3 research outputs found
Contradiction Detection for Rumorous Claims
The utilization of social media material in journalistic workflows is
increasing, demanding automated methods for the identification of mis- and
disinformation. Since textual contradiction across social media posts can be a
signal of rumorousness, we seek to model how claims in Twitter posts are being
textually contradicted. We identify two different contexts in which
contradiction emerges: its broader form can be observed across independently
posted tweets and its more specific form in threaded conversations. We define
how the two scenarios differ in terms of central elements of argumentation:
claims and conversation structure. We design and evaluate models for the two
scenarios uniformly as 3-way Recognizing Textual Entailment tasks in order to
represent claims and conversation structure implicitly in a generic inference
model, while previous studies used explicit or no representation of these
properties. To address noisy text, our classifiers use simple similarity
features derived from the string and part-of-speech level. Corpus statistics
reveal distribution differences for these features in contradictory as opposed
to non-contradictory tweet relations, and the classifiers yield state of the
art performance.Comment: To appear in: Proceedings of Extra-Propositional Aspects of Meaning
(ExProM) in Computational Linguistics, Osaka, Japan, 201
Recognizing Textual Entailment Using a Subsequence Kernel Method
We present a novel approach to recognizing Textual tree descriptions, which are automatically extracted from syntactic dependency trees. These features are then applied in a subsequence-kernel-based classifier to learn whether an entailment relation holds between two texts. Our method makes use of machine learning techniques using a limited data set, no external knowledge bases (e.g. WordNet), and no handcrafted inference rules. We achieve an accuracy of 74.5 % for text pairs in the Information Extraction and Question Answering task, 63.6 % for the RTE-2 test data, and 66.9 % for the RET-3 test data