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Works260 from public data
- TwitterRank2,100
Experimental results show that TwitterRank outperforms the one Twitter currently uses and other related algorithms, including the original PageRank and Topic-sensitive PageRank, which is proposed to measure the influence of users in Twitter.
- Comparing Twitter and Traditional Media Using Topic Models1,338
This paper empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling, and finds interesting and useful findings for downstream IR or DM applications.
- Instance Weighting for Domain Adaptation in NLP915
This paper formally analyze and characterize the domain adaptation problem from a distributional view, and shows that there are two distinct needs for adaptation, corresponding to the different distributions of instances and classification functions in the source and the target domains.
- 597
- Jointly modeling aspects, ratings and sentiments for movie recommendation (JMARS)464
A probabilistic model based on collaborative filtering and topic modeling is proposed that allows it to capture the interest distribution of users and the content distribution for movies; it provides a link between interest and relevance on a per-aspect basis and it allows us to differentiate between positive and negative sentiments on aPer-Aspect basis.
- Learning Natural Language Inference with LSTM461
A special long short-term memory (LSTM) architecture for NLI that remembers important mismatches that are critical for predicting the contradiction or the neutral relationship label and achieves an accuracy of 86.1%, outperforming the state of the art.
- 455
- Jointly Modeling Aspects and Opinions with a MaxEnt-LDA Hybrid452
This paper proposes a MaxEnt-LDA hybrid model to jointly discover both aspects and aspect-specific opinion words and shows that with a relatively small amount of training data, this model can effectively identify aspect and opinion words simultaneously.
- Adaptive filters for continuous queries over distributed data streams449
This work considers an environment where distributed data sources continuously stream updates to a centralized processor that monitors continuous queries over the distributed data, and proposes a new technique for reducing the overhead.
- 376
- Knowledge transfer via multiple model local structure mapping349
A locally weighted ensemble framework to combine multiple models for transfer learning, where the weights are dynamically assigned according to a model's predictive power on each test example, is proposed.
- 346
- Improving Multimodal Named Entity Recognition via Entity Span Detection with Unified Multimodal Transformer343
A Unified Multimodal Transformer to guide the final predictions with the entity span predictions and achieves the new state-of-the-art performance on two benchmark datasets.
- Adapting BERT for Target-Oriented Multimodal Sentiment Classification339
This work studies Target-oriented Multimodal Sentiment Classification (TMSC) and proposes a multimodal BERT architecture and borrows the idea from selfattention and design a target attention mechanism to perform target-image matching to derive targetsensitive visual representations.
- Federated Learning from Pre-Trained Models: A Contrastive Learning Approach322
This work designs a Federated Prototype-wise Contrastive Learning (FedPCL) approach which shares knowledge across clients through their class prototypes and builds client-specific representations in a prototype-wise contrastive manner, and performs a thorough evaluation of the proposed FedPCL in the lightweight framework.
- Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals319
A novel teacher-student approach for the multi-hop KBQA task, where the teacher network tries to learn intermediate supervision signals for improving the reasoning capacity of the student network, and both forward and backward reasoning are utilized to enhance the learning of intermediate entity distributions.
- R3: Reinforced Ranker-Reader for Open-Domain Question Answering305
This paper proposes a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question, and proposes a novel method that jointly trains the Ranker along with an answer-extraction Reader model, based on reinforcement learning
- Query Graph Generation for Answering Multi-hop Complex Questions from Knowledge Bases267
This paper proposes a modified staged query graph generation method with more flexible ways to generate query graphs that achieves the state of the art on three benchmark KBQA datasets.
- Topical Keyphrase Extraction from Twitter256
A context-sensitive topical PageRank method for keyword ranking and a probabilistic scoring function that considers both relevance and interestingness of keyphrases for keyphrase ranking are proposed.
- Finding Bursty Topics from Microblogs253
A topic model that simultaneously captures two observations is proposed that helps find event-driven posts on microblogs and helps identify and filter out "personal" posts.
- Interpretable Rumor Detection in Microblogs by Attending to User Interactions245
This work proposes a post-level attention model (PLAN) to model long distance interactions between tweets with the multi-head attention mechanism in a transformer network and is the first to evaluate the models on two rumor detection data sets: the PHEME data set as well as the Twitter15 and Twitter16 data sets.
- 233
- Learning Sentence Embeddings with Auxiliary Tasks for Cross-Domain Sentiment Classification224
This paper borrows the idea from Structural Correspondence Learning and uses two auxiliary tasks to help induce a sentence embedding that supposedly works well across domains for sentiment classification and proposes to jointly learn this sentenceembedding together with the sentiment classI-class itself.
- CQArank224
This work proposed Topic Expertise Model (TEM), a novel probabilistic generative model with GMM hybrid, to jointly model topics and expertise by integrating textual content model and link structure analysis, and proposed CQARank to measure user interests and expertise score under different topics.
- A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions223
This paper elaborately summarize the typical challenges and solutions for complex KBQA, and presents the two mainstream categories of methods, namely semantic parsing-based (SP-based) methods and information retrieval-based [IR-based] methods.
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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