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- Directional Skip-Gram: Explicitly Distinguishing Left and Right Context for Word Embeddings380
Direction Skip-gram (DSG), a simple but effective enhancement of the skip-gram model by explicitly distinguishing left and right context in word prediction, and which outperforms others on different datasets in semantic and syntactic evaluations.
- Challenging the long tail recommendation376
Empirical experiments show that the proposed algorithms are effective to recommend long tail items and outperform state-of-the-art recommendation techniques.
- Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen362
A large drug combination screen across cancer cell lines is provided to benchmark crowdsourced methods and to computationally predict drug synergies, and genomic rationale for synergy predictions are identified.
- Mapping the Research Trends by Co-word Analysis Based on Keywords from Funded Project302
A co-word method based on keywords from funded project is proposed to map the research trends and shows that Game Theory, Supply Chain Management, Complex Network, Data Mining, Optimize, Risk Management, and Data Envelopment Analysis are hot topics.
- An extensive study on pre-trained models for program understanding and generation189
The first study for natural language-programming language pre-trained model robustness via adversarial attacks is performed and it is found that a simple random attack approach can easily fool the state-of-the-art pre- trained models and thus incur security issues.
- A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check162
This paper proposes a novel approach of constructing CSC corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to the OCR- and ASR-based methods, respectively.
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- Network-based prediction of drug–target interactions using an arbitrary-order proximity embedded deep forest143
A network-based computational framework, termed AOPEDF, an Arbitrary-Order Proximity Embedded Deep Forest approach, for prediction of DTIs achieves high accuracy in identifying molecular targets among known drugs on two external validation sets, outperforming several state-of-the-art methods.
- Topic Memory Networks for Short Text Classification140
Experimental results show that the proposed topic memory networks for short text classification with a novel topic memory mechanism to encode latent topic representations indicative of class labels outperforms state-of-the-art models on shortText classification and generates coherent topics.
- Finding hierarchy in directed online social networks130
This paper defines a measure of hierarchy in a directed online social network, and presents an efficient algorithm to compute this measure, which is validated using ground truth including Wikipedia notability score and shows how hierarchy emerges as the size of the network increases.
- Step-Audio: Unified Understanding and Generation in Intelligent Speech Interaction129
Step-Audio is introduced, the first production-ready open-source solution that achieves state-of-the-art performance in human evaluations, especially in terms of instruction following and on open-source benchmarks like LLaMA Question.
- HME112
A novel hyperbolic metric embedding (HME) model is proposed, which projects the check-in data into aHyperbolic space, which can effectively capture the underlying hierarchical structures, which are implied by the power-law distributions of user movements.
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- ACRec90
This paper proposes a random walk model using three academic metrics as basics for recommending new collaborations through co-authorship in an academic network and shows that it can improve the precision, recall rate and coverage rate of recommendation, compared with other state-of-the-art approaches.
- A bibliometric review of a decade of research: Big data in business research – Setting a research agenda86
This review study depicts the development of big data in business research using bibliometric methods, such as publication counts and trends analysis, co-citation analysis, Co-authorship analysis and keywords co-occurrence analysis, to provide scholars with a systematic understanding of the development and panoramic roadmap ofbig data research in business.
- Topic-Aware Neural Keyphrase Generation for Social Media Language85
This work proposes a sequence-to-sequence (seq2seq) based neural keyphrase generation framework, enabling absent keyphrases to be created, and shows that the model significantly outperforms both extraction and generation models without exploiting latent topics.
- M 6 Doc: A Large-Scale Multi-Format, Multi-Type, Multi-Layout, Multi-Language, Multi-Annotation Category Dataset for Modern Document Layout Analysis72
A transformer-based document layout analysis method called TransDLANet is proposed, which leverages an adaptive element matching mechanism that enables query embedding to better match ground truth to improve recall, and constructs a segmentation branch for more precise document image instance segmentation.
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- Recurrent Neural Networks with External Memory for Spoken Language Understanding61
This work proposes to use an external memory to improve memorisation capability of RNNs and demonstrates that the proposed model was able to achieve the state-of-the-art results.
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- Privacy Protection for Open Sharing of Psychiatric and Behavioral Research Data: Ethical Considerations and Recommendations57
This study comprehensively examined the ethical considerations surrounding the acquisition, management, sharing, and utilization of psychiatric and behavioral research data and suggested ethical standards for privacy protection based on varying levels of data sensitivity.
- Learning distributed word representation with multi-contextual mixed embedding57
The proposed MWE model combines the two variants of word2vec in a seamless way via sharing a common encoding structure, which is able to capture the syntax information of words more accurately and incorporates a global text vector into the CBOW variant so as to capture more semantic information.
- MetaNER: Named Entity Recognition with Meta-Learning56
MetaNER incorporates meta-learning and adversarial training strategies to encourage robust, general and transferable representations for sequence labeling and is capable of adapting to new unseen domains with a small amount of annotated data from those domains.
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- Recommendation Algorithm based on Link Prediction and Domain Knowledge in Retail Transactions54
A new recommendation algorithm is proposed, which extends the idea of linkage measure to recommendation in bipartite network, and incorporate domain knowledge with topological property in recommendation process to improve the recommendation quality.
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 2026-10-16. 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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