Traffic Event Detection as a Slot Filling Problem
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Abstract (arXiv)
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% in terms of F1 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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 35 of its 92 references that are in the map. First posted on arXiv 13 Sep 2021.
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35 of 92 references.
References · 35
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2014Adam: A Method for Stochastic Optimizationcited by 36,960
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
- 2015Fast R-CNNcited by 2,930
- 2016Enriching Word Vectors with Subword Informationcited by 1,578
- 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,471
- 2014Convolutional Neural Networks for Sentence Classificationcited by 1,124
- 2016Neural Architectures for Named Entity Recognitioncited by 548
- 2019An Evaluation Dataset for Intent Classification and Out-of-Scope Predictioncited by 378
23 more
- 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 260
- 2019BERT for Joint Intent Classification and Slot Fillingcited by 195
- 2016End-to-End Relation Extraction using LSTMs on Sequences and Tree Structurescited by 161
- 2016Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Fillingcited by 137
- 2019BERTje: A Dutch BERT Modelcited by 109
- 2020COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twittercited by 96
- 2020RobBERT: a Dutch RoBERTa-based Language Modelcited by 88
- 2019A Novel Bi-directional Interrelated Model for Joint Intent Detection and Slot Fillingcited by 85
- 2018Joint Slot Filling and Intent Detection via Capsule Neural Networkscited by 73
- 2016A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Taskscited by 62
- 2018Joint entity recognition and relation extraction as a multi-head selection problemcited by 59
- 2015A C-LSTM Neural Network for Text Classificationcited by 54
- 2018Double Embeddings and CNN-based Sequence Labeling for Aspect Extractioncited by 42
- 2020A Review on Fact Extraction and Verificationcited by 29
- 2016Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Fillingcited by 25
- 2018Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapescited by 25
- 2018A deep learning approach for detecting traffic accidents from social media datacited by 12
- 2016Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken Language Understandingcited by 9
- 2016Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resourcecited by 7
- 2016Sequential Convolutional Neural Networks for Slot Filling in Spoken Language Understandingcited by 5
- 2019Sub-event detection from Twitter streams as a sequence labeling problemcited by 5
- 2017An attentive neural architecture for joint segmentation and parsing and its application to real estate adscited by 1
- 2020Cascade Convolutional Neural Network for Image Super-Resolutioncited by 1
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