--- title: "Bidirectional LSTM-CRF Models for Sequence — Huang 2015" description: "This 2015 Baidu Research paper looks at sequence tagging (part-of-speech tagging, chunking and named entity recognition) with recurrent neural networks…" authors: ["Zhiheng Huang", "Wei Xu", "Kai Yu"] year: 2015 venue: "arXiv:1508.01991" source: "https://arxiv.org/abs/1508.01991" doi: "10.48550/arxiv.1508.01991" arxiv: "1508.01991" licence: "arXiv — non-exclusive licence to distribute" licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/huang-2015-bidirectional-lstm-crf-models-sequence/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/huang-2015-bidirectional-lstm-crf-models-sequence/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2015](https://md.research.elamur.ai/free/en/papers/2015/) / Bidirectional LSTM-CRF Models for Sequence Tagging # Bidirectional LSTM-CRF Models for Sequence Tagging **Authors:** [Zhiheng Huang](https://md.research.elamur.ai/free/en/authors/zhiheng-huang/), [Wei Xu](https://md.research.elamur.ai/free/en/authors/wei-xu/), Kai Yu · **Year:** 2015 · **Venue:** arXiv:1508.01991 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/) **Links:** [Publisher's page](https://arxiv.org/abs/1508.01991) · [PDF](https://arxiv.org/pdf/1508.01991) · [DOI 10.48550/arxiv.1508.01991](https://doi.org/10.48550/arxiv.1508.01991) · [arXiv 1508.01991](https://arxiv.org/abs/1508.01991) · [OpenAlex](https://openalex.org/W1940872118) **Citations:** 3078 citations (openalex-snapshot · 2026-10-14) · [724](#cited-by) in our library, of the 140673 papers whose bibliographies we read ## Summary This 2015 Baidu Research paper looks at sequence tagging (part-of-speech tagging, chunking and named entity recognition) with recurrent neural networks instead of linear models such as HMM, MEMM and CRF. The authors compare five architectures under one SGD training setup: a plain LSTM, a bidirectional LSTM, a standalone CRF, an LSTM with a CRF output layer, and a bidirectional LSTM with a CRF layer (BI-LSTM-CRF). In the BI-LSTM-CRF, the forward and backward LSTM passes read context on both sides of each word. The CRF layer adds a learned tag-transition matrix and scores whole tag sequences, so labels are not chosen one position at a time. The authors say this is the first use of BI-LSTM-CRF on standard NLP tagging benchmarks. Tested on Penn TreeBank POS, CoNLL 2000 chunking and CoNLL 2003 NER, it matches or nearly matches the best published systems. It also stays strong with randomly initialized embeddings, so it depends less on pretrained word vectors than the earlier convolutional approach. The architecture went on to become a standard neural baseline for tagging. ## Key points - Five models (LSTM, BI-LSTM, CRF, LSTM-CRF, BI-LSTM-CRF) are tested on three benchmarks: Penn TreeBank (PTB) POS, CoNLL 2000 chunking and CoNLL 2003 NER. Inputs combine word embeddings with spelling and context features. - With Senna embeddings, BI-LSTM-CRF scores 97.55% POS accuracy, which beat the earlier systems the paper lists. It reaches 94.46 F1 on chunking, behind only Shen and Sarkar's 95.23. On NER it reaches 90.10 F1 with a gazetteer, compared with 90.90 for Passos et al. (2014). - With random embeddings, BI-LSTM-CRF clearly beats Collobert et al.'s Conv-CRF on NER (84.26 vs. 81.47 F1). With Senna embeddings the gap narrows to 90.10 vs. 89.59. - When only word features are used, the BI-LSTM-CRF chunking F1 drops by just 0.06, while the standalone CRF loses about 8.5 points. - Training uses batches of sentences totaling about 100 tokens. Hidden states are reset at the start of every sentence, and the transition matrix is learned together with the LSTM weights. *Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.* Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/) Topics: [Named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) ## Cited by Cited by 724 papers of our library cite it (of the 140673 whose bibliographies we read); 38 of them have a page here, 686 not yet. - [A Neural Architecture for Dialectal Arabic Segmentation](https://md.research.elamur.ai/free/en/papers/aclbulk-2017-samih-neural-architecture-dialectal-arabic-segmentation/) - [Don't Throw Those Morphological Analyzers Away Just Yet: Neural Morphological Disambiguation for Arabic](https://md.research.elamur.ai/free/en/papers/aclbulk-2017-zalmout-don-t-throw-those-morphological/) - [Contextual String Embeddings for Sequence Labeling](https://md.research.elamur.ai/free/en/papers/akbik-2018-contextual-string-embeddings-sequence-labeling/) - [Pooled Contextualized Embeddings for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/akbik-2019-pooled-contextualized-embeddings-named-entity/) - 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BibTeX from the library's record of the work. ```bibtex @misc{huang2015bidirectional, title = {{Bidirectional LSTM-CRF Models for Sequence Tagging}}, author = {Huang, Zhiheng and Xu, Wei and Yu, Kai}, year = {2015}, doi = {10.48550/arxiv.1508.01991}, eprint = {1508.01991}, archivePrefix = {arXiv}, url = {https://doi.org/10.48550/arxiv.1508.01991} } ``` - [← previous Better Document-level Sentiment Analysis from RST Discourse Parsing](https://md.research.elamur.ai/free/en/papers/bhatia-2015-better-document-level-sentiment-analysis/) - [next → Bilingual Word Representations with Monolingual Quality in Mind](https://md.research.elamur.ai/free/en/papers/luong-2015-bilingual-word-representations-monolingual-quality/) - [↑ back to papers of 2015](https://md.research.elamur.ai/free/en/papers/2015/) ## References 35 references, 8 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. R. K. Ando and T. Zhang. 2005. A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research (JMLR). [find](https://scholar.google.com/scholar?q=R%2E+K%2E+Ando+and+T%2E+Zhang%2E+2005%2E+A+framework+for+learning+predictive+structures+from+multiple+tasks+and+unlabeled+data%2E+Journal+of+Machine+Learning+Research+%28JMLR%29%2E) 2. M. Boden. 2002. A Guide to Recurrent Neural Networks and Back-propagation. In the Dallas project. [find](https://scholar.google.com/scholar?q=M%2E+Boden%2E+2002%2E+A+Guide+to+Recurrent+Neural+Networks+and+Back%2Dpropagation%2E+In+the+Dallas+project%2E) 3. H. L. Chieu. 2003. Named entity recognition with a maximum entropy approach. Proceedings of CoNLL. [find](https://scholar.google.com/scholar?q=H%2E+L%2E+Chieu%2E+2003%2E+Named+entity+recognition+with+a+maximum+entropy+approach%2E+Proceedings+of+CoNLL%2E) 4. R. Collobert, J. Weston, L. Bottou, M. Karlen, K. Kavukcuoglu and P. Kuksa. 2011. Natural Language Processing (Almost) from Scratch. Journal of Machine Learning Research (JMLR). [find](https://scholar.google.com/scholar?q=R%2E+Collobert%2C+J%2E+Weston%2C+L%2E+Bottou%2C+M%2E+Karlen%2C+K%2E+Kavukcuoglu+and+P%2E+Kuksa%2E+2011%2E+Natural+Language+Processing+%28Almost%29+from+Scratch%2E+Journal+of+Machine+Learning+Research+%28JMLR%29%2E) [our card](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/) 5. J. L. Elman. 