Publications related to Word embedding

Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis

This paper introduces a model of multiple-instance learning applied to the prediction of aspect ratings or judgments of specific properties of an item from user-contributed texts such as product reviews. Each variable-length text is represented by several ...

2014

Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis

Andrei Popescu-Belis, Nikolaos Pappas

2014

Multimodal Reranking of Content-based Recommendations for Hyperlinking Video Snippets

Andrei Popescu-Belis, Nikolaos Pappas, Maryam Habibi

In this paper, we present an approach for topic-level search and hyperlinking of video snippets, which relies on contentbased recommendation and multimodal re-ranking techniques. We identify topic-level segments using transcripts or subtitles and enrich th ...

2014

Feature Mapping of Multiple Beamformed Sources for Robust Overlapping Speech Recognition Using a Microphone Array

Hervé Bourlard, John David Scott Dines, Weifeng Li

This paper introduces a non-linear vector-based feature mapping approach to extract robust features for au- tomatic speech recognition (ASR) of overlapping speech using a microphone array. We explore different configurations and additional sources of infor ...

Idiap2014

Classification of selectively constrained DNA elements using feature vectors and rule-based classifiers

Philipp Bucher, Slavica Dimitrieva Janeva

Scarce work has been done in the analysis of the composition of conserved non-coding elements (CNEs) that are identified by comparisons of two or more genomes and are found to exist in all metazoan genomes. Here we present the analysis of CNEs with a metho ...

Academic Press Inc Elsevier Science2014

Learning robotic eye–arm–hand coordination from human demonstration: a coupled dynamical systems approach

Aude Billard, Luka Lukic, José Santos-Victor

We investigate the role of obstacle avoidance in visually guided reaching and grasping movements. We report on a human study in which subjects performed prehensile motion with obstacle avoidance where the position of the obstacle was systematically varied ...

Springer2014

Word Embeddings through Hellinger PCA

Rémi Philippe Lebret, Ronan Collobert

Word embeddings resulting from neural lan- guage models have been shown to be successful for a large variety of NLP tasks. However, such architecture might be difficult to train and time-consuming. Instead, we propose to drastically simplify the word embed ...

Idiap2013

Is Deep Learning Really Necessary for Word Embeddings?

Rémi Philippe Lebret, Ronan Collobert, Joël Yvon Roland Legrand

Word embeddings resulting from neural language models have been shown to be successful for a large variety of NLP tasks. However, such architecture might be difficult to train and time-consuming. Instead, we propose to drastically sim- plify the word embed ...

Idiap2013

Combining Content with User Preferences for TED Lecture Recommendation

Andrei Popescu-Belis, Nikolaos Pappas

This paper introduces a new dataset and compares several methods for the recommendation of non-fiction audio-visual material, namely lectures from the TED website. The TED dataset contains 1,149 talks and 69,023 user profiles, who have made more than 100,0 ...

IEEE2013

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Word embedding

Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis

Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis

Multimodal Reranking of Content-based Recommendations for Hyperlinking Video Snippets

Feature Mapping of Multiple Beamformed Sources for Robust Overlapping Speech Recognition Using a Microphone Array

Classification of selectively constrained DNA elements using feature vectors and rule-based classifiers

Articulatory Feature based Continuous Speech Recognition using Probabilistic Lexical Modeling

Learning robotic eye–arm–hand coordination from human demonstration: a coupled dynamical systems approach

Word Embeddings through Hellinger PCA

Is Deep Learning Really Necessary for Word Embeddings?

Combining Content with User Preferences for TED Lecture Recommendation

Graph Chatbot

Chat with Graph Search

Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis

Explaining the Stars: Weighted Multiple-Instance Learning for Aspect-Based Sentiment Analysis

Articulatory Feature based Continuous Speech Recognition using Probabilistic Lexical Modeling

Combining Content with User Preferences for TED Lecture Recommendation

Multimodal Reranking of Content-based Recommendations for Hyperlinking Video Snippets

Learning robotic eye–arm–hand coordination from human demonstration: a coupled dynamical systems approach

Classification of selectively constrained DNA elements using feature vectors and rule-based classifiers

Word Embeddings through Hellinger PCA

Feature Mapping of Multiple Beamformed Sources for Robust Overlapping Speech Recognition Using a Microphone Array

Is Deep Learning Really Necessary for Word Embeddings?