Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks186 from public data
- 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.
- 55
- A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis45
A novel lexicon-based supervised attention model (LBSA) is proposed, which allows a recurrent neural network to focus on the sentiment content, thus generating sentiment-informative representations and has better interpretability and less noise.
- Mining product reviews based on shallow dependency parsing44
A novel method for mining product reviews, where it mines reviews by identifying product features, expressions of opinions and relations between them by taking advantage of the fact that most of product features are phrases, is presented.
- Knowledge Graph Construction and Applications for Web Search and Beyond40
This paper introduces the solution of building a large-scale multi-source knowledge graph from scratch in Sogou Inc., including its architecture, technical implementation and applications, and builds the knowledge graph on top of SOGouQdb, a distributed search engine developed by Sogu Web Search Department, which can be easily scaled to support petabytes of data.
- Time-aware Personalized Hashtag Recommendation on Social Media40
A novel method is proposed that extends the translation based model and incorporates the temporal and personal factors and incorporates extraction strategies into it to overcome the limitation of only being able to recommend hashtags that exist in the training data of the existing methods.
- Recurrent Memory Reasoning Network for Expert Finding in Community Question Answering39
A novel \textslrecurrent memory reasoning network (RMRN) is proposed to perform expert finding, which focuses on different parts of a question, and accordingly retrieves information from the histories of the candidate expert.
- Discourse Level Explanatory Relation Extraction from Product Reviews Using First-Order Logic39
A novel joint extraction method using firstorder logic to model rich linguistic features and long distance constraints is proposed for identifying subjective text segments and extracting their corresponding explanations from product reviews in discourse level.
- LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition38
This paper proposes $LLM-DA, a novel data augmentation technique based on LLMs for the few-shot NER task that leverages the distinctive characteristics of the NER task by augmenting the original data at both the contextual and entity levels.
- MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective38
MINER, a novel NER learning framework, is proposed to remedy the issue of over-relying on entity mention information, resulting in poor performance on out-of-vocabulary (OOV) entity recognition.
- Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning35
This work leverages Flooding method which primarily aims at better generalization and finds promising in defending adversarial attacks, and proposes an effective criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch.
- SENT: Sentence-level Distant Relation Extraction via Negative Training35
This work proposes the use of negative training (NT), in which a model is trained using complementary labels regarding that “the instance does not belong to these complementary labels”, since the probability of selecting a true label as a complementary label is low, which provides less noisy information.
- 34
- Asynchronous Deep Interaction Network for Natural Language Inference34
An asynchronous deep interaction network (ADIN) is proposed that deconstructs the reasoning process and implements the asynchronous and multi-step reasoning and achieves competitive performance and outperforms strong baselines on three popular benchmarks: SNLI, MultiNLI, and SciTail.
- Sparse higher order conditional random fields for improved sequence labeling33
This work describes Sparse Higher Order Conditional Random Fields (SHO-CRFs), which are able to handle local features and sparse higher order features together using a novel tractable exact inference algorithm, and shows that with the same higher order feature set, SHO- CRFs significantly outperform previous approaches.
- Causal Intervention Improves Implicit Sentiment Analysis32
This work proposes a CausaL intervention model for implicit sEntiment ANalysis using instrumental variable (CLEAN), and introduces instrumental variable to eliminate the confounding causal effects, thus extracting the pure causal effect between sentence and sentiment.
- Long Short-Term Memory with Dynamic Skip Connections32
This work proposes a novel reinforcement learning-based method to model the dependency relationship and connect dependent words, which provides a dynamic skipping advantage over RNNs that always tackle entire sentences sequentially.
- 31
- 31
- Unsupervised Summarization for Chat Logs with Topic-Oriented Ranking and Context-Aware Auto-Encoders30
Experimental results show that RankAE significantly outperforms other unsupervised methods and is able to generate high-quality summaries in terms of relevance and topic coverage.
- 29
- Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model28
A novel co-attention mechanism based neural network is proposed to capture the interactions between participants on argument level and significantly outperforms some state-of-the-art methods for persuasiveness evaluation.
- 27
- 27
- Uncertainty-Aware Label Refinement for Sequence Labeling27
This work introduces a novel two-stage label decoding framework to model long-term label dependencies, while being much more computationally efficient and mitigating the side effects of incorrect draft labels.
- TextFusion: Privacy-Preserving Pre-trained Model Inference via Token Fusion26
This work proposes TextFusion, a novel method for preserving inference privacy, which trains a Fusion Predictor to dynamically fuse token representations, which hides multiple private token representations behind an unrecognizable one.
