97 citations · 136 across the 12 of their papers we have counts for
13 papers
Bilingual Topic Models for Comparable Corpora
Georgios Balikas, Massih-Reza Amini, Marianne Clausel
Probabilistic topic models like Latent Dirichlet Allocation (LDA) have been previously extended to the bilingual setting. A fundamental modeling assumption in several of these exte…
Query Understanding for Natural Language Enterprise Search
Francisco Borges, Georgios Balikas, Marc Brette +5
Natural Language Search (NLS) extends the capabilities of search engines that perform keyword search allowing users to issue queries in a more "natural" language. The engine tries…
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…
Multitask Learning for Fine-Grained Twitter Sentiment Analysis
Georgios Balikas, Simon Moura, Massih-Reza Amini
Traditional sentiment analysis approaches tackle problems like ternary (3-category) and fine-grained (5-category) classification by learning the tasks separately. We argue that suc…
On the effectiveness of feature set augmentation using clusters of word embeddings
Georgios Balikas, Ioannis Partalas
Word clusters have been empirically shown to offer important performance improvements on various tasks. Despite their importance, their incorporation in the standard pipeline of fe…
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…
TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
Georgios Balikas, Massih-Reza Amini
This paper describes the participation of the team "TwiSE" in the SemEval 2016 challenge. Specifically, we participated in Task 4, namely "Sentiment Analysis in Twitter" for which…
e-Commerce product classification: our participation at cDiscount 2015 challenge
Ioannis Partalas, Georgios Balikas
This report describes our participation in the cDiscount 2015 challenge where the goal was to classify product items in a predefined taxonomy of products. Our best submission yield…
On a Topic Model for Sentences
Georgios Balikas, Massih-Reza Amini, Marianne Clausel
Probabilistic topic models are generative models that describe the content of documents by discovering the latent topics underlying them. However, the structure of the textual inpu…