Capturing Long-range Contextual Dependencies with Memory-enhanced Conditional Random Fields

IJCNLP 2017 Fei LiuTimothy BaldwinTrevor Cohn

Despite successful applications across a broad range of NLP tasks, conditional random fields ("CRFs"), in particular the linear-chain variant, are only able to model local features. While this has important benefits in terms of inference tractability, it limits the ability of the model to capture long-range dependencies between items... (read more)

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