in MB404A.

Please let me know by the end of this week if you intend to give a talk on
13/12 or if you would like to invite an external speaker.

Best,
Luca

On 22 November 2016 at 10:16, Luca Rossi <l.rossi at aston.ac.uk> wrote:

> Dear all,
>
> Next Tuesday (29/11) Dr. Yulan He will give a talk during our cs seminar
> series. The talk will be held as usual in MB404A from 2pm to 3pm (
> https://cs.aston.ac.uk/seminars/)
>
> ===
>
> Title: Unsupervised Event Extraction and Storyline Generation from Text
>
> Abstract: This talk consists of two parts. In the first part, I will
> present our proposed Latent Event and Categorisation Model (LECM) which is
> an unsupervised Bayesian model for the extraction of structured
> representations of events from Twitter without the use of any labelled
> data. The extracted events are automatically clustered into coherence event
> type groups. The proposed framework has been evaluated on over 60 millions
> tweets and has achieved a precision of 70%, outperforming the
> state-of-the-art open event extraction system by nearly 6%. The LECM model
> has been extended to jointly modelling event extraction and visualisation
> in which each event is modelled as a joint distribution over named
> entities, a date, a location and event-related keywords. Moreover, both
> tweets and event instances are associated with coordinates in the
> visualization space. Experimental results show that the proposed approach
> performs remarkably better than both the state-of-the-art event extraction
> method and a pipeline approach for event extraction and visualization.
>
> In the second part of my talk, I will present a non-parametric generative
> model to extract structured representations and evolution patterns of
> storylines simultaneously. In the model, each storyline is modelled as a
> joint distribution over some locations, organizations, persons, keywords
> and a set of topics. We further combine this model with the Chinese
> restaurant process so that the number of storylines can be determined
> automatically without human intervention. The proposed model has been
> evaluated on three news corpora and the experimental results show that it
> generates coherent storylines from new articles.
>
> ===
>
> Best,
> Luca
> --
> Luca Rossi
>
> Lecturer in Computer Science
> School of Engineering and Applied Science
> Aston University
> Web: http://www.cs.aston.ac.uk/~rossil/
> <http://www.cs.bham.ac.uk/~rossil/>
>



-- 
Luca Rossi

Lecturer in Computer Science
School of Engineering and Applied Science
Aston University
Web: http://www.cs.aston.ac.uk/~rossil/ <http://www.cs.bham.ac.uk/~rossil/>
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