This work investigates the use of focussed crawling techniques for the discovery of environmental multimedia Web resources that provide air quality measurements and forecasts. Focussed crawlers automatically navigate the hyperlinked structure of the Web and select the hyperlinks to follow by estimating their relevance to a given topic, based on evidence obtained from the already downloaded page...

In the work here presented, we apply textual and sequential methods to assess the outcomes of an unconstrained multiparty dialogue. In the context of chat transcripts from a collaborative learning scenario, we demonstrate that while low-level textual features can indeed predict student success, models derived from sequential discourse act labels are also predictive, both on their own and as a s...

Abstract Updating one’s beliefs about the causes and effects of climate change is crucial for altering attitudes behaviours. Importantly, metacognitive abilities - insight into (in)correctness beliefs- play a key role in formation polarised beliefs. We here aimed at investigated metacognition changing change. To that end, we focused on domain-general domain-specific updating prior across spectr...

In this paper, we examine institutional trading surrounding corporate news by combining a comprehensive database of news releases for all US firms during 2000 to 2010 with a high-frequency database of institutional trades. We find that institutions trade on the news tone only on the news release day. This news-trading pattern is more pronounced for news related to firm fundamentals, and with a ...

Existing knowledge-based question answering systems often rely on small annotated training data. While shallow methods like relation extraction are robust to data scarcity, they are less expressive than the deep meaning representation methods like semantic parsing, thereby failing at answering questions involving multiple constraints. Here we alleviate this problem by empowering a relation extr...

This paper describes the Emory system for recognizing textual entailment as used for the RTE4 track at the TAC 2008 competition. We use a supervised machine learning approach to train a classifier over a variety of lexical, syntactic, and semantic metrics. We treat the output of each metric as a feature, and train a classifier on the provided data from the previous RTE tracks. As a result, our ...

We present a visual analytic framework for exploring the relationship of textual evidence for computer forensics. Based upon a task analysis study performed with practitioners, our tool addresses the inefficiency of searching for related text documents on a hard drive. Our framework searches both allocated and unallocated sectors for text and performs some pre-analysis processing; this informat...