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>
Ali Hürriyetoğlu <ali.hurriyetoglu
Extracting Actionable Information from Microtexts E-mail this message to a friend
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
Antal van den Bosch
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