Learning causality for news events prediction

By Kira Radinsky, Sagie Davidovich, Shaul Markovitch. 2012

282 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Stock Price Prediction by Deep Neural Generative Model of News Articles (2018, neutral): “Just as in the previous studies [12], [24], we only extracted the titles from these news articles.”
Sentiment Predictability for Stocks (2017, neutral): “[DZLD15] notes that news titles are actually more useful for prediction than the news content themselves, with the reference [RDM12] showing these experimental results.”
Using Structured Events to Predict Stock Price Movement: An Empirical Investigation (2014, background): “Radinsky et al. (2012) argued that news titles are more helpful for prediction compared to news con-1420 tents, and this paper mainly uses titles.”
Physical Causality of Action Verbs in Grounded Language Understanding (2016, background): “…and Song, 2003) have been studied extensively as well as their linguistic expressions (Wolff, 2003; Song and Wolff, 2003; Neeleman et al., 2012) and automated extraction of causal relations from text (Blanco et al., 2008; Mulkar-Mehta et al., 2011; Radinsky et al., 2012; Riaz and Girju, 2014).”
COD3S: Diverse Generation with Discrete Semantic Signatures (2020, background): “Applications of LSH (Indyk and Motwani, 1998; Charikar, 2002) in NLP began with Ravichandran et al. (2005) who demonstrated its use in fast lexical similarity comparison; later, Van Durme and Lall (2010) showed such hashing could be performed online.”
Guided Generation of Cause and Effect (2020, background): “Causal Knowledge Acquisition Causal knowledge acquisition [Radinsky et al., 2012; Radinsky and Horvitz, 2013] is crucial for many AI systems, and it is often acquired via text.”
Stock Market Prediction with Deep Learning: A Character-based Neural Language Model for Event-based Trading (2017, background): “Following Radinsky et al. (2012) and Ding et al. (2014) we focus on the news headlines instead of the full content of the news articles for prediction since they found it produced better results.”