Search Results for author:
Found 36 papers, 2 papers with code
Towards Visual Dialog for Radiology
Using MIMIC-CXR, an openly available database of chest X-ray images, we construct both a synthetic and a real-world dataset and provide baseline scores achieved by state-of-the-art models.
NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning
Sentiment analysis (SA) in texts is a well-studied Natural Language Processing task, which in nowadays gains popularity due to the explosion of social media, and the subsequent accumulation of huge amounts of related data.
NTUA-ISLab at SemEval-2019 Task 9: Mining Suggestions in the wild
As online customer forums and product comparison sites increase their societal influence, users are actively expressing their opinions and posting their recommendations on their fellow customers online.
SEQ\^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression
The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.
Neural Activation Semantic Models: Computational lexical semantic models of localized neural activations
Neural activation models have been proposed in the literature that use a set of example words for which fMRI measurements are available in order to find a mapping between word semantics and localized neural activations.
Spoken Dialogue for Information Navigation
Aiming to expand the current research paradigm for training conversational AI agents that can address real-world challenges, we take a step away from traditional slot-filling goal-oriented spoken dialogue systems (SDS) and model the dialogue in a way that allows users to be more expressive in describing their needs.
DeepAlignment: Unsupervised Ontology Matching with Refined Word Vectors
In this work, we present a novel entity alignment method which we dub DeepAlignment.
Tweester at SemEval-2017 Task 4: Fusion of Semantic-Affective and pairwise classification models for sentiment analysis in Twitter
In this paper, we describe our submission to SemEval2017 Task 4: Sentiment Analysis in Twitter.
Feature-Rich Networks for Knowledge Base Completion
We propose jointly modelling Knowledge Bases and aligned text with Feature-Rich Networks.
The SUMMA Platform Prototype
We present the first prototype of the SUMMA Platform: an integrated platform for multilingual media monitoring.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Structured Generative Models of Continuous Features for Word Sense Induction
We propose a structured generative latent variable model that integrates information from multiple contextual representations for Word Sense Induction.
Affective Lexicon Creation for the Greek Language
Starting from the English affective lexicon ANEW (Bradley and Lang, 1999a) we have created the first Greek affective lexicon.
Cognitively Motivated Distributional Representations of Meaning
Although meaning is at the core of human cognition, state-of-the-art distributional semantic models (DSMs) are often agnostic to the findings in the area of semantic cognition.
The SpeDial datasets: datasets for Spoken Dialogue Systems analytics
The SpeDial consortium is sharing two datasets that were used during the SpeDial project.
Crossmodal Network-Based Distributional Semantic Models
Despite the recent success of distributional semantic models (DSMs) in various semantic tasks they remain disconnected with real-world perceptual cues since they typically rely on linguistic features.
Word Semantic Similarity for Morphologically Rich Languages
In this work, we investigate the role of morphology on the performance of semantic similarity for morphologically rich languages, such as German and Greek.
Evaluation of Online Dialogue Policy Learning Techniques
The number of applied Dialogue Systems is ever increasing in several service providing and other applications as a way to efficiently and inexpensively serve large numbers of customers.
SemSim: Resources for Normalized Semantic Similarity Computation Using Lexical Networks
We investigate the creation of corpora from web-harvested data following a scalable approach that has linear query complexity.
Associative and Semantic Features Extracted From Web-Harvested Corpora
We address the problem of automatic classification of associative and semantic relations between words, and particularly those that hold between nouns.
