Document Similarity Metric Learning Task

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A Document Similarity Metric Learning Task is a similarity metric learning task to learn a document similarity scoring model (that can assess the semantic similarity between two or more text documents).



References

2009

  • (Ahlgren & Colliander, 2009) ⇒ Per Ahlgren and Cecilia Colliander. (2009). “Document–document similarity approaches and science mapping: Experimental comparison of five approaches." In: Journal of Informetrics 3.1: 1-12.
    • QUOTE: "This paper treats document–document similarity approaches in the context of science mapping. Five approaches, involving nine methods, are compared experimentally. We compare ..."
    • NOTE: Compares multiple approaches to Document Similarity Metric Learning experimentally in the context of science mapping applications.

2008

  • (Chim & Deng, 2008) ⇒ Hoi Chim and Xiaotie Deng. (2008). “Efficient phrase-based document similarity for clustering." In: IEEE Transactions on Knowledge and Data Engineering 20.9 (2008): 1217-1229.
    • QUOTE: "... we propose a phrase-based document similarity measure called VSM-EPM ... our experiments indicate that the VSM-EPM similarity is very effective for clustering tasks."
    • NOTE: Proposes and evaluates a phrase-based approach to Document Similarity Metric Learning that outperforms word overlap methods.

2005

  • (Lee et al., 2005) ⇒ Michael D. Lee, Brandon Pincombe, and Matthew Welsh. (2005). “An empirical evaluation of models of text document similarity." In: Proceedings of the 27th Annual Conference of the Cognitive Science Society. pp. 1254-1259.
    • QUOTE: "... we experimentally compared a number of recently developed models of text document similarity. These include ... We evaluated their performance at predicting human judgments of text document similarity."
    • NOTE: Empirically compares and evaluates different approaches to Document Similarity Metric Learning on standard datasets.

2003

  • (Aslam & Frost, 2003) ⇒ Javed A. Aslam and Matthias Frost. (2003). “An Information-Theoretic Measure for Document Similarity." In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 449–450.
    • QUOTE: "...test the effectiveness of an information-theoretic measure for pairwise document similarity. We adapt query retrieval to rate the quality of document similarity..."
    • NOTE: Proposes and evaluates an information-theoretic approach to quantify Document Similarity for tasks like clustering and retrieval.