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• W2775287156 abstract "We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is difficult to consider all possible variable length segments, our method uses segment lattice constructed from the word-level tagging model to reduce the search space. Performing experiments on NER and chunking, we demonstrate that our method outperforms conventional word-level CRF with neural networks." @default.
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• W2775287156 date "2017-11-01" @default.
• W2775287156 modified "2023-09-23" @default.
• W2775287156 title "Segment-Level Neural Conditional Random Fields for Named Entity Recognition" @default.
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