An Extensible Event Extraction System With Cross-Media Event Resolution

Petroni, Filippo; Raman, Natraj; Nugent, Tim; Nourbakhsh, Armineh; Panić, Žarko; Shah, Sameena; Leidner, Jochen L.

doi:10.1145/3219819.3219827

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…For the detection of unexpected incidents, Sakaki et al use a probabilistic spatio-temporal model to detect earthquake events from Twitter [6], while Radinsky and Horvitz use a keyword dictionary to detect disease outbreaks and terrorist attacks from news [13]. More recently, Petroni et al co-reference both newswire text and social media to extract seven types of critical events [14].…”

Section: Related Workmentioning

confidence: 99%

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One Step Ahead: A Framework for Detecting Unexpected Incidents and Predicting the Stock Markets

Lyu

Zhang

et al. 2021

IEEE Access

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Unexpected incidents can be destructive or even disastrous, affecting the financial markets. Incidents such as the 9/11 attacks (2001), the Fukushima nuclear disaster (2011), and the COVID-19 outbreaks (2019, 2020) severely shocked both local and global markets. For investors, it is crucial to quantify the key facts that affect the incidents' impacts, and to estimate the reactions of the markets accurately and efficiently for global event-driven investment strategies. Though Web data and other alternative data allow such a possibility, it is still very challenging to mine noisy and often biased heterogeneous data sources, and construct a unified framework for modeling global markets across across time and regions. As a first attempt, we build a framework that extracts incident facts globally based on a deep neural network, feeds them into models built on a global event database complemented with novel socioeconomic datasets (e.g. nightlight data from satellites), and predicts stock market directions in a simulated real-world setting with interpretable results that outperform various baselines. Specifically, we study terrorist attacks in three countries for over 20 years on average, as a first effort to systematically quantify the impact on stock markets at a large scale using novel indicators. INDEX TERMS satellite data, stock market prediction, terrorist attacks, unexpected incidents

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Section: Related Workmentioning

confidence: 99%

“…In order to make sense of news data, we start with detecting unexpected incidents. Similar to the work of Petroni et al [14], we first use a list of incident keywords to filter out many irrelevant ones. The keywords are these appearing much more in incident-relevant articles than in regular ones.…”

Section: B Incident Detectionmentioning

confidence: 99%

One Step Ahead: A Framework for Detecting Unexpected Incidents and Predicting the Stock Markets

Lyu

Zhang

et al. 2021

IEEE Access

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“…Petroni et al [28] defined structures for breaking events, including 7 event types like ''Floods'', ''Storms'', ''Fires'' and etc., as well as their ''5W1H'' attributes, so as to extract breaking events from news reports and social media. Yang et al [29] focused on extracting events in the financial domain to help predicting the stock market, investment decision support and etc.…”

Section: A Closed-domain Event Extractionmentioning

confidence: 99%

“…Other than newswire articles, many online social media, such as Twitter and Facebook etc., provide abundant and timely information about diverse types of events. Detecting and extracting events from social media have also been becoming an important task recently [28], [38]- [43]. It is worth noting that as posts in social networks are kind of unofficial texts with lots of abbreviations, misspellings and grammar errors, how to extract events from such online posts faces more challenges than extracting events from news articles.…”

Section: B Open-domain Event Extractionmentioning

confidence: 99%

“…Tweets are mostly published by individual users, and each tweet is with a character limit. So tweets are often with abbreviations, misspellings and grammar errors, which causes many fragmented and noisy text without enough contexts for event extraction [28], [38]- [41]. In contrast to event extraction from newswire, entities, date, location, and keywords are the main components to be extracted from tweets.…”

Section: Event Extraction From Social Mediamentioning

confidence: 99%

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A Survey of Event Extraction From Text

2019

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Numerous important events happen everyday and everywhere but are reported in different media sources with different narrative styles. How to detect whether real-world events have been reported in articles and posts is one of the main tasks of event extraction. Other tasks include extracting event arguments and identifying their roles, as well as clustering and tracking similar events from different texts. As one of the most important research themes in natural language processing and understanding, event extraction has a wide range of applications in diverse domains and has been intensively researched for decades. This article provides a comprehensive yet up-to-date survey for event extraction from text. We not only summarize the task definitions, data sources and performance evaluations for event extraction, but also provide a taxonomy for its solution approaches. In each solution group, we provide detailed analysis for the most representative methods, especially their origins, basics, strengths and weaknesses. Last, we also present our envisions about future research directions. INDEX TERMS Event extraction, event extraction tasks, event corpus, natural language processing.

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Design Event Extraction Model from Amharic Texts Using Deep Learning Approach

Andualem

Tegegne

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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An Extensible Event Extraction System With Cross-Media Event Resolution

Cited by 14 publications

References 23 publications

One Step Ahead: A Framework for Detecting Unexpected Incidents and Predicting the Stock Markets

One Step Ahead: A Framework for Detecting Unexpected Incidents and Predicting the Stock Markets

A Survey of Event Extraction From Text

Design Event Extraction Model from Amharic Texts Using Deep Learning Approach

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