LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF)
Jump to navigation
Jump to search
A LSTM with a Conditional Random Field (CRF) layer (LSTM-CRF) is a sequence modeling technique that combines a Long Short-Term Memory (LSTM) network with a Conditional Random Field (CRF) layer to model sequence data with potential dependencies between labels in the sequence.
- Context:
- It can (typically) be used for sequence labeling tasks such as Named Entity Recognition (NER), part-of-speech (POS) tagging, and chunking, where the dependencies between output labels are significant.
- It can (often) leverage the LSTM network to capture long-range dependencies within the input data by processing the data in both forward and backward directions, thus encapsulating context from both sides of a token in the sequence.
- It can (typically) utilize the CRF layer to model the transition probabilities between labels in the sequence, ensuring that the sequence of predicted labels is the most probable given the input sequence.
- It can (often) outperform models that treat the prediction of each label in the sequence independently, by considering the constraints and dependencies between labels in the sequence.
- ...
- Example(s):
- An LSTM-CRF model used for NER might predict the sequence of labels ["B-PER", "I-PER", "O", "B-ORG"] for the input sequence ["John", "Doe", "works", "at", "Google"], taking into account the likelihood of label transitions (e.g., "B-PER" to "I-PER") and the context provided by the LSTM.
- A model used for POS tagging that labels the sequence ["The", "cat", "sat", "on", "the", "mat"] with POS tags ["DT", "NN", "VBD", "IN", "DT", "NN"], considering both the context of each word and the transitions between POS tags.
- ...
- Counter-Example(s):
- A standalone LSTM network without a CRF layer, which might not model label dependencies as effectively.
- A traditional CRF model that does not leverage LSTM for capturing long-range dependencies within the input sequence.
- See: Bidirectional LSTM/CRF Training System, Unidirectional LSTM/CRF Training System, Bidirectional LSTM/CRF Training System, Unidirectional LSTM/CRF Training System.
References
2016
- (Lample et al., 2016) ⇒ Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, Kazuya Kawakami, and Chris Dyer. (2016). “Neural Architectures for Named Entity Recognition.” In: Proceedings of NAACL-HLT (NAACL-HLT, 2016).
- QUOTE: ... We provide a brief description of LSTMs and CRFs, and present a hybrid tagging architecture. This architecture is similar to the ones presented by Collobert et al. (2011) and Huang et al. (2015). ...