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 ...
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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,
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-
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-
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
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
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
Information, Model, Texts, Extraction, Neural, Neural model for information extraction
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.
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.
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Building Machine Learning Systems with Python
totoharyanto.staff.ipb.ac.idChapter 6: Classification II – Sentiment Analysis 117 Sketching our roadmap 117 Fetching the Twitter data 118 Introducing the Naive Bayes classifier 118 Getting to know the Bayes theorem 119 Being naive 120 Using Naive Bayes to classify 121 Accounting for unseen words and other oddities 124 Accounting for arithmetic underflows 125
The use of social media for research and analysis: a ...
assets.publishing.service.gov.ukSentiment analysis – A family of techniques which aim to automatically extract “sentiment” from a piece of text (for example, whether it is positive or negative, or whether the person writing it was angry or excited, etc.). Social media – A means of communication, based …
Powered by Reuters - Thomson Reuters
share.thomsonreuters.comdenied allowing its platformto be used to promote illegalbehaviour. Twitter did notimmediatelyrespond to a requestfor comment.Moscow's TaganskyDistrict Court said Twitter had beenfined 3 million roubles for failing to delete banned content. ANALYSIS Afteranother stunning U.S.stock market year,investors wonder howmuch gas is left
Evaluation Tool - Office of Educational Technology
tech.ed.govLevel of engagement • Intensity of discussion (measured through analysis of strength of sentiment or average time between comments) • Length of threads • Length of website visits • Interest and enjoyment of participants Quality of interactions • Perceptions of relevancy of the experience (e.g., collected through an evaluation survey)
Big Data - Fujitsu
www.fujitsu.comFurthermore, Big Data solutions can add new value by analysing the sentiment contained in the data rather than just looking at the raw information (for example, they can understand how customers are feeling about a particular product). This is known as ‘semantic analysis’. There are also growing developments in artificial
THE CHALLENGES FACING EFFECTIVE COMMUNICATION AS …
profiles.uonbi.ac.kesentiment and to disseminate information to the public successful public relations communication covers the five w’s, who, what, when, where & why. Organizations often use the company website as a tool for communicating with the public, disseminating information about events, new services or other topics of public interest.