SPRF: A semantic Pseudo-relevance Feedback enhancement for information retrieval via ConceptNet

Pan, Min; Pei, Quanli; Yu, Lei; Li, Teng; Huang, Ellen; Wang, Junmei; Huang, Jimmy Xiangji

doi:10.1016/j.knosys.2023.110602

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…In the original RAT-SQL model, the schemalinking operation utilizes strings for matching, and therefore, the matching mechanism will not work correctly when multiple languages are involved. To address this problem, we introduced a multilingual, cross-domain common sense knowledge graph (ConceptNet [37]) and optimized the schema-linking process using its tautological edges. ConceptNet is a directed graph structure whose vertices are natural language words and phrases, and its edges are labeled 'types' and 'weights'.…”

Section: Natural Language Query Interfacementioning

confidence: 99%

AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments

Chen,

Jiao,

Song

et al. 2024

Applied Sciences

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In recent years, with the rapid development of medicine, pathology, toxicology, and neuroscience technology, animal behavior research has become essential in modern life science research. However, the current mainstream commercial animal behavior recognition tools only provide a single behavior recognition method, limiting the expansion of algorithms and how researchers interact with experimental data. To address this issue, we propose an AI-enabled, highly usable platform for analyzing experimental animal behavior, which aims to provide better flexibility, scalability, and interactivity to make the platform more usable. Researchers can flexibly select or extend different behavior recognition algorithms for automated recognition of animal behaviors or experience more convenient human-computer interaction through natural language descriptions only. A case study at a medical laboratory where the platform was used to evaluate behavioral differences between sick and healthy animals demonstrated the high usability of the platform.

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Section: Natural Language Query Interfacementioning

confidence: 99%

AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments

Chen,

Jiao,

Song

et al. 2024

Applied Sciences

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“…Bi et al 43 utilized fine-grained semantic matching to explain the ranking results to serve people shopping online. Pan, Li, et al 44 proposed SE-BERT to utilize a pre-trained generative language model to summarize the sides of candidate passages and connect them to a new input sequence.…”

Section: Contributions and Shortcomings Solutionsmentioning

confidence: 99%

Utilizing passage‐level relevance and kernel pooling for enhancing BERT‐based document reranking

Pan,

Zhou,

et al. 2024

Computational Intelligence

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The pre‐trained language model (PLM) based on the Transformer encoder, namely BERT, has achieved state‐of‐the‐art results in the field of Information Retrieval. Existing BERT‐based ranking models divide documents into passages and aggregate passage‐level relevance to rank the document list. However, these common score aggregation strategies cannot capture important semantic information such as document structure and have not been extensively studied. In this article, we propose a novel kernel‐based score pooling system to capture document‐level relevance by aggregating passage‐level relevance. In particular, we propose and study several representative kernel pooling functions and several different document ranking strategies based on passage‐level relevance. Our proposed framework KnBERT naturally incorporates kernel functions from the passage level into the BERT‐based re‐ranking method, which provides a promising avenue for building universal retrieval‐then‐rerank information retrieval systems. Experiments conducted on two widely used TREC Robust04 and GOV2 test datasets show that the KnBERT has made significant improvements over other BERT‐based ranking approaches in terms of MAP, P@20, and NDCG@20 indicators with no extra or even less computations.

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GEML: a graph-enhanced pre-trained language model framework for text classification via mutual learning

Yu,

Song,

Pinto

et al. 2024

Appl Intell

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SPRF: A semantic Pseudo-relevance Feedback enhancement for information retrieval via ConceptNet

Cited by 6 publications

References 31 publications

AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments

AI-Enabled Animal Behavior Analysis with High Usability: A Case Study on Open-Field Experiments

Utilizing passage‐level relevance and kernel pooling for enhancing BERT‐based document reranking

GEML: a graph-enhanced pre-trained language model framework for text classification via mutual learning

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