1990. Finding structure in time. Cognitive Science. [find](https://scholar.google.com/scholar?q=J%2E+L%2E+Elman%2E+1990%2E+Finding+structure+in+time%2E+Cognitive+Science%2E) 6. J. R. Finkel, T. Grenager, and C. Manning. 2005. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. Proceedings of ACL. [find](https://scholar.google.com/scholar?q=J%2E+R%2E+Finkel%2C+T%2E+Grenager%2C+and+C%2E+Manning%2E+2005%2E+Incorporating+Non%2Dlocal+Information+into+Information+Extraction+Systems+by+Gibbs+Sampling%2E+Proceedings+of+ACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/finkel-2005-incorporating-non-local-information-into/) 7. R. Florian, A. Ittycheriah, H. Jing, and T. Zhang. 2003. Named entity recognition through classifier combination. Proceedings of NAACL-HLT. [find](https://scholar.google.com/scholar?q=R%2E+Florian%2C+A%2E+Ittycheriah%2C+H%2E+Jing%2C+and+T%2E+Zhang%2E+2003%2E+Named+entity+recognition+through+classifier+combination%2E+Proceedings+of+NAACL%2DHLT%2E) [our card](https://md.research.elamur.ai/free/en/papers/florian-2003-named-entity-recognition-through-classifier/) 8. J. Gimenez and L. Marquez. 2004. SVMTool: A general POS tagger generator based on support vector machines. Proceedings of LREC. [find](https://scholar.google.com/scholar?q=J%2E+Gimenez+and+L%2E+Marquez%2E+2004%2E+SVMTool%3A+A+general+POS+tagger+generator+based+on+support+vector+machines%2E+Proceedings+of+LREC%2E) [our card](https://md.research.elamur.ai/free/en/papers/gimenez-2004-svmtool-general-pos-tagger-generator/) 9. A. Graves and J. Schmidhuber. 2005. Framewise Phoneme Classification with Bidirectional LSTM and Other Neural Network Architectures. Neural Networks. [find](https://scholar.google.com/scholar?q=A%2E+Graves+and+J%2E+Schmidhuber%2E+2005%2E+Framewise+Phoneme+Classification+with+Bidirectional+LSTM+and+Other+Neural+Network+Architectures%2E+Neural+Networks%2E) 10. A. Graves, A. Mohamed, and G. Hinton. 2013. Speech Recognition with Deep Recurrent Neural Networks. arxiv. [find](https://scholar.google.com/scholar?q=A%2E+Graves%2C+A%2E+Mohamed%2C+and+G%2E+Hinton%2E+2013%2E+Speech+Recognition+with+Deep+Recurrent+Neural+Networks%2E+arxiv%2E) 11. J. Hammerton. 2003. Named Entity Recognition with Long Short-Term Memory. Proceedings of HLT-NAACL. [find](https://scholar.google.com/scholar?q=J%2E+Hammerton%2E+2003%2E+Named+Entity+Recognition+with+Long+Short%2DTerm+Memory%2E+Proceedings+of+HLT%2DNAACL%2E) 12. S. Hochreiter and J. Schmidhuber. 1997. Long short-term memory. Neural Computation, 9(8):1735-1780. [find](https://scholar.google.com/scholar?q=S%2E+Hochreiter+and+J%2E+Schmidhuber%2E+1997%2E+Long+short%2Dterm+memory%2E+Neural+Computation%2C+9%288%29%3A1735%2D1780%2E) 13. T. Kudo and Y. Matsumoto. 2000. Use of support vector learning for chunk identification. Proceedings of CoNLL. [find](https://scholar.google.com/scholar?q=T%2E+Kudo+and+Y%2E+Matsumoto%2E+2000%2E+Use+of+support+vector+learning+for+chunk+identification%2E+Proceedings+of+CoNLL%2E) 14. T. Kudo and Y. Matsumoto. 2001. Chunking with support vector machines. Proceedings of NAACL-HLT. [find](https://scholar.google.com/scholar?q=T%2E+Kudo+and+Y%2E+Matsumoto%2E+2001%2E+Chunking+with+support+vector+machines%2E+Proceedings+of+NAACL%2DHLT%2E) [our card](https://md.research.elamur.ai/free/en/papers/kudo-2001-chunking-support-vector-machines/) 15. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. Proceedings of ICML. [find](https://scholar.google.com/scholar?q=J%2E+Lafferty%2C+A%2E+McCallum%2C+and+F%2E+Pereira%2E+2001%2E+Conditional+random+fields%3A+Probabilistic+models+for+segmenting+and+labeling+sequence+data%2E+Proceedings+of+ICML%2E) 16. A. McCallum, D. Freitag, and F. Pereira. 