115 research outputs found
Link Prediction with Mutual Attention for Text-Attributed Networks
In this extended abstract, we present an algorithm that learns a similarity
measure between documents from the network topology of a structured corpus. We
leverage the Scaled Dot-Product Attention, a recently proposed attention
mechanism, to design a mutual attention mechanism between pairs of documents.
To train its parameters, we use the network links as supervision. We provide
preliminary experiment results with a citation dataset on two prediction tasks,
demonstrating the capacity of our model to learn a meaningful textual
similarity.Comment: Added missing referenc
Shallow Text Clustering Does Not Mean Weak Topics: How Topic Identification Can Leverage Bigram Features
DMNLP co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)International audienceText clustering and topic learning are two closely related tasks. In this paper, we show that the topics can be learnt without the absolute need of an exact categorization. In particular, the experiments performed on two real case studies with a vocabulary based on bigram features lead to extracting readable topics that cover most of the documents. Precision at 10 is up to 74% for a dataset of scientific abstracts with 10,000 features, which is 4% less than when using unigrams only but provides more interpretable topics
Global Vectors for Node Representations
Most network embedding algorithms consist in measuring co-occurrences of
nodes via random walks then learning the embeddings using Skip-Gram with
Negative Sampling. While it has proven to be a relevant choice, there are
alternatives, such as GloVe, which has not been investigated yet for network
embedding. Even though SGNS better handles non co-occurrence than GloVe, it has
a worse time-complexity. In this paper, we propose a matrix factorization
approach for network embedding, inspired by GloVe, that better handles non
co-occurrence with a competitive time-complexity. We also show how to extend
this model to deal with networks where nodes are documents, by simultaneously
learning word, node and document representations. Quantitative evaluations show
that our model achieves state-of-the-art performance, while not being so
sensitive to the choice of hyper-parameters. Qualitatively speaking, we show
how our model helps exploring a network of documents by generating
complementary network-oriented and content-oriented keywords.Comment: 2019 ACM World Wide Web Conference (WWW 19
- …