97 citations · 112 across the 4 of their papers we have counts for
8 papers · 1 filter
Wasserstein distances for evaluating cross-lingual embeddings
Georgios Balikas, Ioannis Partalas
Word embeddings are high dimensional vector representations of words that capture their semantic similarity in the vector space. There exist several algorithms for learning such em…
Lexical Bias In Essay Level Prediction
Georgios Balikas
Automatically predicting the level of non-native English speakers given their written essays is an interesting machine learning problem. In this work I present the system "balikasg…
Concurrent Learning of Semantic Relations
Georgios Balikas, Gaël Dias, Rumen Moraliyski +1
Discovering whether words are semantically related and identifying the specific semantic relation that holds between them is of crucial importance for NLP as it is essential for ta…
Cross-lingual Document Retrieval using Regularized Wasserstein Distance
Georgios Balikas, Charlotte Laclau, Ievgen Redko +1
Many information retrieval algorithms rely on the notion of a good distance that allows to efficiently compare objects of different nature. Recently, a new promising metric called…
CAp 2017 challenge: Twitter Named Entity Recognition
Cédric Lopez, Ioannis Partalas, Georgios Balikas +5
The paper describes the CAp 2017 challenge. The challenge concerns the problem of Named Entity Recognition (NER) for tweets written in French. We first present the data preparation…
An empirical study on large scale text classification with skip-gram embeddings
Georgios Balikas, Massih-Reza Amini
We investigate the integration of word embeddings as classification features in the setting of large scale text classification. Such representations have been used in a plethora of…