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- Ting LiuSuggested from co-authorship
- Guohong FuSuggested from co-authorship
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Works167 from public data
- Unified Named Entity Recognition as Word-Word Relation Classification392
This work develops a neural framework, in which the unified NER is modeled as a 2D grid of word pairs, and proposes multi-granularity 2D convolutions for better refining the grid representations.
- Gated Neural Networks for Targeted Sentiment Analysis311
A sentence-level neural model is proposed to address the limitation of pooling functions, which do not explicitly model tweet-level semantics and gives significantly higher accuracies compared to the current best method for targeted sentiment analysis.
- A neural joint model for entity and relation extraction from biomedical text251
It is demonstrated that the model based on neural networks is effective for biomedical entity and relation extraction and parameter sharing is an alternative method for neural models to jointly process this task.
- Neural Networks for Open Domain Targeted Sentiment247
This work empirically studies the effect of word embeddings and automatic feature combinations on the open domain targeted sentiment task by extending a CRF baseline using neural networks, and proposes a novel integration of neural and discrete features which combines their relative advantages, leading to significantly higher results compared to both baselines.
- Tweet Sarcasm Detection Using Deep Neural Network217
This work investigates the use of neural network for tweet sarcasm detection, and compares the effects of the continuous automatic features with discrete manual features.
- GME: Improving Universal Multimodal Retrieval by Multimodal LLMs203
The General Multimodal Embedder (GME) is developed, an MLLM-based dense retriever designed for UMR, and a comprehensive UMR Benchmark (UMRB) is constructed to evaluate the effectiveness of this approach.
- End-to-End Neural Relation Extraction with Global Optimization188
This work builds a globally optimized neural model for end-to-end relation extraction, proposing novel LSTM features in order to better learn context representations, and presents a novel method to integrate syntactic information to facilitate global learning.
- Context-Sensitive Twitter Sentiment Classification Using Neural Network151
A context-based neural network model for Twitter sentiment analysis is proposed, incorporating contextualized features from relevant Tweets into the model in the form of word embedding vectors.
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- Transition-Based Neural Word Segmentation126
Experimental results demonstrate that word features lead to comparable performance to the best systems in the literature, and a further combination of discrete and neural features obtains top accuracies on several benchmarks.
- A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition123
Experimental results on multiple benchmark datasets show that the proposed span-based model can be regarded as a relation extraction paradigm essentially and is highly competitive for overlapped and discontinuous NER.
- Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus116
This paper proposes a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations, and Experimental results on Universal Proposition Bank show that the translation-based method is highly effective, and the automatic pseudo datasets can improve the target-language SRL performances significantly.
- LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model114
In this work, a novel structure-aware GLM is proposed, fully unleashing the power of syntactic knowledge for UIE, and rich task-adaptive structural bias is learned that greatly resolves the UIE crux, the long-range dependence issue and boundary identifying.
- Improving Twitter Sentiment Classification Using Topic-Enriched Multi-Prototype Word Embeddings113
Two neural networks are developed which learning topic-enriched multi-prototype word embeddings (TMWE) that better capture tweet context by incorporating topic information and a single distributed representation for each word.
- HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations109
A transformer-based context- and speaker-sensitive model for EDC, namely HiTrans, which consists of two hierarchical transformers, which outperforms previous state-of-the-art models.
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- Enhancing Video-Language Representations With Structural Spatio-Temporal Alignment87
Finnsta is designed as a plug&play system, which can be integrated into existing well-trained VLMs for further representation augmentation, without training from scratch or relying on SG annotations in downstream applications.
- On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model, Data, and Training85
This study strengthens the current best-robust syntax-aware models by further incorporating the rich external syntactic dependencies and the labels with aspect simultaneously with a universal-syntax graph convolutional network and proposes to automatically induce high-quality synthetic training data with various types, allowing models to learn sufficient inductive bias for better robustness.
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- Transition-based Neural RST Parsing with Implicit Syntax Features77
This paper proposes an implicit syntax feature extraction approach, using hidden-layer vectors extracted from a neural syntax parser, using a simple transition-based model as the baseline, further enhancing it with dynamic oracle.
- Character-Level Chinese Dependency Parsing76
This paper presents novel adaptations of two major shift-reduce dependency parsing algorithms to character-level parsing, and demonstrates improved performances over word-based parsing methods on the Chinese Treebank.
- Combining Discrete and Neural Features for Sequence Labeling74
The results on standard benchmarks show that state-of-the-art neural models can give accuracies comparable to the best discrete models in the literature for most tasks and combing discrete and neural features unanimously yield better results.
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- Have You been Properly Notified? Automatic Compliance Analysis of Privacy Policy Text with GDPR Article 1373
This work targets to solve the problem of compliance analysis between GDPR (Article 13) and privacy policies and manually curates a corpus of 36,610 labeled sentences from 304 privacy policies, and conducts a rule-based analysis and a user study to evaluate the usability of the approach.
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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 2026-10-12. 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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