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Rehabilitation of Count-based Models for Word Vector Representations

Publications associées (39)

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Similarity Learning Over Large Collaborative Networks

Majid Yazdani

In this thesis, we propose novel solutions to similarity learning problems on collaborative networks. Similarity learning is essential for modeling and predicting the evolution of collaborative networks. In addition, similarity learning is used to perform ...

EPFL2013

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

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

Semantic Vector Machines

Vincent Etter

We first present our work in machine translation, during which we used aligned sentences to train a neural network to embed n-grams of different languages into an d-dimensional space, such that n-grams that are the translation of each other are close with ...

2009

Start making sense: The Chatty Web approach for global semantic agreements

Karl Aberer, Manfred Hauswirth, Philippe Cudré-Mauroux

This paper describes a novel approach for obtaining semantic interoperability among data sources in a bottom-up, semi-automatic manner without relying on pre-existing, global semantic models. We assume that large amounts of data exist that have been organi ...

2003

Thematic Indexing of Spoken Documents by Using Self-Organizing Maps

A method is presented to provide a useful searchable index for spoken audio documents. The task differs from the traditional (text) document indexing, because large audio databases are decoded by automatic speech recognition and decoding errors occur frequ ...

IDIAP2000

Indexing spoken audio by LSA and SOMs

This paper presents an indexing system for spoken audio documents. The framework is indexing and retrieval of broadcast news. The proposed indexing system applies latent semantic analysis (LSA) and self-organizing maps (SOM) to map the documents into a sem ...

IDIAP2000

Indexing spoken audio by LSA and SOMs

2000

Aligning Multilingual Word Embeddings for Cross-Modal Retrieval Task

Karl Aberer, Rémi Philippe Lebret, Alireza Mohammadshahi

In this paper, we propose a new approach to learn multimodal multilingual embeddings for matching images and their relevant captions in two languages. We combine two existing objective functions to make images and captions close in a joint embedding space ...

Association for Computational Linguistics0