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

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Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework

Daniel Gatica-Perez

This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine ...

ASSOC COMPUTING MACHINERY2023

Robustness, replicability and scalability in topic modelling

Orion B Penner

Approaches for estimating the similarity between individual publications are an area of long -standing interest in the scientometrics and informetrics communities. Traditional techniques have generally relied on references and other metadata, while text mi ...

ELSEVIER2022

Lightweight Cross-Lingual Sentence Representation Learning

Martin Jaggi, Prakhar Gupta, Zhuoyuan Mao

Large-scale models for learning fixed-dimensional cross-lingual sentence representations like LASER (Artetxe and Schwenk, 2019b) lead to significant improvement in performance on downstream tasks. However, further increases and modifications based on such ...

ASSOC COMPUTATIONAL LINGUISTICS-ACL2021

Multiple Hypothesis Semantic Mapping for Robust Data Association

Roland Siegwart

In this letter, we present a semantic mapping approach with multiple hypothesis tracking for data association. As semantic information has the potential to overcome ambiguity in measurements and place recognition, it forms an eminent modality for autonomou ...

2019

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

Semantic Attribute Matching Networks

Seungryong Kim

We present semantic attribute matching networks (SAM-Net) for jointly establishing correspondences and transferring attributes across semantically similar images, which intelligently weaves the advantages of the two tasks while overcoming their limitations ...

IEEE2019

Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization

Boi Faltings, Diego Matteo Antognini

Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multi-document summarization. Consequently, existing approaches heavily rely on h ...

2019

Message Distortion in Information Cascades

Robert West, Manoel Horta Ribeiro, Kristina Gligoric

Information diffusion is usually modeled as a process in which immutable pieces of information propagate over a network. In reality, however, messages are not immutable, but may be morphed with every step, potentially entailing large cumulative distortions ...

ASSOC COMPUTING MACHINERY2019

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

Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Martin Jaggi, Claudiu-Cristian Musat, Kamil Bennani-Smires

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrases can be used for indexing, searching, aggregating and summarizing text documents, serving many automatic as well as ...

2018