25,793 research outputs found
Latent Space Model for Multi-Modal Social Data
With the emergence of social networking services, researchers enjoy the
increasing availability of large-scale heterogenous datasets capturing online
user interactions and behaviors. Traditional analysis of techno-social systems
data has focused mainly on describing either the dynamics of social
interactions, or the attributes and behaviors of the users. However,
overwhelming empirical evidence suggests that the two dimensions affect one
another, and therefore they should be jointly modeled and analyzed in a
multi-modal framework. The benefits of such an approach include the ability to
build better predictive models, leveraging social network information as well
as user behavioral signals. To this purpose, here we propose the Constrained
Latent Space Model (CLSM), a generalized framework that combines Mixed
Membership Stochastic Blockmodels (MMSB) and Latent Dirichlet Allocation (LDA)
incorporating a constraint that forces the latent space to concurrently
describe the multiple data modalities. We derive an efficient inference
algorithm based on Variational Expectation Maximization that has a
computational cost linear in the size of the network, thus making it feasible
to analyze massive social datasets. We validate the proposed framework on two
problems: prediction of social interactions from user attributes and behaviors,
and behavior prediction exploiting network information. We perform experiments
with a variety of multi-modal social systems, spanning location-based social
networks (Gowalla), social media services (Instagram, Orkut), e-commerce and
review sites (Amazon, Ciao), and finally citation networks (Cora). The results
indicate significant improvement in prediction accuracy over state of the art
methods, and demonstrate the flexibility of the proposed approach for
addressing a variety of different learning problems commonly occurring with
multi-modal social data.Comment: 12 pages, 7 figures, 2 table
Semi-Supervised Learning for Neural Keyphrase Generation
We study the problem of generating keyphrases that summarize the key points
for a given document. While sequence-to-sequence (seq2seq) models have achieved
remarkable performance on this task (Meng et al., 2017), model training often
relies on large amounts of labeled data, which is only applicable to
resource-rich domains. In this paper, we propose semi-supervised keyphrase
generation methods by leveraging both labeled data and large-scale unlabeled
samples for learning. Two strategies are proposed. First, unlabeled documents
are first tagged with synthetic keyphrases obtained from unsupervised keyphrase
extraction methods or a selflearning algorithm, and then combined with labeled
samples for training. Furthermore, we investigate a multi-task learning
framework to jointly learn to generate keyphrases as well as the titles of the
articles. Experimental results show that our semi-supervised learning-based
methods outperform a state-of-the-art model trained with labeled data only.Comment: To appear in EMNLP 2018 (12 pages, 7 figures, 6 tables
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