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- 444
- Adaptive Co-attention Network for Named Entity Recognition in Tweets436
A bi-directional long short term memory network with conditional random fields and an adaptive co-attention network is extended to achieve named entity recognition for tweets to make full use of textual and visual information.
- Phrase dependency parsing for opinion mining435
A novel approach for mining opinions from product reviews is presented, where it converts opinion mining task to identify product features, expressions of opinions and relations between them by taking advantage of the observation that a lot of product features are phrases.
- CNN-Based Chinese NER with Lexicon Rethinking265
This work proposes a faster alternative to Chinese NER: a convolutional neural network (CNN)-based method that incorporates lexicons using a rethinking mechanism that can model all the characters and potential words that match the sentence in parallel.
- A Lexicon-Based Graph Neural Network for Chinese NER249
A lexicon-based graph neural network with global semantics is introduced, in which lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and long-range dependency.
- Hashtag recommendation using attention-based convolutional neural network224
CNNs are adopted to perform the hashtag recommendation problem for microblogs and a novel architecture with an attention mechanism is proposed to incorporate the trigger words whose effectiveness have been experimentally evaluated in several previous works.
- Keyphrase Extraction Using Deep Recurrent Neural Networks on Twitter198
A novel deep recurrent neural network model is proposed to combine keywords and context information to perform keyphrases from tweets and the experimental results showed that the proposed method performs significantly better than previous methods.
- Cooperative Multimodal Approach to Depression Detection in Twitter135
Experimental results demonstrate that the proposed method outperforms state-of-the-art methods by a large margin, and the model can obtained a robust performance in realistic scenarios.
- Retweet Prediction with Attention-based Deep Neural Network120
A novel attention-based deep neural network to incorporate contextual and social information for this task and experimental results showed that the proposed method could achieve better results than the previous state-of-the-art methods.
- Trainable Undersampling for Class-Imbalance Learning116
A meta-learning method built on the undersampling to parametrize the data sampler and train it to optimize the classification performance over the evaluation metric through reinforcement learning, which solves the non-differentiable optimization problem for training the data Sampler via reinforcement learning.
- How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks114
While GPT-3.5 outperforms existing fine-tuned models on some tasks, it still encounters significant robustness degradation, such as its average performance dropping by up to 35.74\% and 43.59\% in natural language inference and sentiment analysis tasks, respectively.
- A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images112
The experimental results indicate that the proposed DCCA-MKL framework achieves best performance for discriminating benign liver tumors from malignant liver cancers and it is proved that the three-phase CEUS image based CAD is feasible for liver tumors with the proposed DCCA-MKL framework.
- 111
- Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution94
An effective method to further improve the robustness of neural text classifiers against adversarial word-substitution attacks for neural NLP models is proposed and achieved the highest accuracy on both clean and adversarial examples on AGNEWS and IMDB datasets.
- A benchmark for automatic medical consultation system: frameworks, tasks and datasets93
This article creates a new large medical dialogue dataset with multi-level fine-grained annotations and establishes five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy.
- One2Set: Generating Diverse Keyphrases as a Set93
This work proposes a new training paradigm One2 set, a novel model that utilizes a fixed set of learned control codes as conditions to generate a set of keyphrases in parallel, and proposes a K-step label assignment mechanism via bipartite matching, which greatly increases the diversity and reduces the repetition rate of generated keyPhrases.
- Part-of-Speech Tagging for Twitter with Adversarial Neural Networks92
A novel neural network to make use of out-of-domain labeled data, unlabeled in- domain data, and labeled in-domain data is proposed to learn common features through adversarial discriminator for Tweets tagging.
- FudanNLP: A Toolkit for Chinese Natural Language Processing82
FudanNLP is an open source toolkit for Chinese natural language processing (NLP), which uses statistics-based and rule-based methods to deal with Chinese NLP tasks, such as word segmentation, part-ofspeech tagging, named entity recognition, dependency parsing, time phrase recognition, anaphora resolution and so on.
- AgentPRM: Process Reward Models for LLM Agents via Step-Wise Promise and Progress81
This work explores constructing process reward models (PRMs) to evaluate each decision and guide the agent's decision-making process, and proposes a re-defined PRM for agent tasks, named AgentPRM, to capture both the interdependence between sequential decisions and their contribution to the final goal.
- 79
- Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling65
This work proposes a novel topic-augmented two-stage dialogue summarizer (TDS) jointly with a saliency-aware neural topic model (SATM) for topic-oriented summarization of customer service dialogues.
- Math Word Problem Solving with Explicit Numerical Values63
A novel approach called NumS2T is proposed, which enhances math word problem solving performance by explicitly incorporating numerical values into a sequence-to-tree network.
- Structural Opinion Mining for Graph-based Sentiment Representation61
A novel graph-based representation for sentence level sentiment is proposed and an integer linear programming-based structural learning method is introduced to produce the graph representations of input sentences.
- Tasty Burgers, Soggy Fries: Probing Aspect Robustness in Aspect-Based Sentiment Analysis56
A simple but effective approach to enrich ABSA test sets is developed to disentangle the confounding sentiments of the non-target aspects from the target aspect's sentiment, and several ways to improve aspect robustness are explored.
- Adaptive Multi-Attention Network Incorporating Answer Information for Duplicate Question Detection56
An answer information- enhanced adaptive multi-attention network (AMAN) is proposed to perform this task, which takes full advantage of the semantic information in the paired answers while alleviating the noise problem caused by adding the answers.
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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