A Maximum Entropy-Based Bio-Molecular Event Extraction Model that Considers Event Generation

2014 ◽  
Keyword(s):  
Maximum Entropy ◽  
Event Extraction ◽  
Molecular Event ◽  
Event Generation ◽  
Extraction Model

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 BIO-MOLECULAR EVENT EXTRACTION WITH MARKOV LOGIC

2011 ◽  
Vol 27 (4) ◽  
pp. 558-582 ◽  
Author(s):  
Sebastian Riedel ◽  
Rune Saetre ◽  
Hong-Woo Chun ◽  
Toshihisa Takagi ◽  
Jun’ichi Tsujii
Keyword(s):  
Event Extraction ◽  
Molecular Event ◽  
Markov Logic
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