A Neural Model for User Geolocation and Lexical Dialectology

04/13/2017
by   Afshin Rahimi, et al.
0

We propose a simple yet effective text- based user geolocation model based on a neural network with one hidden layer, which achieves state of the art performance over three Twitter benchmark geolocation datasets, in addition to producing word and phrase embeddings in the hidden layer that we show to be useful for detecting dialectal terms. As part of our analysis of dialectal terms, we release DAREDS, a dataset for evaluating dialect term detection methods.

READ FULL TEXT

page 3

page 5

research
11/30/2020

Feature Space Singularity for Out-of-Distribution Detection

Out-of-Distribution (OoD) detection is important for building safe artif...
research
10/28/2019

A Hierarchical Location Prediction Neural Network for Twitter User Geolocation

Accurate estimation of user location is important for many online servic...
research
10/12/2018

A Word-Complexity Lexicon and A Neural Readability Ranking Model for Lexical Simplification

Current lexical simplification approaches rely heavily on heuristics and...
research
05/08/2018

Reasoning with Sarcasm by Reading In-between

Sarcasm is a sophisticated speech act which commonly manifests on social...
research
08/21/2022

DPTNet: A Dual-Path Transformer Architecture for Scene Text Detection

The prosperity of deep learning contributes to the rapid progress in sce...
research
10/07/2020

VCDM: Leveraging Variational Bi-encoding and Deep Contextualized Word Representations for Improved Definition Modeling

In this paper, we tackle the task of definition modeling, where the goal...
research
06/05/2023

Early Rumor Detection Using Neural Hawkes Process with a New Benchmark Dataset

Little attention has been paid on EArly Rumor Detection (EARD), and EARD...

Please sign up or login with your details

Forgot password? Click here to reset