Event Detection Dataset

Published: 11 July 2020| Version 1 | DOI: 10.17632/7d54rvzxkr.1
Mariano Maisonnave,


The present is a manually labeled data set for the task of Event Detection (ED). The task of ED consists of identifying event triggers, the word that most clearly indicates the occurrence of an event. The present data set consists of 2,200 news extracts from The New York Times (NYT) Annotated Corpus, separated into training (2,000) and testing (200) sets. Each news extract contains the plain text with the labels (event mentions), along with two metadata (publication date and an identifier). Labels description: We consider as event any ongoing real-world event or situation reported in the news articles. It is important to distinguish those events and situations that are in progress (or are reported as fresh events) at the moment the news is delivered from past events that are simply brought back, future events, hypothetical events, or events that will not take place. In our data set we only labeled as event the first type of event. Based on this criterion, some words that are typically considered as events are labeled as non-event triggers if they do not refer to ongoing events at the time the analyzed news is released. Take for instance the following news extract: "devaluation is not a realistic option to the current account deficit since it would only contribute to weakening the credibility of economic policies as it did during the last crisis." The only word that is labeled as event trigger in this example is "deficit" because it is the only ongoing event refereed in the news. Note that the words "devaluation", "weakening" and "crisis" could be labeled as event triggers in other news extracts, where the context of use of these words is different, but not in the given example. Further information: For a more detailed description of the data set and the data collection process please visit: https://cs.uns.edu.ar/~mmaisonnave/resources/ED_data. Data format: The dataset is split in two folders: training and testing. The first folder contains 2,000 XML files. The second folder contains 200 XML files. Each XML file has the following format. <?xml version="1.0" encoding="UTF-8"?> <dataitem> <pubdate>YYYYMMDDTHHMMSS</pubdate> <file-id>...</file-id> <sent-idx>...</sent-idx> <sentence> ... </sentence> </dataitem> The first three tags (pubdate, file-id and sent-idx) contain metadata information. The first one is the publication date of the news article that contained that text extract. The next two tags represent a unique identifier for the text extract. The file-id uniquely identifies a news article, that can hold several text extracts. The second one is the index that identifies that text extract inside the full article. The last tag (sentence) defines the beginning and end of the text extract. Inside that text are the <event></event> tags. Each of these tags surrounds one word that was manually labeled as an event trigger.



Universidad Nacional del Sur, Dalhousie University


Natural Language Processing, Information Extraction