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

Transformer-XL: Attentive Language Models beyond a Fixed ...

⌧+1= Transformer-Layer(q n,kn ⌧+1,v n). where the function SG(·) stands for stop-gradient, the notation [hu hv] indicates the concatenation of two hidden sequences along the length dimen-sion, and W· denotes model parameters. Com-pared to the standard Transformer, the critical dif-ference lies in that the key kn ⌧+1 and value v n ⌧+1

  Transformers

Improving Multimodal Named Entity Recognition via Entity ...

with cross-modal attention mechanism to produce an image-aware word representation and a word-aware visual representation for each input word, respectively. Finally, to largely eliminate the bias of the visual context, we propose to leverage text-based entity span detection as an auxiliary task, and design a unified neural architecture based on

  Span, Attention

Entity, Relation, and Event Extraction with Contextualized ...

Span enumeration Event propagation Coreference Auxiliary Sentence Sentence É É Sentence Figure 1: Overview of our framework: DYGIE++. Shared span representations are constructed by refin-ing contextualized word embeddings via span graph updates, then passed to scoring functions for three IE tasks. Mitchell,2016;Li and Ji,2014) and neural scor-

  Span

CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset ...

Sentiment analysis is an important research area in Natural Language Processing (NLP). It has wide applications for other NLP tasks, such as opinion mining, dialogue generation, and user behavior analysis. Previous study (Pang et al.,2008;Liu and Zhang,2012) mainly focused on text sentiment analysis and achieved impressive results. However,

  Analysis, Sentiment, Sentiment analysis

Recurrent Attention Network on Memory for Aspect …

tic analysis (Socher et al.,2010) and sentence sen-timent analysis (Socher et al.,2013). (Dong et al., 2014;Nguyen and Shirai,2015) adopted Rec-NN for aspect sentiment classication, by converting the opinion target as the tree root and propagating the sentiment of targets depending on the context and syntactic relationships between them. How-

  Analysis, Sentiment, Mitten, Sen timent analysis

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

BLEU: a Method for Automatic Evaluation of Machine …

tor and a standard (poor) machine translation system using 4 reference translations for each of 127 source sentences. The average precision results are shown in Figure 1. Figure 1: Distinguishing Human from Machine ˘ ˇ ˆ The strong signal differentiating human (high pre-cision) from machine (low precision) is striking.

  Machine, Blue, Precision, Cisions, Pre cision

Dual Graph Convolutional Networks for Aspect-based ...

Aspect-based sentiment analysis is a fine-grained sentiment classification task. Re-cently, graph neural networks over depen-dency trees have been explored to explicitly model connections between aspects and opin-ion words. However, the improvement is lim-ited due to the inaccuracy of the dependency parsing results and the informal expressions

  Analysis, Sentiment, Sentiment analysis

Learning Implicit Sentiment in Aspect-based Sentiment ...

Aspect-based sentiment analysis aims to iden-tify the sentiment polarity of a specific aspect in product reviews. We notice that about 30% of reviews do not contain obvious opinion words, but still convey clear human-aware sen-timent orientation, which is known as implicit sentiment. However, recent neural network-

  Analysis, Sentiment, Mitten, Sentiment analysis, Sen timent

Ruddit: Norms of Offensiveness for English Reddit Comments

sive tweets from the Twitter search API.Park et al. (2018),Wiegand et al.(2019), andDavidson et al. (2019) show that theWaseem and Hovy 2016) dataset exhibits topic bias and author bias due to the employed sampling strategy.Founta et al.(2018) boosted the representation of offensive class in their dataset by analysing the sentiment of the ...

  Sentiment, Twitter

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

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

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

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

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

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

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