2000. Maximum entropy Markov models for information extraction and segmentation. Proceedings of ICML. [find](https://scholar.google.com/scholar?q=A%2E+McCallum%2C+D%2E+Freitag%2C+and+F%2E+Pereira%2E+2000%2E+Maximum+entropy+Markov+models+for+information+extraction+and+segmentation%2E+Proceedings+of+ICML%2E) 17. R. Mcdonald , K. Crammer , and F. Pereira. 2005. Flexible text segmentation with structured multilabel classification. Proceedings of HLT-EMNLP. [find](https://scholar.google.com/scholar?q=R%2E+Mcdonald+%2C+K%2E+Crammer+%2C+and+F%2E+Pereira%2E+2005%2E+Flexible+text+segmentation+with+structured+multilabel+classification%2E+Proceedings+of+HLT%2DEMNLP%2E) 18. G. Mesnil, X. He, L. Deng, and Y. Bengio. 2013. Investigation of recurrent-neural-network architectures and learning methods for language understanding. Proceedings of IN-TERSPEECH. [find](https://scholar.google.com/scholar?q=G%2E+Mesnil%2C+X%2E+He%2C+L%2E+Deng%2C+and+Y%2E+Bengio%2E+2013%2E+Investigation+of+recurrent%2Dneural%2Dnetwork+architectures+and+learning+methods+for+language+understanding%2E+Proceedings+of+IN%2DTERSPEECH%2E) 19. T. Mikolov, M. Karafiat, L. Burget, J. Cernocky, and S. Khudanpur. 2010. Recurrent neural network based language model. INTER-SPEECH. [find](https://scholar.google.com/scholar?q=T%2E+Mikolov%2C+M%2E+Karafiat%2C+L%2E+Burget%2C+J%2E+Cernocky%2C+and+S%2E+Khudanpur%2E+2010%2E+Recurrent+neural+network+based+language+model%2E+INTER%2DSPEECH%2E) 20. T. Mikolov, A. Deoras, D. Povey, L. Burget, J. Eernocky. 2011. Strategies for Training Large Scale Neural Network Language Models. Proceedings of ASRU. [find](https://scholar.google.com/scholar?q=T%2E+Mikolov%2C+A%2E+Deoras%2C+D%2E+Povey%2C+L%2E+Burget%2C+J%2E+Eernocky%2E+2011%2E+Strategies+for+Training+Large+Scale+Neural+Network+Language+Models%2E+Proceedings+of+ASRU%2E) 21. T. Mikolov, I. Sutskever, K. Chen, G. Corrado, and J. Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. Proceedings of NIPS. [find](https://scholar.google.com/scholar?q=T%2E+Mikolov%2C+I%2E+Sutskever%2C+K%2E+Chen%2C+G%2E+Corrado%2C+and+J%2E+Dean%2E+2013%2E+Distributed+Representations+of+Words+and+Phrases+and+their+Compositionality%2E+Proceedings+of+NIPS%2E) 22. A. Passos, V. Kumar, and A. McCallum. 2014. Lexicon Infused Phrase Embeddings for Named Entity Resolution. Proceedings of CoNLL. [find](https://scholar.google.com/scholar?q=A%2E+Passos%2C+V%2E+Kumar%2C+and+A%2E+McCallum%2E+2014%2E+Lexicon+Infused+Phrase+Embeddings+for+Named+Entity+Resolution%2E+Proceedings+of+CoNLL%2E) [our card](https://md.research.elamur.ai/free/en/papers/passos-2014-lexicon-infused-phrase-embeddings-named/) 23. L. R. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE. [find](https://scholar.google.com/scholar?q=L%2E+R%2E+Rabiner%2E+1989%2E+A+tutorial+on+hidden+Markov+models+and+selected+applications+in+speech+recognition%2E+Proceedings+of+the+IEEE%2E) 24. A. Ratnaparkhi. 1996. A maximum entropy model for part-of-speech tagging. Proceedings of EMNLP. [find](https://scholar.google.com/scholar?q=A%2E+Ratnaparkhi%2E+1996%2E+A+maximum+entropy+model+for+part%2Dof%2Dspeech+tagging%2E+Proceedings+of+EMNLP%2E) [our card](https://md.research.elamur.ai/free/en/papers/ratnaparkhi-1996-maximum-entropy-model-part-speech/) 25. F. Sha and F. Pereira. 