Pre-trained language models (PLMs) are first trained on a large dataset and then directly transferred to downstream tasks, or further fine-tuned on another small dataset for specific NLP tasks. Early PLMs, such as Skip-Gram [1] and GloVe [2], are shallow neural networks, and their word e...
Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient. Many existing methods are purely data driven, focusing on exploiting the intrinsic topology and construction rules of molecules without any chemical prior inf...
(1) A white car and a red sheep (1) A single clock is sitting on a table (2) A panda making latte art (2) An umbrella on top of a spoon (3) A small red ball in a big green block (3) Wolf in a suit (4) A burning fish (4...
The results show that the proposed method significantly outperforms existing models across multiple evaluation metrics, with an F1 score improvement of 2.4% on the CHIP-CTC dataset, 3.1% on the IMCS-V2-NER dataset,and 4.2% on the KUAKE-QTR dataset. Additionally,ablation studies confirmed the ...
Finding useful information from big data is very difficult and time consuming. Recommendation systems are a good solution to find useful information according to users' interests. Usually, recommendation system is a collection of algorithms that discover data patterns from the accessible dataset by ...
Long text brings a big challenge to neural network based text matching approaches due to their complicated structures. To tackle the challenge, we propose a knowledge enhanced hybrid neural network (KEHNN) that leverages prior knowledge to identify useful information and filter out noise in long ...
Achieves isolation between the engine and business logic, domain models, facilitating rapid definition of knowledge graph solutions for businesses; Constructs a controllable AI technology stack driven by knowledge, based on the OpenSPG engine, connecting deep learning capabilities such as LLM and GraphLea...
By linking prior knowledge of the PKG’s structure, the KGE models become more robust and better able to capture the underlying patterns in the data. 2.5. Evaluation metrics To measure the performance of these KGE methods of link prediction in the PKG and the CKG, we employ three standard ...
Bender E M, Gebru T, McMillan-Major A, Shmitchell S. On the dangers of stochastic parrots: Can language models be too big? InProc. the 2021 ACM Conference on Fairness, Accountability, and Transparency, Mar. 2021, pp.610–623. DOI:https://doi.org/10.1145/3442188.3445922. ...
Recent advancements in machine learning techniques, particularly deep learning, have enabled these systems to analyze users’ historical behavior and predict their future actions with greater accuracy [12]. Additionally, the rise of big data technologies has empowered these systems to process enormous dat...