- Depression Detection on Social Media with Reinforcement Learning26
This work proposes the use of reinforcement learning method to automatically select the indicator posts from the historical posts of users using only the textual information to detect depression based on the content users posted on social media sites.
- 26
- Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation24
This paper presents the ArgTersely benchmark for sentence-level counter-argument generation, drawing from a manually annotated dataset from the ChangeMyView debate forum, and proposes Arg-LlaMA for generating high-quality counter-argument.
- Continuous word embeddings for detecting local text reuses at the semantic level24
A novel method to efficiently detect local reuses at the semantic level for large scale problems by using continuous vector representations of words to capture the semanticlevel similarities between short text segments is introduced.
- 23
- Keyphrase Generation with Fine-Grained Evaluation-Guided Reinforcement Learning23
A new fine-grained evaluation metric is proposed to improve the RL framework, which considers different granularities: token-level $F_1$ score, edit distance, duplication, and prediction quantities, and can effectively ease the synonym problem and generate a higher quality prediction.
- PathQG: Neural Question Generation from Facts22
This paper presents a novel task of question generation given a query path in the knowledge graph constructed from the input text, and formulate query representation learning as a sequence labeling problem for identifying the involved facts to form a query and employ an RNN-based generator for question generation.
- 21
- 20
- 19
- 19
- Review Response Generation in E-Commerce Platforms with External Product Information19
A novel deep neural network model based on the Seq2Seq framework for the review response generation task in e-commerce platforms, which can incorporate product information by a gated multi-source attention mechanism and a copy mechanism is proposed.
- Phrase-based hashtag recommendation for microblog posts18
This work proposes a novel phrase-based topical translation model that uses the bag-of-phrases model to better capture the underlying topics of posted microblogs and demonstrates that the proposed method outperforms state-of-the-art methods that use the unigram model.
- 18
- 17
- Discrete Argument Representation Learning for Interactive Argument Pair Identification17
Experimental results on the large-scale dataset collected from CMV show that the proposed framework can significantly outperform the competitive baselines and reveal why the model yields superior performance and prove the usefulness of the learned representations.
- Chinese-English mixed text normalization17
This paper analyzes the phenomena of mixed usage of Chinese and English in Chinese microblogs and details the proposed two-stage method for normalizing mixed texts, including a noisy channel approach to translate in-vocabulary words into Chinese and a novel user aware neural network language model.
- 16
- 16
- 15
- 15
- Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models14
Self-Consistent Internal Rewards (SCIR) is proposed, a novel framework designed to enhance consistency among internal reward models during training that significantly improves the alignment performance and reward modeling capability of LLMs, outperforming baseline methods by a notable margin.
- 14
- Generating Keyword Queries for Natural Language Queries to Alleviate Lexical Chasm Problem14
This work formulated the task as a translation problem to convert natural language queries into keyword queries as a novel encoder-decoder method with two decoders: the pointer decoder firstly extracts query terms directly from the source text via copying mechanism, then the generator decoder generates query terms using two attention modules simultaneously considering the sourceText and extracted query terms.
- Discourse Relations Detection via a Mixed Generative-Discriminative Framework14
A mixed generative-discriminative framework, in which vector offsets between embeddings of words are used to represent the semantic relations between text segments and Fisher kernel framework is introduced to convert a variable number of vector offsets into a fixed length vector.
- 14
- 12
- 12
- 12
- 12
- 11
- 11
- 11
- 11
- 11
- 10
- 10
- 10
- 10
- Learning Task-Specific Representation for Novel Words in Sequence Labeling10
This work proposes a novel method to predict representations for OOV words from their surface-forms and contexts and shows that the proposed method can achieve better or competitive performance on the OOV problem compared with existing state-of-the-art methods.
- 10
- 10
- 10
- 9
- 9
- Joint Training and Decoding Using Virtual Nodes for Cascaded Segmentation and Tagging Tasks9
A novel method which integrates graph structures of two sub-tasks into one using virtual nodes, and performs joint training and decoding in the factorized state space is presented.
- On the Tip of the Tongue: Analyzing Conceptual Representation in Large Language Models with Reverse-Dictionary Probe8
In-context learning is used to guide the models to generate the term for an object concept implied in a linguistic description to probe LLMs' capacity for conceptual inference in the reverse dictionary task.