2003. Shallow parsing with conditional random fields. Proceedings of NAACL. [find](https://scholar.google.com/scholar?q=F%2E+Sha+and+F%2E+Pereira%2E+2003%2E+Shallow+parsing+with+conditional+random+fields%2E+Proceedings+of+NAACL%2E) 26. L. Shen, G. Sara, and A. K. Joshi. 2007. Guided learning for bidirectional sequence classification. Proceedings of ACL. [find](https://scholar.google.com/scholar?q=L%2E+Shen%2C+G%2E+Sara%2C+and+A%2E+K%2E+Joshi%2E+2007%2E+Guided+learning+for+bidirectional+sequence+classification%2E+Proceedings+of+ACL%2E) 27. H. Shen and A. Sarkar. 2005. Voting between multiple data representations for text chunking. Canadian AI. [find](https://scholar.google.com/scholar?q=H%2E+Shen+and+A%2E+Sarkar%2E+2005%2E+Voting+between+multiple+data+representations+for+text+chunking%2E+Canadian+AI%2E) 28. A. Soegaard. 2011. Semi-supervised condensed nearest neighbor for part-of-speech tagging. Proceedings of ACL-HLT. [find](https://scholar.google.com/scholar?q=A%2E+Soegaard%2E+2011%2E+Semi%2Dsupervised+condensed+nearest+neighbor+for+part%2Dof%2Dspeech+tagging%2E+Proceedings+of+ACL%2DHLT%2E) 29. X. Sun. 2014. Structure Regularization for Structured Prediction. Proceedings of NIPS. [find](https://scholar.google.com/scholar?q=X%2E+Sun%2E+2014%2E+Structure+Regularization+for+Structured+Prediction%2E+Proceedings+of+NIPS%2E) 30. X. Sun, L.P. Morency, D. OKanohara and J. Tsujii. 2008. Modeling Latent-Dynamic in Shallow Parsing: A Latent Conditional Model with Improved Inference. Proceedings of COLING. [find](https://scholar.google.com/scholar?q=X%2E+Sun%2C+L%2EP%2E+Morency%2C+D%2E+OKanohara+and+J%2E+Tsujii%2E+2008%2E+Modeling+Latent%2DDynamic+in+Shallow+Parsing%3A+A+Latent+Conditional+Model+with+Improved+Inference%2E+Proceedings+of+COLING%2E) 31. K. Toutanova, D. Klein, C. Manning, and Y. Singer. 2003. Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. Proceedings of HLT-NAACL. [find](https://scholar.google.com/scholar?q=K%2E+Toutanova%2C+D%2E+Klein%2C+C%2E+Manning%2C+and+Y%2E+Singer%2E+2003%2E+Feature%2DRich+Part%2Dof%2DSpeech+Tagging+with+a+Cyclic+Dependency+Network%2E+Proceedings+of+HLT%2DNAACL%2E) [our card](https://md.research.elamur.ai/free/en/papers/toutanova-2003-feature-rich-part-speech-tagging/) 32. M. Wang and C. D. Manning. 2013. Effect of Non-linear Deep Architecture in Sequence Labeling. Proceedings of IJCNLP. [find](https://scholar.google.com/scholar?q=M%2E+Wang+and+C%2E+D%2E+Manning%2E+2013%2E+Effect+of+Non%2Dlinear+Deep+Architecture+in+Sequence+Labeling%2E+Proceedings+of+IJCNLP%2E) 33. P. Xu and R. Sarikaya. 2013. Convolutional neural network based triangular CRF for joint intent detection and slot filling. Proceedings of ASRU. [find](https://scholar.google.com/scholar?q=P%2E+Xu+and+R%2E+Sarikaya%2E+2013%2E+Convolutional+neural+network+based+triangular+CRF+for+joint+intent+detection+and+slot+filling%2E+Proceedings+of+ASRU%2E) 34. K. S. Yao, B. L. Peng, G. Zweig, D. Yu, X. L. Li, and F. Gao. 2014. Recurrent conditional random fields for language understanding. ICASSP. [find](https://scholar.google.com/scholar?q=K%2E+S%2E+Yao%2C+B%2E+L%2E+Peng%2C+G%2E+Zweig%2C+D%2E+Yu%2C+X%2E+L%2E+Li%2C+and+F%2E+Gao%2E+2014%2E+Recurrent+conditional+random+fields+for+language+understanding%2E+ICASSP%2E) 35. K. S. Yao, B. Peng, Y. Zhang, D. Yu, G. Zweig, and Y. Shi. 2014. Spoken Language Understanding using Long Short-Term Memory Neural Networks. IEEE SLT. [find](https://scholar.google.com/scholar?q=K%2E+S%2E+Yao%2C+B%2E+Peng%2C+Y%2E+Zhang%2C+D%2E+Yu%2C+G%2E+Zweig%2C+and+Y%2E+Shi%2E+2014%2E+Spoken+Language+Understanding+using+Long+Short%2DTerm+Memory+Neural+Networks%2E+IEEE+SLT%2E)