Search Results for author: David Garcia

Found 6 papers, 5 papers with code

LEXpander: applying colexification networks to automated lexicon expansion

1 code implementation31 May 2022 Anna Di Natale, David Garcia

Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents.

Detecting potentially harmful and protective suicide-related content on twitter: A machine learning approach

2 code implementations9 Dec 2021 Hannah Metzler, Hubert Baginski, Thomas Niederkrotenthaler, David Garcia

The two deep learning models achieved the best performance in two classification tasks: In the first task, we classified six main content categories, including personal stories about either suicidal ideation and attempts or coping, calls for action intending to spread either problem awareness or prevention-related information, reporting of suicide cases, and other tweets irrelevant to these categories.

Improving accuracy and speeding up Document Image Classification through parallel systems

1 code implementation16 Jun 2020 Javier Ferrando, Juan Luis Dominguez, Jordi Torres, Raul Garcia, David Garcia, Daniel Garrido, Jordi Cortada, Mateo Valero

This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions.

Document Classification Document Image Classification +4

A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency

no code implementations28 Mar 2020 Tara N. Sainath, Yanzhang He, Bo Li, Arun Narayanan, Ruoming Pang, Antoine Bruguier, Shuo-Yiin Chang, Wei Li, Raziel Alvarez, Zhifeng Chen, Chung-Cheng Chiu, David Garcia, Alex Gruenstein, Ke Hu, Minho Jin, Anjuli Kannan, Qiao Liang, Ian McGraw, Cal Peyser, Rohit Prabhavalkar, Golan Pundak, David Rybach, Yuan Shangguan, Yash Sheth, Trevor Strohman, Mirko Visontai, Yonghui Wu, Yu Zhang, Ding Zhao

Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.

Women Through the Glass Ceiling: Gender Asymmetries in Wikipedia

1 code implementation19 Jan 2016 Claudia Wagner, Eduardo Graells-Garrido, David Garcia, Filippo Menczer

Contributing to the writing of history has never been as easy as it is today thanks to Wikipedia, a community-created encyclopedia that aims to document the world's knowledge from a neutral point of view.

Social and Information Networks

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