Search Results for author: Giannis Bekoulis

Found 12 papers, 6 papers with code

Traffic Event Detection as a Slot Filling Problem

no code implementations13 Sep 2021 Xiangyu Yang, Giannis Bekoulis, Nikos Deligiannis

In particular, we experiment with several models to identify (i) whether a tweet is traffic-related or not, and (ii) in the case that the tweet is traffic-related to identify more fine-grained information regarding the event (e. g., the type of the event, where the event happened).

Event Detection Slot Filling +2

Learned Gradient Compression for Distributed Deep Learning

no code implementations16 Mar 2021 Lusine Abrahamyan, Yiming Chen, Giannis Bekoulis, Nikos Deligiannis

In contrast, we advocate that the gradients across the nodes are correlated and propose methods to leverage this inter-node redundancy to improve compression efficiency.

Image Classification Quantization +1

Solving Arithmetic Word Problems by Scoring Equations with Recursive Neural Networks

no code implementations11 Sep 2020 Klim Zaporojets, Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder

Recent works use automatic extraction and ranking of candidate solution equations providing the answer to arithmetic word problems.

Graph Convolutional Neural Networks with Node Transition Probability-based Message Passing and DropNode Regularization

no code implementations28 Aug 2020 Tien Huu Do, Duc Minh Nguyen, Giannis Bekoulis, Adrian Munteanu, Nikos Deligiannis

Among the existing GCNNs, many methods can be viewed as instances of a neural message passing motif; features of nodes are passed around their neighbors, aggregated and transformed to produce better nodes' representations.

Data Augmentation Graph Classification

Zero-Shot Cross-Lingual Transfer with Meta Learning

1 code implementation EMNLP 2020 Farhad Nooralahzadeh, Giannis Bekoulis, Johannes Bjerva, Isabelle Augenstein

We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first.

Language Modelling Meta-Learning +4

Sub-event detection from Twitter streams as a sequence labeling problem

1 code implementation NAACL 2019 Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder

This paper introduces improved methods for sub-event detection in social media streams, by applying neural sequence models not only on the level of individual posts, but also directly on the stream level.

Event Detection

Adversarial training for multi-context joint entity and relation extraction

1 code implementation EMNLP 2018 Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder

Adversarial training (AT) is a regularization method that can be used to improve the robustness of neural network methods by adding small perturbations in the training data.

Joint Entity and Relation Extraction

Joint entity recognition and relation extraction as a multi-head selection problem

6 code implementations20 Apr 2018 Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder

State-of-the-art models for joint entity recognition and relation extraction strongly rely on external natural language processing (NLP) tools such as POS (part-of-speech) taggers and dependency parsers.

POS

An attentive neural architecture for joint segmentation and parsing and its application to real estate ads

1 code implementation27 Sep 2017 Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder

In this work, we propose a new joint model that is able to tackle the two tasks simultaneously and construct the property tree by (i) avoiding the error propagation that would arise from the subtasks one after the other in a pipelined fashion, and (ii) exploiting the interactions between the subtasks.

Dependency Parsing

Reconstructing the house from the ad: Structured prediction on real estate classifieds

1 code implementation EACL 2017 Giannis Bekoulis, Johannes Deleu, Thomas Demeester, Chris Develder

In this paper, we address the (to the best of our knowledge) new problem of extracting a structured description of real estate properties from their natural language descriptions in classifieds.

Dependency Parsing Named Entity Recognition +1

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