# Modality and Negation in Event Extraction

arXiv: https://arxiv.org/abs/2109.09393 · HTML: https://mlsanity.com/paper/2109.09393/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 54 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [NegBio: a high-performance tool for negation and uncertainty detection in radiology reports](https://mlsanity.com/paper/1712.05898/) (2017)
- [A Survey on Open Information Extraction](https://mlsanity.com/paper/1806.05599/) (2018)
- [Temporal Common Sense Acquisition with Minimal Supervision](https://mlsanity.com/paper/2005.04304/) (2020)
- [Statistical modality tagging from rule-based annotations and crowdsourcing](https://mlsanity.com/paper/1503.01190/) (2015)
- [A Modality Lexicon and its use in Automatic Tagging](https://mlsanity.com/paper/1410.4868/) (2014)
- [Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation](https://mlsanity.com/paper/1906.01834/) (2019)

## Built on by (most cited first, 6 of 6)

- [Smoothing Entailment Graphs with Language Models](https://mlsanity.com/paper/2208.00318/) (2022)
- [An overview of event extraction and its applications](https://mlsanity.com/paper/2111.03212/) (2021)
- [Blindness to Modality Helps Entailment Graph Mining](https://mlsanity.com/paper/2109.10227/) (2021)
- [MIST: a Large-Scale Annotated Resource and Neural Models for Functions of Modal Verbs in English Scientific Text](https://mlsanity.com/paper/2212.07156/) (2022)
- [Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection](https://mlsanity.com/paper/2305.04181/) (2023)
- [Factuality Detection using Machine Translation -- a Use Case for German Clinical Text](https://mlsanity.com/paper/2308.08827/) (2023)

## 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.