Modality and Negation in Event Extraction
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Abstract (arXiv)
Language provides speakers with a rich system of modality for expressing thoughts about events, without being committed to their actual occurrence. Modality is commonly used in the political news domain, where both actual and possible courses of events are discussed. NLP systems struggle with these semantic phenomena, often incorrectly extracting events which did not happen, which can lead to issues in downstream applications. We present an open-domain, lexicon-based event extraction system that captures various types of modality. This information is valuable for Question Answering, Knowledge Graph construction and Fact-checking tasks, and our evaluation shows that the system is sufficiently strong to be used in downstream applications.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 7 of its 54 references that are in the map. First posted on arXiv 20 Sep 2021.
Builds on
7 of 54 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2017NegBio: a high-performance tool for negation and uncertainty detection in radiology reportscited by 79
- 2018A Survey on Open Information Extractioncited by 51
- 2020Temporal Common Sense Acquisition with Minimal Supervisioncited by 38
- 2015Statistical modality tagging from rule-based annotations and crowdsourcingcited by 6
- 2014A Modality Lexicon and its use in Automatic Taggingcited by 4
- 2019Automatic Generation of High Quality CCGbanks for Parser Domain Adaptationcited by 1
Built on by
Citing papers per year: 2021: 2; 2022: 2; 2023: 2.
6 papers.
- 2022Smoothing Entailment Graphs with Language Modelscited by 8
- 2021An overview of event extraction and its applicationscited by 5
- 2021Blindness to Modality Helps Entailment Graph Miningcited by 2
- 2022MIST: a Large-Scale Annotated Resource and Neural Models for Functions of Modal Verbs in English Scientific Textcited by 0
- 2023Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detectioncited by 0
- 2023Factuality Detection using Machine Translation -- a Use Case for German Clinical Textcited by 0
- 2023Factuality Detection using Machine Translation -- a Use Case for German Clinical Text17 Aug 2023
- 2023Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection7 May 2023
- 2022MIST: a Large-Scale Annotated Resource and Neural Models for Functions of Modal Verbs in English Scientific Text14 Dec 2022
- 2022Smoothing Entailment Graphs with Language Models30 Jul 2022
- 2021An overview of event extraction and its applications5 Nov 2021
- 2021Blindness to Modality Helps Entailment Graph Mining21 Sep 2021