Supply chain management has a lot of logistical challenges, which can significantly affect the efficacy and efficiency of logistics operations as a whole. Machine learning techniques are increasingly popular because t...
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The accurate identification of students in need is crucial for governments and colleges to allocate resources more effectively and enhance social equity and educational fairness. Existing approaches to identifying stu...
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In times of crisis, the human mind is often a voracious information forager. It might not be immediately apparent what one wants or needs, and people frequently look for answers to their most pressing questions and wo...
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The digital economy is playing an increasingly important role in the global economy. National and international organizations commonly utilize a composite index composed of multi-dimensional indicators to monitor perf...
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Federated learning has emerged as a promising technique in machine learning, enabling collaborative training across distributed datasets. Particularly in fields like healthcare, where data privacy is paramount, federa...
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The tail-?1 minimization model greatly improves the sparse signal recovery ability compared with ?1 minimization model. However, solving the tail-?1 minimization problem requires high computational cost and a lot of t...
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When uploading multimedia data such as photos or videos on network, they might be covered with mosaic blocks since some private information is not suitable for exposing. In some situation, mosaic images or video frame...
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Document-level relation extraction is a fundamental task of many downstream applications such as knowledge graph and has gained improvement through document graph and sequence models. These methods always utilize the ...
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Explainable artificial intelligence aims to describe an artificial intelligence model and its predictions. In this research work, this technique is applied to a subject of a Computer science degree where the programmi...
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Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity *** is of great significance to some NLP(natural language proc...
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Entity linking refers to linking a string in a text to corresponding entities in a knowledge base through candidate entity generation and candidate entity *** is of great significance to some NLP(natural language processing)tasks,such as question *** English entity linking,Chinese entity linking requires more consideration due to the lack of spacing and capitalization in text sequences and the ambiguity of characters and words,which is more evident in certain *** Chinese domains,such as industry,the generated candidate entities are usually composed of long strings and are heavily *** addition,the meanings of the words that make up industrial entities are sometimes *** semantic space is a subspace of the general word embedding space,and thus each entity word needs to get its exact ***,we propose two schemes to achieve better Chinese entity ***,we implement an ngram based candidate entity generation method to increase the recall rate and reduce the nesting ***,we enhance the corresponding candidate entity ranking mechanism by introducing sense *** the contradiction between the ambiguity of word vectors and the single sense of the industrial domain,we design a sense embedding model based on graph clustering,which adopts an unsupervised approach for word sense induction and learns sense representation in conjunction with *** test the embedding quality of our approach on classical datasets and demonstrate its disambiguation ability in general *** confirm that our method can better learn candidate entities’fundamental laws in the industrial domain and achieve better performance on entity linking through experiments.
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