Classifying temporal relations between events

Chambers, Nathanael; Wang, Shan; Jurafsky, Dan

doi:10.3115/1557769.1557820

Cited by 121 publications

(101 citation statements)

References 3 publications

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“…8 Twitter status messages present both unique challenges and opportunities when compared with news articles. Twitter's noisy text presents serious challenges for NLP tools.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Open domain event extraction from twitter

Ritter

Etzioni

Clark³

2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

468

356

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Tweets are the most up-to-date and inclusive stream of information and commentary on current events, but they are also fragmented and noisy, motivating the need for systems that can extract, aggregate and categorize important events. Previous work on extracting structured representations of events has focused largely on newswire text; Twitter's unique characteristics present new challenges and opportunities for open-domain event extraction. This paper describes TwiCalthe first open-domain event-extraction and categorization system for Twitter. We demonstrate that accurately extracting an open-domain calendar of significant events from Twitter is indeed feasible. In addition, we present a novel approach for discovering important event categories and classifying extracted events based on latent variable models. By leveraging large volumes of unlabeled data, our approach achieves a 14% increase in maximum F1 over a supervised baseline. A continuously updating demonstration of our system can be viewed at http://statuscalendar.com; Our NLP tools are available at http://github.com/aritter/ twitter_nlp.

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“…8 Twitter status messages present both unique challenges and opportunities when compared with news articles. Twitter's noisy text presents serious challenges for NLP tools.…”

Section: Related Workmentioning

confidence: 99%

“…For example in newswire, complex reasoning about relations between events (e.g. before and after ) is often required to accurately relate events to temporal expressions [32,8]. The volume of Tweets is also much larger than the volume of news articles, so redundancy of information can be exploited more easily.…”

Section: Introductionmentioning

confidence: 99%

Open domain event extraction from twitter

Ritter

Etzioni

Clark³

2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

468

356

View full text Add to dashboard Cite

show abstract

“…Mani et al (2006) built a MaxEnt classifier to label the temporal links using training data which were bootstrapped by applying temporal closure. Chambers et al (2007) focused on classifying the temporal relation type of event-event pairs using previously learned event attributes as features. However, both works use a reduced set of temporal relations, obtained by collapsing the relation types that inverse each other into a single type.…”

Section: Related Workmentioning

confidence: 99%

Classifying Temporal Relations with Simple Features

Mirza

Tonelli

2014

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Approaching temporal link labelling as a classification task has already been explored in several works. However, choosing the right feature vectors to build the classification model is still an open issue, especially for event-event classification, whose accuracy is still under 50%. We find that using a simple feature set results in a better performance than using more sophisticated features based on semantic role labelling and deep semantic parsing. We also investigate the impact of extracting new training instances using inverse relations and transitive closure, and gain insight into the impact of this bootstrapping methodology on classifying the full set of TempEval-3 relations.

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“…For example, Mani et al (2006Mani et al ( , 2007 trained a MaxEnt classifier using training data which was bootstrapped by applying temporal closure. Chambers et al (2007) focused on eventevent relations using previously learned event attributes. More recently, DŚouza and Ng (2013) combined hand-coded rules with some semantic and discourse features.…”

Section: Related Workmentioning

confidence: 99%

Multi-lingual and Cross-lingual timeline extraction

Laparra

Agerri

Aldabe

et al. 2017

Knowledge-Based Systems

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In this paper we present an approach to extract ordered timelines of events, their participants, locations and times from a set of multilingual and cross-lingual data sources. Based on the assumption that event-related information can be recovered from different documents written in different languages, we extend the Cross-document Event Ordering task presented at SemEval 2015 by specifying two new tasks for, respectively, Multilingual and Cross-lingual Timeline Extraction. We then develop three deterministic algorithms for timeline extraction based on two main ideas. First, we address implicit temporal relations at document level since explicit time-anchors are too scarce to build a wide coverage timeline extraction system. Second, we leverage several multilingual resources to obtain a single, interoperable, semantic representation of events across documents and across languages. The result is a highly competitive system that strongly outperforms the current state-of-the-art. Nonetheless, further analysis of the results reveals that linking the event mentions with their target entities and time-anchors remains a difficult challenge. The systems, resources and scorers are freely available to facilitate its use and guarantee the reproducibility of results.

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Classifying temporal relations between events

Cited by 121 publications

References 3 publications

Open domain event extraction from twitter

Open domain event extraction from twitter

Classifying Temporal Relations with Simple Features

Multi-lingual and Cross-lingual timeline extraction

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