Open Event Extraction from Online Text using a Generative Adversarial Network (D19-1)
Copied to clipboard
| Challenge: | Existing approaches to extract structured representations of open-domain events are limited . a recent study shows that the model outperforms the baseline approaches for extracting events from online texts . |
| Approach: | They propose an event extraction model based on Generative Adversarial Nets which captures latent events with a generator network and a discriminator to distinguish documents reconstructed from latent and original events. |
| Outcome: | The proposed model outperforms baseline models on two Twitter and a news article datasets. |
Similar Papers
Open Domain Event Extraction Using Neural Latent Variable Models (P19-1)
Copied to clipboard
| Challenge: | Existing work on extracting events from news documents focuses on a set of pre-specified event types. |
| Approach: | They propose a latent variable neural model which is scalable to large corpus. |
| Outcome: | The proposed model performs better than the state-of-the-art method for event schema induction. |
Event Extraction from Unstructured Amharic Text (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing information extraction systems for Amharic have not represented the linguistic structure and morphological richness of the languages. |
| Approach: | They propose a system that extracts an event from unstructured Amharic text using supervised machine learning and rule-based approaches. |
| Outcome: | The proposed system outperforms the existing rule-based method on Amharic text. |
Neural Storyline Extraction Model for Storyline Generation from News Articles (N18-1)
Copied to clipboard
| Challenge: | Existing approaches to storyline generation are domain dependent and cannot deal with unseen event types. |
| Approach: | They propose a neural network-based approach to extract structured representations and evolution patterns of storylines without using annotated data. |
| Outcome: | The proposed model outperforms state-of-the-art approaches on accuracy and efficiency on three news corpora and it is based on supervised models. |
Event Detection with Neural Networks: A Rigorous Empirical Evaluation (D18-1)
Copied to clipboard
| Challenge: | Neural network models have been the most successful for event detection, but they ignore syntactic relationships in the text. |
| Approach: | They propose a GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. |
| Outcome: | The proposed model is competitive with existing models on a ACE2005 dataset. |
Exploring Sentence Community for Document-Level Event Extraction (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing approaches to document-level event extraction neglect the complex logic structures in long texts. |
| Approach: | They propose a framework that exploits the relationship between sentences to extract multiple events by sentence community detection using graph attention networks. |
| Outcome: | The proposed framework achieves competitive results over state-of-the-art methods on the large-scale document-level event extraction dataset. |
Event-Driven Learning of Systematic Behaviours in Stock Markets (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Using financial news, we can predict stock market behaviours by extracting financial events from the news and ranking the importance of the events. |
| Approach: | They propose to combine open information extraction and neural co-reference resolution to extract financial events from news streams and extend hierarchical attention networks that include attentions on event, news and temporal levels. |
| Outcome: | The proposed method achieves significantly better accuracies and higher simulated annualized returns than state-of-the-art models when being applied to predicting Standard&Poor 500, Dow Jones, Nasdaq indices and 10 individual stocks. |
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)
Copied to clipboard
| Challenge: | Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks. |
| Approach: | They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm . |
| Outcome: | The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings. |
Text-to-Text Extraction and Verbalization of Biomedical Event Graphs (2022.coling-1)
Copied to clipboard
| Challenge: | Biomedical events represent complex, graphical, and semantically rich interactions expressed in the scientific literature. |
| Approach: | They propose a framework to solve event extraction and event verbalization with a unified text-to-text approach. |
| Outcome: | The proposed framework achieves greater state-of-the-art performance than single-task competitors and can generate coherent natural language utterances from structured data. |
Exploring Pre-trained Language Models for Event Extraction and Generation (P19-1)
Copied to clipboard
| Challenge: | Existing methods to extract event data are laborious to create and limited in size. |
| Approach: | They propose an event extraction model to overcome the roles overlap problem by separating the argument prediction in terms of roles. |
| Outcome: | The proposed method surpasses existing methods on the ACE2005 dataset and improves on the previous methods. |
Event-Centric Natural Language Processing (2021.acl-tutorials)
Copied to clipboard
| Challenge: | This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations. |
| Approach: | This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks. |
| Outcome: | This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks. |