Name
Abstract | ||
|---|---|---|
Recurrent neural networks (RNNs) are a powerful model for sequential data. RNNs that use long short-term memory (LSTM) cells have proven effective in handwriting recognition, language modeling, speech recognition, and language comprehension tasks. In this study, we propose LSTM conditional random fields (LSTM-CRF); it is an LSTM-based RNN model that uses output-label dependencies with transition features and a CRF-like sequence-level objective function. We also propose variations to the LSTM-CRF model using a gate recurrent unit (GRU) and structurally constrained recurrent network (SCRN). Empirical results reveal that our proposed models attain state-of-the-art performance for named entity recognition. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1587/transinf.2016EDP7179 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS |
Keywords | Field | DocType |
LSTM-CRF, LSTM RNN, recurrent neural network, name entity recognition | Entity linking,Pattern recognition,Computer science,Recurrent neural network,Speech recognition,Artificial intelligence,Natural language processing,Named-entity recognition | Journal |
Volume | Issue | ISSN |
E100D | 4 | 1745-1361 |
Citations | PageRank | References |
3 | 0.38 | 8 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Changki Lee | 1 | 279 | 26.18 |