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Aspect-Category-Opinion-Sentiment Quadruple Extraction ...
sidered the extraction of explicit aspects and opin-ions, while ignored the implicit ones. In fact, prod-uct reviews contain a large amount of implicit as-pects and opinions. Table1summarizes the per-centage of implicit aspects and opinions in the
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Dual Graph Convolutional Networks for Aspect-based ...
aclanthology.orgAspect-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
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset ...
aclanthology.orgSentiment 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,
Recurrent Attention Network on Memory for Aspect …
aclanthology.orgtic 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-
Entity, Relation, and Event Extraction with Contextualized ...
aclanthology.orgSpan 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-
A Joint Neural Model for Information Extraction with ...
aclanthology.orgEntity 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
BLEU: a Method for Automatic Evaluation of Machine …
aclanthology.orgtor 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.
Learning Implicit Sentiment in Aspect-based Sentiment ...
aclanthology.orgAspect-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-
Transformer-XL: Attentive Language Models beyond a Fixed ...
aclanthology.org⌧+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
Improving Multimodal Named Entity Recognition via Entity ...
aclanthology.orgwith 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
Exploring Pre-trained Language Models for Event Extraction ...
aclanthology.org3 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.
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www.apa.orgTerrorism and Responses to Terrorism (START). However, any opin-ions, findings, recommendations, or conclusions in this document are those of the authors and do not necessarily reflect views of the DHS. Correspondence concerning this article should be addressed to Clark McCauley, Department of Psychology, Bryn Mawr College, Bryn Mawr, PA 19010.
RACE, ETHNICITY, CLASS, AND GENDER
www.sagepub.compreadolescent females may begin to voice their opin-ions less in class discussions because a strong female voice is deemed unfeminine. Interventions aimed at changing this process point to the need for instructional strategies that better position young women to join in peer-led discussion groups with confidence and ease.
Reading and Writing Academic Texts
newsmanager.commpartners.comcan also help students understand the text and develop original opin-ions about an idea or ideas in the text. Jiang and Grabe (2007) synthe-size important research findings on graphic organizers and provide several examples of ones that can be used for numerous types of writ-ten texts.
Guide to the Software Engineering - IEEE Computer Society
ieeecs-media.computer.orgThe views and opin-ions expressed in this work do not necessarily reflect those of IEEE. IEEE makes this document available on an “as is” basis and makes no warranty, express or implied, as to the accuracy, capabil-ity, efficiency merchantability, or functioning of this document. In no event will IEEE be liable for any general, consequential,