3,846 research outputs found
"Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection
Automatic fake news detection is a challenging problem in deception
detection, and it has tremendous real-world political and social impacts.
However, statistical approaches to combating fake news has been dramatically
limited by the lack of labeled benchmark datasets. In this paper, we present
liar: a new, publicly available dataset for fake news detection. We collected a
decade-long, 12.8K manually labeled short statements in various contexts from
PolitiFact.com, which provides detailed analysis report and links to source
documents for each case. This dataset can be used for fact-checking research as
well. Notably, this new dataset is an order of magnitude larger than previously
largest public fake news datasets of similar type. Empirically, we investigate
automatic fake news detection based on surface-level linguistic patterns. We
have designed a novel, hybrid convolutional neural network to integrate
meta-data with text. We show that this hybrid approach can improve a text-only
deep learning model.Comment: ACL 201
Negotiation in Multi-Agent Systems
In systems composed of multiple autonomous agents, negotiation is a key form of interaction that enables groups of agents to arrive at a mutual agreement regarding some belief, goal or plan, for example. Particularly because the agents are autonomous and cannot be assumed to be benevolent, agents must influence others to convince them to act in certain ways, and negotiation is thus critical for managing such inter-agent dependencies. The process of negotiation may be of many different forms, such as auctions, protocols in the style of the contract net, and argumentation, but it is unclear just how sophisticated the agents or the protocols for interaction must be for successful negotiation in different contexts. All these issues were raised in the panel session on negotiation
The Royal Birth of 2013: Analysing and Visualising Public Sentiment in the UK Using Twitter
Analysis of information retrieved from microblogging services such as Twitter
can provide valuable insight into public sentiment in a geographic region. This
insight can be enriched by visualising information in its geographic context.
Two underlying approaches for sentiment analysis are dictionary-based and
machine learning. The former is popular for public sentiment analysis, and the
latter has found limited use for aggregating public sentiment from Twitter
data. The research presented in this paper aims to extend the machine learning
approach for aggregating public sentiment. To this end, a framework for
analysing and visualising public sentiment from a Twitter corpus is developed.
A dictionary-based approach and a machine learning approach are implemented
within the framework and compared using one UK case study, namely the royal
birth of 2013. The case study validates the feasibility of the framework for
analysis and rapid visualisation. One observation is that there is good
correlation between the results produced by the popular dictionary-based
approach and the machine learning approach when large volumes of tweets are
analysed. However, for rapid analysis to be possible faster methods need to be
developed using big data techniques and parallel methods.Comment: http://www.blessonv.com/research/publicsentiment/ 9 pages. Submitted
to IEEE BigData 2013: Workshop on Big Humanities, October 201
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Most existing event extraction (EE) methods merely extract event arguments
within the sentence scope. However, such sentence-level EE methods struggle to
handle soaring amounts of documents from emerging applications, such as
finance, legislation, health, etc., where event arguments always scatter across
different sentences, and even multiple such event mentions frequently co-exist
in the same document. To address these challenges, we propose a novel
end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic
graph to fulfill the document-level EE (DEE) effectively. Moreover, we
reformalize a DEE task with the no-trigger-words design to ease the
document-level event labeling. To demonstrate the effectiveness of Doc2EDAG, we
build a large-scale real-world dataset consisting of Chinese financial
announcements with the challenges mentioned above. Extensive experiments with
comprehensive analyses illustrate the superiority of Doc2EDAG over
state-of-the-art methods. Data and codes can be found at
https://github.com/dolphin-zs/Doc2EDAG.Comment: Accepted by EMNLP 201
Implementing the Duty Trip Support Application
We are in the process of developing an agent and ontology-based Duty Trip Support application. The goal of this paper is to consider issues arising when implementing such a system. In addition to the description of our current implementation, which is also critically analyzed, other possible approaches are considered as well.software agents, agent systems, ontologies, transport objects, agent-non-agent integration.
Automatic Article Commenting: the Task and Dataset
Comments of online articles provide extended views and improve user
engagement. Automatically making comments thus become a valuable functionality
for online forums, intelligent chatbots, etc. This paper proposes the new task
of automatic article commenting, and introduces a large-scale Chinese dataset
with millions of real comments and a human-annotated subset characterizing the
comments' varying quality. Incorporating the human bias of comment quality, we
further develop automatic metrics that generalize a broad set of popular
reference-based metrics and exhibit greatly improved correlations with human
evaluations.Comment: ACL2018; with supplements; Dataset link available in the pape
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