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Works267 from public data
- 1,345
- Lexical Normalisation of Short Text Messages: Makn Sens a #twitter583
This paper targets out-of-vocabulary words in short text messages and proposes a method for identifying and normalising ill-formed words, which achieves state- of-the-art performance over an SMS corpus and a novel dataset based on Twitter.
- DeepInception: Hypnotize Large Language Model to Be Jailbreaker438
A lightweight method to take advantage of the LLMs' personification capabilities to construct $\textit{a virtual, nested scene}$, allowing it to realize an adaptive way to escape the usage control in a normal scenario.
- Text-Based Twitter User Geolocation Prediction323
This paper presents an integrated geolocation prediction framework, and evaluates the impact of nongeotagged tweets, language, and user-declared metadata on geolocated prediction, and discusses how users differ in terms of their geolocatability.
- Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning298
This paper introduces an intermediate class to avoid directly estimating the noisy class posterior of the transition matrix, and introduces the dual $T-estimator for estimating transition matrices, leading to better classification performances.
- Shared Tasks of the 2015 Workshop on Noisy User-generated Text: Twitter Lexical Normalization and Named Entity Recognition232
The task, annotation process and dataset statistics are outlined, and a high-level overview of the participating systems for each shared task is provided.
- Geolocation Prediction in Social Media Data by Finding Location Indicative Words217
This paper shows that an information gain ratiobased approach surpasses other methods at LIW selection, outperforming state-of-the-art geolocation prediction methods by 10.6% in accuracy and reducing the mean and median of prediction error distance on a public dataset.
- Automatically Constructing a Normalisation Dictionary for Microblogs201
This paper proposes a method for constructing a dictionary of lexical variants of known words that facilitates lexical normalisation via simple string substitution and shows that a dictionary-based approach achieves state-of-the-art performance for both F-score and word error rate on a standard dataset.
- Lexical normalization for social media text197
This article targets out-of-vocabulary words in short text messages and proposes a method for identifying and normalizing lexical variants, which achieves state- of-the-art performance over an SMS corpus and a novel dataset based on Twitter.
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- Searching to Exploit Memorization Effect in Learning with Noisy Labels124
This paper designs a domain-specific search space based on general patterns of the memorization effect and proposes a novel Newton algorithm to solve the bi-level optimization problem ef ficiently and provides theoretical analysis of the algorithm, which ensures a good approximation to critical points.
- 116
- Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation100
The proposed manifold-regularized technique works by directly reducing the estimation error without hurting the approximation error about the estimation problem of T(x), and demonstrates that the method is superior to state-of-the-art approaches for label-noise learning under the challenging IDN.
- 92
- Understanding Robust Overfitting of Adversarial Training and Beyond90
The causes of robust overfitting are explored by comparing the data distribution of non-overfit and strong adversary adversarial training, and it is identified that some small-loss data which are not worthy of the adversary strength cause robust over fitting in the strong adversary mode.
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- Device-Cloud Collaborative Learning for Recommendation78
This work is among the first attempts to study the Device-Cloud Collaborative Learning (DCCL) framework and proposes a novel MetaPatch learning approach on the device side to efficiently achieve "thousands of people with thousands of models'' given a centralized cloud model.
- Harnessing Out-Of-Distribution Examples via Augmenting Content and Style70
A HOOD method that can leverage the content and style from each image instance to identify benign and malign OOD data is proposed, and the proposed novel disentanglement and data augmentation techniques can effectively deal with OOD examples in unknown and open environments.
- 69
- A Stacking-based Approach to Twitter User Geolocation Prediction68
This work implements a city-level geolocation prediction system for Twitter users and suggests that user-declared location metadata is more sensitive to temporal change than the text of Twitter messages.
- Out-of-distribution Detection with Implicit Outlier Transformation65
This work proposes a novel OE-based approach that makes the model perform well for unseen OOD situations, even forseen OOD cases, and offers an efficient way of synthesizing OOD data, which can further benefit the detection model, besides the surrogate Ood data.
- Parts-dependent Label Noise: Towards Instance-dependent Label Noise65
Empirical evaluations demonstrate this method is superior to the state-of-the-art approaches for learning from the instance-dependent label noise and the transition matrices for parts of the instance are learned by exploiting anchor points.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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