11 research outputs found
Learning Topics and Positions from Debatepedia
We explore Debatepedia, a community-authored encyclopedia of sociopolitical de-bates, as evidence for inferring a low-dimensional, human-interpretable representa-tion in the domain of issues and positions. We introduce a generative model positing latent topics and cross-cutting positions that gives special treatment to person mentions and opin-ion words. We evaluate the resulting repre-sentation’s usefulness in attaching opinionated documents to arguments and its consistency with human judgments about positions.
KHAN: Knowledge-Aware Hierarchical Attention Networks for Accurate Political Stance Prediction
The political stance prediction for news articles has been widely studied to
mitigate the echo chamber effect -- people fall into their thoughts and
reinforce their pre-existing beliefs. The previous works for the political
stance problem focus on (1) identifying political factors that could reflect
the political stance of a news article and (2) capturing those factors
effectively. Despite their empirical successes, they are not sufficiently
justified in terms of how effective their identified factors are in the
political stance prediction. Motivated by this, in this work, we conduct a user
study to investigate important factors in political stance prediction, and
observe that the context and tone of a news article (implicit) and external
knowledge for real-world entities appearing in the article (explicit) are
important in determining its political stance. Based on this observation, we
propose a novel knowledge-aware approach to political stance prediction (KHAN),
employing (1) hierarchical attention networks (HAN) to learn the relationships
among words and sentences in three different levels and (2) knowledge encoding
(KE) to incorporate external knowledge for real-world entities into the process
of political stance prediction. Also, to take into account the subtle and
important difference between opposite political stances, we build two
independent political knowledge graphs (KG) (i.e., KG-lib and KG-con) by
ourselves and learn to fuse the different political knowledge. Through
extensive evaluations on three real-world datasets, we demonstrate the
superiority of DASH in terms of (1) accuracy, (2) efficiency, and (3)
effectiveness.Comment: 12 pages, 5 figures, 10 tables, the Web Conference 2023 (WWW
A conceptual framework for analyzing students' feedback
Ministry of Education, Singapore under its Academic Research Funding Tier
Agreement and Disagreement: Comparison of points of view in the political domain
The automated comparison of points of view between two politicians is a very challenging task, due not only to the lack of annotated resources, but also to the different dimensions participating to the definition of agreement and disagreement. In order to shed light on this complex task, we first carry out a pilot study to manually annotate the components involved in detecting agreement and disagreement. Then, based on these findings, we implement different features to capture them automatically via supervised classification. We do not focus on debates in dialogical form, but we rather consider sets of documents, in which politicians may express their position with respect to different topics in an implicit or explicit way, like during an electoral campaign. We create and make available three different datasets
Argumentation Mining in User-Generated Web Discourse
The goal of argumentation mining, an evolving research field in computational
linguistics, is to design methods capable of analyzing people's argumentation.
In this article, we go beyond the state of the art in several ways. (i) We deal
with actual Web data and take up the challenges given by the variety of
registers, multiple domains, and unrestricted noisy user-generated Web
discourse. (ii) We bridge the gap between normative argumentation theories and
argumentation phenomena encountered in actual data by adapting an argumentation
model tested in an extensive annotation study. (iii) We create a new gold
standard corpus (90k tokens in 340 documents) and experiment with several
machine learning methods to identify argument components. We offer the data,
source codes, and annotation guidelines to the community under free licenses.
Our findings show that argumentation mining in user-generated Web discourse is
a feasible but challenging task.Comment: Cite as: Habernal, I. & Gurevych, I. (2017). Argumentation Mining in
User-Generated Web Discourse. Computational Linguistics 43(1), pp. 125-17
Fine-grained position analysis for political texts
Meinungsanalyse auf politischen Textdaten hat im Bereich der Computerlinguistik in den letzten
Jahren stets an Bedeutung gewonnen. Dabei werden politische Texte zumeist in voneinander
diskrete Klassen unterteilt, wie zum Beispiel pro vs. contra oder links vs. rechts. In den
Politikwissenschaften dagegen werden bei der Analyse von politischen Texten Positionen auf
Skalen mit fließenden Werten abgebildet. Diese feingranulare Darstellung ist für die dort
gegebenen Fragestellungen erforderlich. Das Feld der “quantitativen Analyse” - der automatisierten
Analyse von Texten - die der traditionellen qualitativen Analyse gegenüber steht, hat
erst kürzlich mehr Beachtung gefunden. Bisher werden Texte dabei zumeist lediglich durch
Worthäufigkeiten dargestellt und ohne jegliche Struktur modelliert.
Wir entwickeln in dieser Dissertation Ansätze basierend auf Methoden der Computerlinguistik
und der Informatik, die gegeignet sind, politikwissenschaftliche Forschungsfragen zu untersuchen.
Im Gegensatz zu bisherigen Arbeiten in der Computerlinguistik klassifizieren
wir nicht diskrete Klassen von Meinungen, sondern projizieren feingranulare Positionen auf
fließende Skalen. Darüber hinaus schreiben wir nicht Dokumenten ganzheitlich eine Position
zu, sondern bestimmen die Meinungen zu den jeweiligen Themen, die in den Texten enthalten
sind. Diese mehrdimensionale Meinungsanalyse ist nach unserem Kenntnisstand neu im
Bereich der quantitativen Analyse.
Was unsere Ansätze von anderen Methoden unterscheidet, sind insbesondere folgende zwei
Eigenschaften: Zum Einen nutzen wir Wissen aus externen Quellen, das wir in die Verfahren
einfließen lassen - beispielsweise integrieren wir die Beschreibungen von Ministerien des Bundestags
als Definition von politischen Themenbereichen, mit welchen wir automatisch Themen
in Parteiprogrammen erkennen. Zum Anderen reichern wir unsere Verfahren mit linguistischem
Wissen über Textkomposition und Dialogstruktur an. Somit gelingt uns eine tiefere
Modellierung der Textstruktur.
Anhand der folgenden drei Fragestellungen aus dem Bereich der Politikwissenschaften untersuchen
wir die Umsetzung der oben beschriebenen Methoden:
1. Multi-Dimensionale Positionsanalyse von Parteiprogrammen
2. Analyse von Themen und Positionen in der US-Präsidentschaftswahl
3. Bestimmen von Dove-Hawk-Positionen in Diskussionen der amerikanischen Zentralbank
Wir zeigen, dass die vorgestellten Lösungen erfolreich feingranulare Positionen in den jeweiligen
Daten erkennen und analysieren Möglichkeiten sowie Grenzen dieser zukunftsweisenden
Verfahren