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Robustness, replicability and scalability in topic modelling

Publications associées (32)

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The organisation of science: topics, incentives and funding.

Omar Ballester Gonzalez

Ever since the links between the development of new technologies and economic growth became evident, researchers have attempted to study how the creation of knowledge fosters progress. If pushing the frontier of knowledge has an impact on progress and well ...

EPFL2020

Evolution of Topics and Novelty in Science

Orion B Penner

Methods of estimating the similarity between individual publications is an area of long-standing interest in the scientometrics community. Traditional methods have generally relied on references and other metadata, while text mining approaches based on tit ...

INT SOC SCIENTOMETRICS & INFORMETRICS-ISSI2019

Text Similarity in Vector Space Models: A Comparative Study

Kenneth Younge, Omid Shahmirzadi, Adam Lugowski

Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to ...

2018

Explicit Suggestion of Query Terms for News Search using Topic Models and Word Embeddings

Andrei Popescu-Belis, Parvaz Mahdabi

This report presents a study on assisting users in building queries to perform real-time searches in a news and social media monitoring system. The system accepts complex queries, and we assist the user by suggesting related keywords or entities. We do thi ...

Idiap2016

Data Summarization with Social Contexts

Karl Aberer, Tian Guo, Rameez Rahman, Hao Zhuang, Xia Hu

While social data is being widely used in various applications such as sentiment analysis and trend prediction, its sheer size also presents great challenges for storing, sharing and processing such data. These challenges can be addressed by data summariza ...

Assoc Computing Machinery2016

Building Word Embeddings for Solving Natural Language Processing

Rémi Philippe Lebret

Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...

École Polytechnique Fédérale de Lausanne2016

Word Embeddings for Natural Language Processing

Rémi Philippe Lebret

EPFL2016

N-gram-Based Low-Dimensional Representation for Document Classification

Rémi Philippe Lebret, Ronan Collobert

The bag-of-words (BOW) model is the common approach for classifying documents, where words are used as feature for training a classifier. This generally involves a huge number of features. Some techniques, such as Latent Semantic Analysis (LSA) or Latent D ...

2015

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

Sequential Topic Models for Mining Recurrent Activities and their Relationships

Jagannadan Varadarajan

In this thesis, we address the analysis of activities from long term data logs with an emphasis on video recordings. Starting from simple words from video, we progressively build methods to infer higher level scene semantics. The main strategies used to ac ...

EPFL2012