Exploring Pre-trained Language Models for Event Extraction ...

3 Extraction Model This section describes our approach to extract events that occur in plain text. We consider event extraction as a two-stage task, which includes trig-ger extraction and argument extraction, and pro-pose a Pre-trained Language Model based Event Extractor (PLMEE). Figure3illustrates the archi-tecture of PLMEE.

  Texts, Extraction

EAST: An Efficient and Accurate Scene Text Detector

Text detection, as a prerequisite of the subsequent pro-cesses, plays a critical role in the whole procedure of tex-tual information extraction and understanding. Previous text detection approaches [2, 33, 12, 7, 48] have already ob-tained promising performances on various benchmarks in this field. The core of text detection is the design of fea-

  Texts, Extraction

A Joint Neural Model for Information Extraction with ...

Entity Extraction aims to identify entity men-tions in text and classify them into pre-defined en-tity types. A mention can be a name, nominal, or pronoun. For example, “Kashmir region” should be recognized as a location (LOC) named entity mention in Figure2. Relation Extraction is the task of assigning a

  Information, Model, Texts, Extraction, Neural, Neural model for information extraction

Vehicle Exhaust Extraction System Design Guide

extremely high temperatures within the hoses and the fan. The text box on the right provides more details. Dyno testing involves running an engine under its own power coupled to a controlled amount of resistance. Here are questions you can ask to determine the exhaust extraction equipment needed for a Dyno testing environment:

  Texts, Exhaust, Extraction, Exhaust extraction

Distant supervision for relation extraction without ...

text (for example, learning that a person is em-ployed by a particular organization, or that a ge-ographic entity is located in a particular region). In supervised approaches, sentences in a cor-pus are first hand-labeled for the presence of en-tities and the relations between them. The NIST Automatic Content Extraction (ACE) RDC 2003

  Texts, Supervision, Extraction

LayoutLM: Pre-training of Text and Layout for Document ...

between text and layout information across scanned document images, which is beneficial for a great number of real-world doc-ument image understanding tasks such as information extraction from scanned documents. Furthermore, we also leverage image features to incorporate words’ visual information into LayoutLM.

  Texts, Extraction

arXiv:2111.12085v1 [cs.CV] 23 Nov 2021

Figure 2. UNICORN is an encoder-decoder framework that jointly supports open-ended text generation and box prediction without task-specific modules. The model first encodes image and text inputs with a ConvNet and a text encoder, respectively. Then, image-text features are fed into a transformer encoder-decoder to predict the target sequence.

  Texts

Patterning of light-extraction nanostructures on sapphire ...

to improve light extraction is to pattern the nanostructures on the sapphire substrates, which also improves the GaN epi-taxial crystal quality [1]. However, it is a challenge to etch a sapphire substrate to the desired shape and depth in the required nanoscale, since sapphire is mechanically strong (e.g. hard) and chemically resistant [2].

  Extraction

USGS Mineral Resources Program Copper—A Metal for the Ages

U.S. Department of the Interior U.S. Geological Survey. Fact Sheet 2009–3031 May 2009. 29. Cu. 63.55. copper [Ar]4s13d10. USGS Mineral Resources Program. Copper—A Metal for the Ages