- A Soft Contrastive Learning-Based Prompt Model for Few-Shot Sentiment Analysis8
A sentiment-aware chain of thought prompt module is designed to guide the model to predict the sentiment from coarse grain to fine grain via a series of intermediate reasoning steps, and a soft contrastive learning algorithm is proposed to take the correlation of the labels into account.
- 8
- 8
- 7
- 7
- Chinese Named Entity Abbreviation Generation Using First-Order Logic7
A novel entity abbreviation generation method using first-order logic to model long distance constraints to reduce the human effort of manual annotating corpus and an automatically training data construction method with simple strategies is introduced.
- 6
- 6
- 6
- Model the Long-Term Post History for Hashtag Recommendation6
This work proposed to model the long-term post history for the recommendation of a keyword-suggestion-like hashtag recommendation service, and quantitatively studied the informativeness of different parts of the post history and proved the feasibility of organizing the historical posts of a user in the time order.
- 5
- 5
- Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons5
This work proposes to adjust the existing NER system using the previously labeled data and entity lexicons of the newly introduced entity types, and formulate such a task as a partially supervised learning problem and accordingly propose an effective algorithm to solve the problem.
- Reformulating natural language queries using sequence-to-sequence models5
Dear editor, In recent years, reformulating natural language queries has attracted significant amount of attention from both industry and academia, however, there is a lexical chasm betweennatural language queries and relevant documents.
- Chinese Word Similarity Computing Based on Combination Strategy5
A method to calculate the similarity between Chinese words based on combination strategy and the Spearman correlation coefficient of test data shows that the proposed method is feasible and effective.
- 5
- Learning Semantic Lexicons Using Graph Mutual Reinforcement Based Bootstrapping5
Experimental results show that the GMR-based bootstrapping approach outperforms the existing algorithms both in in- domain data and out-domain data and that the result depends on not only the size of the corpus but also the quality.
- 5
- 4
- 4
- 4
- Map search via a factor graph model4
A novel factor graph-based semi-structured retrieval framework is introduced to incorporate concept weighting, attribute selection, and word-based similarity metrics together and demonstrates that this method outperforms both state-of-the-art semi- Structured retrieval methods and some commercial systems in retrieving freeform location queries.
- Understanding the Semantic Intent of Domain-Specific Natural Language Query4
A hierarchical structure is introduced to represent the semantic intent of NL query and then the problem of mapping NL queries to the corresponding semantic intents is focused on.
- Understanding the Semantic Intent of Natural Language Query4
A hierarchical structure is introduced to represent the semantic intent of NL query and then the problem of mapping NL queries to the corresponding semantic intents is focused on.
- 3
- 3
- 3
- 3
- 3
- 3
- Learning to Generate Representations for Novel Words: Mimic the OOV Situation in Training3
A novel training strategy is proposed to address the out-of-vocabulary (OOV) problem in sequence labeling using only training data of the task and trains the supervised model to fit the OOV word representations generated by the mean-pooling operation.
- 3
- A Learning Error Analysis for Structured Prediction with Approximate Inference3
This work compares the estimation and approximation error of both underestimate and overestimate models and shows that, from the perspective of learning errors, performances of approximate inference could be as good as exact inference.
- 3
- 3
- Graph Mutual Reinforcement Based Bootstrapping3
Experimental results on MUC4 corpus show that GMR-Bootstrapping outperforms the state-of-the-art algorithms and is used to extract names of automobile manufactures and models from Chinese corpus.
- Semi-Supervised Multi-View Crowd Counting by Ranking Multi-View Fusion Models2
Two semi-supervised multi-view crowd counting frameworks are proposed by ranking the multi-view fusion models of different numbers of input views, in terms of the model predictions or the model uncertainties, in terms of the model predictions or the model uncertainties.
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- 2
- Traffic signs identification by using sparse image decomposition and adaptive correlation techniques2
- 2
- 2
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- 1
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
- CausalAPM: Generalizable Literal Disentanglement for NLU Debiasing–
CausalAPM, a generalizable literal disentangling framework to ameliorate the bias problem from feature granularity, is proposed and extensive experiments demonstrate that the proposed framework significantly improves the OOD generalization performance while maintaining ID performance.
- –
- –
- –
- –
- Mining User Profiles from Query Log–
A novel method for mining user profiles using the query log in a search engine using the popular convolution neural network, which infers the searching topic of the query, by fitting a Gaussian mixture distribution, and obtains the query topic distribution.
- –
- –
- –
- –
- –
- –
- –
- –
- –
- –
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.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.