Relation Extraction
Relation extraction is a natural-language-processing task that detects and classifies the semantic relations that hold between entities mentioned in text. Building on early kernel-based methods (Zelenko and colleagues, 2003) and later neural matching approaches (Baldini Soares and colleagues, 2019), it turns free-form text into structured facts of the form entity–relation–entity.
Rekod sumber
Petikan disalin secara verbatim daripada rekod sumber kaedah. Tiada pengesahan peringkat tuntutan disimpulkan daripadanya.
- Zelenko, D., Aone, C. & Richardella, A. (2003). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083-1106. · URL
- Soares, L. B., FitzGerald, N., Ling, J. & Kwiatkowski, T. (2019). Matching the Blanks: Distributional Similarity for Relation Learning. Proceedings of ACL 2019. · DOI 10.18653/v1/P19-1279
Tuntutan yang dikurasi
Tuntutan disimpan dalam lejar bukti, setiap satu dengan penilaiannya sendiri.
Pandangan ini tidak mencipta penilaian tuntutan apabila lejar tiada.
Kaedah berkaitan
Dijana daripada graf kaedah dan ditunjukkan sebagai perhubungan yang dicadangkan mesin — tiada tuntutan bukti disimpulkan.