FEE: An Event Extraction Model for Flood and Drought Disaster Based on Sequence Labeling

2021 ◽  
Author(s):  
Zulong Ma ◽  
Jiamin Lu ◽  
Wei Wu ◽  
Jun Feng
Keyword(s):  
Event Extraction ◽  
Sequence Labeling ◽  
Drought Disaster ◽  
Extraction Model

scholarly journals Biomedical Event Extraction as Sequence Labeling

2020 ◽  
Author(s):  
Alan Ramponi ◽  
Rob van der Goot ◽  
Rosario Lombardo ◽  
Barbara Plank
Keyword(s):  
Event Extraction ◽  
Sequence Labeling ◽  
Biomedical Event Extraction

scholarly journals Joint Event Extraction Model based on Multi-feature Fusion

2020 ◽  
Vol 174 ◽  
pp. 115-122
Author(s):  
Wang Shuo ◽  
Rao Yuan ◽  
Fan Xiaobing ◽  
Qi Jiangnan
Keyword(s):  
Feature Fusion ◽  
Event Extraction ◽  
Model Based ◽  
Joint Event ◽  
Extraction Model

scholarly journals Event Geoparser with Pseudo-Location Entity Identification and Numerical Argument Extraction Implementation and Evaluation in Indonesian News Domain

2020 ◽  
Vol 9 (12) ◽  
pp. 712
Author(s):  
Agung Dewandaru ◽  
Dwi Hendratmo Widyantoro ◽  
Saiful Akbar
Keyword(s):  
Topic Model ◽  
Event Extraction ◽  
Geographic Information Retrieval ◽  
Unstructured Text ◽  
Three Stages ◽  
Entity Identification ◽  
Choropleth Map ◽  
Extraction Model ◽  
Document Level ◽  
Large Corpus

Geoparser is a fundamental component of a Geographic Information Retrieval (GIR) geoparser, which performs toponym recognition, disambiguation, and geographic coordinate resolution from unstructured text domain. However, geoparsing of news articles which report several events across many place-mentions in the document are not yet adequately handled by regular geoparser, where the scope of resolution is either toponym-level or document-level. The capacity to detect multiple events and geolocate their true coordinates along with their numerical arguments is still missing from modern geoparsers, much less in Indonesian news corpora domain. We propose an event geoparser model with three stages of processing, which tightly integrates event extraction model into geoparsing and provides precise event-level resolution scope. The model casts the geotagging and event extraction as sequence labeling and uses LSTM-CRF inferencer equipped with features derived using Aggregated Topic Model from a large corpus to increase the generalizability. Throughout the proposed workflow and features, the geoparser is able to significantly improve the identification of pseudo-location entities, resulting in a 23.43% increase for weighted F1 score compared to baseline gazetteer and POS Tag features. As a side effect of event extraction, various numerical arguments are also extracted, and the output is easily projected to a rich choropleth map from a single news document.

scholarly journals TDJEE: A Document-Level Joint Model for Financial Event Extraction

Electronics ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 824
Author(s):  
Peng Wang ◽  
Zhenkai Deng ◽  
Ruilong Cui
Keyword(s):  
Event Extraction ◽  
Distant Supervision ◽  
Sentence Level ◽  
Financial Domain ◽  
Level Information ◽  
Financial Events ◽  
Model Training ◽  
Joint Event ◽  
Extraction Model ◽  
Document Level

Extracting financial events from numerous financial announcements is very important for investors to make right decisions. However, it is still challenging that event arguments always scatter in multiple sentences in a financial announcement, while most existing event extraction models only work in sentence-level scenarios. To address this problem, this paper proposes a relation-aware Transformer-based Document-level Joint Event Extraction model (TDJEE), which encodes relations between words into the context and leverages modified Transformer to capture document-level information to fill event arguments. Meanwhile, the absence of labeled data in financial domain could lead models be unstable in extraction results, which is known as the cold start problem. Furthermore, a Fonduer-based knowledge base combined with the distant supervision method is proposed to simplify the event labeling and provide high quality labeled training corpus for model training and evaluating. Experimental results on real-world Chinese financial announcement show that, compared with other models, TDJEE achieves competitive results and can effectively extract event arguments across multiple sentences.

scholarly journals Joint Extraction of Entities and Overlapping Relations Using Position-Attentive Sequence Labeling

