Re-ranking models of parse trees have been focused on re-ordering parse trees with a syntactic view. However, also a semantic view should be considered in re-ranking parse trees, because the fact that a word pair has ...
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ISBN:
(纸本)9781467395960
Re-ranking models of parse trees have been focused on re-ordering parse trees with a syntactic view. However, also a semantic view should be considered in re-ranking parse trees, because the fact that a word pair has a dependency implies that the pair has both syntactic and semantic relations. This paper proposes a re-ranking model for dependency parsing based on a combination of syntactic and semantic plausibilities of dependencies. The syntactic probability is used as a syntactic plausibility of a parse tree, and a knowledge graph embedding is adopted to represent its semantic plausibility. The knowledge graph embedding allows the semantic plausibility of parse trees to be expressed effectively with ease. The experiments on the standard Penn Treebank corpus prove that the proposed model improves the base parser regardless of the number of candidate parse trees.
Re-ranking models of parse trees have been focused on re-ordering parse trees with a syntactic ***, also a semantic view should be considered in reranking parse trees, because the fact that a word pair has a dependenc...
详细信息
Re-ranking models of parse trees have been focused on re-ordering parse trees with a syntactic ***, also a semantic view should be considered in reranking parse trees, because the fact that a word pair has a dependency implies that the pair has both syntactic and semantic relations. This paper proposes a re-ranking model for dependency parsing based on a combination of syntactic and semantic plausibilities of dependencies. The syntactic probability is used as a syntactic plausibility of a parse tree,and a knowledge graph embedding is adopted to represent its semantic plausibility. The knowledge graph embedding allows the semantic plausibility of parse trees to be expressed effectively with ease. The experiments on the standard Penn Treebank corpus prove that the proposed model improves the base parser regardless of the number of candidate parse trees.
Recommendation systems are designed to uncover users’ potential preferences and make recommendations. However, they often face challenges such as data sparsity and the cold start problem. Although the introduction of...
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Recommendation systems are designed to uncover users’ potential preferences and make recommendations. However, they often face challenges such as data sparsity and the cold start problem. Although the introduction of knowledgegraphs has partially addressed the issue of data sparsity, the challenge of cold start has not been effectively resolved. In this paper, a novel approach called Social Perception with graph Attention Network (SPGAT) for Recommendation is proposed. In SPGAT, we aim to leverage social perception to solve the cold start effectively for more accurate recommendations. The approach utilizes a multi-layer graph attention network to aggregate user preference features from collaborative knowledgegraphs and social perception graphs. By analyzing the social network of a new user, associated friend users can be identified. The interaction data of these friend users is then provided as side information to recommend to the new user. To handle one-to-many and many-to-many relations, we introduce the TransD graphembedding model, which maps different types of relations and entities to different spaces. To optimize the proposed SPGAT, self-adversarial negative sampling is utilized to implement entity and relation embedding and generate negative samples. Experimental results demonstrate that SPGAT has achieved superior performance compared to several advanced methods.
The rapid growth of biomedical publications has presented significant challenges in the field of information retrieval. Most existing work focuses on document retrieval given explicit queries. However, in real applica...
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The rapid growth of biomedical publications has presented significant challenges in the field of information retrieval. Most existing work focuses on document retrieval given explicit queries. However, in real applications such as curated biomedical database maintenance, explicit queries are missing. In this paper, we propose a two-step model for biomedical information retrieval in the case that only a small set of example documents is available without explicit queries. Initially, we extract keywords from the observed documents using large pre-trained language models and biomedical knowledgegraphs. These keywords are then enriched with domain-specific entities. Information retrieval techniques can subsequently use the collected entities to rank the documents. Following this, we introduce an iterative Positive-Unlabeled learning method to classify all unlabeled documents. Experiments conducted on the PubMed dataset demonstrate that the proposed technique outperforms the state-of-the-art positive-unlabeled learning methods. The results underscore the effectiveness of integrating large language models and biomedical knowledgegraphs in improving zero-shot information retrieval performance in the biomedical domain.
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