92 research outputs found
A Survey on Graph Representation Learning Methods
Graphs representation learning has been a very active research area in recent
years. The goal of graph representation learning is to generate graph
representation vectors that capture the structure and features of large graphs
accurately. This is especially important because the quality of the graph
representation vectors will affect the performance of these vectors in
downstream tasks such as node classification, link prediction and anomaly
detection. Many techniques are proposed for generating effective graph
representation vectors. Two of the most prevalent categories of graph
representation learning are graph embedding methods without using graph neural
nets (GNN), which we denote as non-GNN based graph embedding methods, and graph
neural nets (GNN) based methods. Non-GNN graph embedding methods are based on
techniques such as random walks, temporal point processes and neural network
learning methods. GNN-based methods, on the other hand, are the application of
deep learning on graph data. In this survey, we provide an overview of these
two categories and cover the current state-of-the-art methods for both static
and dynamic graphs. Finally, we explore some open and ongoing research
directions for future work
Learning to Determine the Quality of News Headlines
Today, most newsreaders read the online version of news articles rather than
traditional paper-based newspapers. Also, news media publishers rely heavily on
the income generated from subscriptions and website visits made by newsreaders.
Thus, online user engagement is a very important issue for online newspapers.
Much effort has been spent on writing interesting headlines to catch the
attention of online users. On the other hand, headlines should not be
misleading (e.g., clickbaits); otherwise, readers would be disappointed when
reading the content. In this paper, we propose four indicators to determine the
quality of published news headlines based on their click count and dwell time,
which are obtained by website log analysis. Then, we use soft target
distribution of the calculated quality indicators to train our proposed deep
learning model which can predict the quality of unpublished news headlines. The
proposed model not only processes the latent features of both headline and body
of the article to predict its headline quality but also considers the semantic
relation between headline and body as well. To evaluate our model, we use a
real dataset from a major Canadian newspaper. Results show our proposed model
outperforms other state-of-the-art NLP models.Comment: 10 Pages, Accepted at the 12th International Conference on Agents and
Artificial Intelligence (ICAART) 202
Unsupervised Emotion Detection from Text using Semantic and Syntactic Relations
Abstract-Emotion detection from text is a relatively new classification task. This paper proposes a novel unsupervised context-based approach to detecting emotion from text at the sentence level. The proposed methodology does not depend on any existing manually crafted affect lexicons such as WordNetAffect, thereby rendering our model flexible enough to classify sentences beyond Ekman's model of six basic emotions. Our method computes an emotion vector for each potential affectbearing word based on the semantic relatedness between words and various emotion concepts. The scores are then fine tuned using the syntactic dependencies within the sentence structure. Extensive evaluation on various data sets shows that our framework is a more generic and practical solution to the emotion classification problem and yields significantly more accurate results than recent unsupervised approaches
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