LINGUIST List 31.335

Thu Jan 23 2020

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

Editor for this issue: Sarah Robinson <srobinsonlinguistlist.org>



Date: 21-Jan-2020
From: Ali Hürriyetoğlu <ali.hurriyetoglugmail.com>
Subject: Extracting Actionable Information from Microtexts
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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:
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.




Page Updated: 23-Jan-2020