599 citations · 1.2k across the 114 of their papers we have counts for
7 papers · 1 filter
Scalable graph-based individual named entity identification
Sammy Khalife, Michalis Vazirgiannis
Named entity discovery (NED) is an important information retrieval problem that can be decomposed into two sub-problems. The first sub-problem, named entity recognition (NER), aims…
Word Embeddings from Large-Scale Greek Web Content
Stamatis Outsios, Konstantinos Skianis, Polykarpos Meladianos +2
Word embeddings are undoubtedly very useful components in many NLP tasks. In this paper, we present word embeddings and other linguistic resources trained on the largest to date di…
Message Passing Graph Kernels
Giannis Nikolentzos, Michalis Vazirgiannis
Graph kernels have recently emerged as a promising approach for tackling the graph similarity and learning tasks at the same time. In this paper, we propose a general framework for…
Perturb and Combine to Identify Influential Spreaders in Real-World Networks
Antoine J. -P. Tixier, Maria-Evgenia G. Rossi, Fragkiskos D. Malliaros +2
Some of the most effective influential spreader detection algorithms are unstable to small perturbations of the network structure. Inspired by bagging in Machine Learning, we propo…
Orthogonal Matching Pursuit for Text Classification
Konstantinos Skianis, Nikolaos Tziortziotis, Michalis Vazirgiannis
In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential. Although classic regularizers provide sparsity, they fail…
GraKeL: A Graph Kernel Library in Python
Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios +3
The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines. Graph kernels have recently emerged as a promisin…
Unsupervised Abstractive Meeting Summarization with Multi-Sentence Compression and Budgeted Submodular Maximization
Guokan Shang, Wensi Ding, Zekun Zhang +4
We introduce a novel graph-based framework for abstractive meeting speech summarization that is fully unsupervised and does not rely on any annotations. Our work combines the stren…