31.335, Diss: Computational Linguistics; Text/Corpus Linguistics: Ali Hurriyetoglu: ''Extracting Actionable Information from Microtexts''

[The LINGUIST List]

LINGUIST List: Vol-31-335. Thu Jan 23 2020. ISSN: 1069 - 4875.

Subject: 31.335, Diss:  Computational Linguistics; Text/Corpus Linguistics: Ali Hurriyetoglu: ''Extracting Actionable Information from Microtexts''

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Date: Thu, 23 Jan 2020 13:23:34
From: Ali Hürriyetoğlu [ali.hurriyetoglu at gmail.com]
Subject: Extracting Actionable Information from Microtexts

 
Institution: Radboud Universiteit Nijmegen 
Program: Centre for Language Studies 
Dissertation Status: Completed 
Degree Date: 2019 

Author: Ali Hurriyetoglu

Dissertation Title: Extracting Actionable Information from Microtexts 

Dissertation URL:  https://repository.ubn.ru.nl/handle/2066/204517

Linguistic Field(s): Computational Linguistics
                     Text/Corpus Linguistics


Dissertation Director(s):
Antal van den Bosch
Nelleke Oostdijk

Dissertation Abstract:

Microblogs such as Twitter represent a powerful source of information. Part of
this information can be aggregated beyond the level of individual posts. Some
of this aggregated information is referring to events that could or should be
acted upon in the interest of e-governance, public safety, or other levels of
public interest. Moreover, a significant amount of this information, if
aggregated, could complement existing information networks in a non-trivial
way. This dissertation proposes a semi-automatic method for extracting
actionable information that serves this purpose. 

We report three main contributions and a final conclusion that are presented
in a separate chapter of this dissertation. First, we show that predicting
time to event is possible for both in-domain and cross-domain scenarios.
Second, we suggest a method which facilitates the definition of relevance for
an analyst’s context and the use of this definition to analyze new data.
Finally, we propose a method to integrate the machine learning based relevant
information classification method with a rule-based information classification
technique to classify microtexts.




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