2019 ◽  
Vol 33 ◽  
pp. 6300-6308 ◽  
Author(s):  
Dai Dai ◽  
Xinyan Xiao ◽  
Yajuan Lyu ◽  
Shan Dou ◽  
Qiaoqiao She ◽  
...  
Keyword(s):  
Long Range ◽  
State Of The Art ◽  
Relation Extraction ◽  
Attention Mechanism ◽  
Single Model ◽  
Sequence Labeling ◽  
Word Position ◽  
Query Word ◽  
Public Datasets ◽  
Extraction Model

Joint entity and relation extraction is to detect entity and relation using a single model. In this paper, we present a novel unified joint extraction model which directly tags entity and relation labels according to a query word position p, i.e., detecting entity at p, and identifying entities at other positions that have relationship with the former. To this end, we first design a tagging scheme to generate n tag sequences for an n-word sentence. Then a position-attention mechanism is introduced to produce different sentence representations for every query position to model these n tag sequences. In this way, our method can simultaneously extract all entities and their type, as well as all overlapping relations. Experiment results show that our framework performances significantly better on extracting overlapping relations as well as detecting long-range relation, and thus we achieve state-of-the-art performance on two public datasets.

scholarly journals DeepEventMine: end-to-end neural nested event extraction from biomedical texts

2020 ◽  
Vol 36 (19) ◽  
pp. 4910-4917
Author(s):  
Hai-Long Trieu ◽  
Thy Thy Tran ◽  
Khoa N A Duong ◽  
Anh Nguyen ◽  
Makoto Miwa ◽  
...  
Keyword(s):  
Directed Acyclic Graph ◽  
State Of The Art ◽  
Event Extraction ◽  
Supplementary Information ◽  
Supplementary Data ◽  
General Domain ◽  
Acyclic Graph ◽  
End To End ◽  
Biomedical Texts ◽  
Extraction Model

Abstract Motivation Recent neural approaches on event extraction from text mainly focus on flat events in general domain, while there are less attempts to detect nested and overlapping events. These existing systems are built on given entities and they depend on external syntactic tools. Results We propose an end-to-end neural nested event extraction model named DeepEventMine that extracts multiple overlapping directed acyclic graph structures from a raw sentence. On the top of the bidirectional encoder representations from transformers model, our model detects nested entities and triggers, roles, nested events and their modifications in an end-to-end manner without any syntactic tools. Our DeepEventMine model achieves the new state-of-the-art performance on seven biomedical nested event extraction tasks. Even when gold entities are unavailable, our model can detect events from raw text with promising performance. Availability and implementation Our codes and models to reproduce the results are available at: https://github.com/aistairc/DeepEventMine. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Event Geoparser with Pseudo-Location Entity Identification and Numerical Extraction in Indonesian News Corpus

2020 ◽  
Author(s):  
Agung Dewandaru ◽  
Dwi Hendratmo Widyantoro ◽  
Saiful Akbar
Keyword(s):  
Topic Model ◽  
Conditional Random Field ◽  
Event Extraction ◽  
News Story ◽  
Geographic Information Retrieval ◽  
Entity Identification ◽  
News Corpus ◽  
Choropleth Map ◽  
Extraction Model ◽  
Document Level

One of the most important component of a Geographic Information Retrieval (GIR) is the geoparser, which performs toponym recognition, disambiguation, and geographic coordinate resolution from unstructured text domain. However, news articles which report several events across many place references mentioned in the document is not yet adequately modeled by regular geoparser types where the scope of resolution is either on toponym-level or document-level. The capacity to detect multiple events, geolocate its true locations and coordinates along with their numerical arguments are still missing from modern geoparsers, much less in Indonesian news corpora domain. We propose a novel type event geoparser which integrates an ACE-based event extraction model and provides precise event-level scope resolution. The geoparser casts the geotagging and event extraction as sequence labeling and uses Conditional Random Field with keywords feature obtained using Aggregated Topic Model as a semantic exploration from large corpus, which eventually increases the generalizability of the model. The geoparser also use Smallest Administrative Level feature along with Spatial Minimality-derived algorithm to improve the identification of Pseudo-location entities, resulting 19.4% increase for weighted F1 score. As a side effect of event extraction, the geoparser also extracts various numerical arguments and able to generate thematic choropleth map from a single news story.

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