Traffic Event Detection as a Slot Filling Problem

09/13/2021
by   Xiangyu Yang, et al.
0

In this paper, we introduce the new problem of extracting fine-grained traffic information from Twitter streams by also making publicly available the two (constructed) traffic-related datasets from Belgium and the Brussels capital region. In particular, we experiment with several models to identify (i) whether a tweet is traffic-related or not, and (ii) in the case that the tweet is traffic-related to identify more fine-grained information regarding the event (e.g., the type of the event, where the event happened). To do so, we frame (i) the problem of identifying whether a tweet is a traffic-related event or not as a text classification subtask, and (ii) the problem of identifying more fine-grained traffic-related information as a slot filling subtask, where fine-grained information (e.g., where an event has happened) is represented as a slot/entity of a particular type. We propose the use of several methods that process the two subtasks either separately or in a joint setting, and we evaluate the effectiveness of the proposed methods for solving the traffic event detection problem. Experimental results indicate that the proposed architectures achieve high performance scores (i.e., more than 95 F_1 score) on the constructed datasets for both of the subtasks (i.e., text classification and slot filling) even in a transfer learning scenario. In addition, by incorporating tweet-level information in each of the tokens comprising the tweet (for the BERT-based model) can lead to a performance improvement for the joint setting.

READ FULL TEXT

page 1

page 2

page 3

page 4

10/01/2019

Type-aware Convolutional Neural Networks for Slot Filling

The slot filling task aims at extracting answers for queries about entit...
04/21/2020

TD-GIN: Token-level Dynamic Graph-Interactive Network for Joint Multiple Intent Detection and Slot Filling

Intent detection and slot filling are two main tasks for building a spok...
07/05/2019

Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model

Intent Detection and Slot Filling are two pillar tasks in Spoken Natural...
09/29/2020

TEST_POSITIVE at W-NUT 2020 Shared Task-3: Joint Event Multi-task Learning for Slot Filling in Noisy Text

The competition of extracting COVID-19 events from Twitter is to develop...
10/13/2020

"What Are You Trying to Do?" Semantic Typing of Event Processes

This paper studies a new cognitively motivated semantic typing task, mul...
05/22/2022

TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks

Foodborne illness is a serious but preventable public health problem – w...
09/14/2021

Semantic Answer Type Prediction using BERT: IAI at the ISWC SMART Task 2020

This paper summarizes our participation in the SMART Task of the ISWC 20...