| Challenge: | Existing methods for sequence labeling tasks such as Named Entity Recognition (NER) suffer from quadratic complexity over sequence length and poor performance compared to CRF. |
| Approach: | They propose a variant of Semi-Markov CRF that incorporates a filtering step to eliminate irrelevant segments, reducing complexity and search space. |
| Outcome: | The proposed method outperforms both CRF and Semi-CRF on several NER benchmarks while being significantly faster. |
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| Challenge: | Existing conditional random fields (CRFs) use hand-crafted features to perform sequence labeling tasks. |
| Approach: | They propose to use semi-Markov conditional random fields for neural sequence labeling in natural language processing to extract features from segments instead of words. |
| Outcome: | The proposed model achieves state-of-the-art when no external knowledge is used. |
Neural semi-Markov CRF for Monolingual Word Alignment (2021.acl-long)
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| Challenge: | Experimental results show that our proposed model outperforms all previous approaches for monolingual word alignment. |
| Approach: | They propose a neural semi-Markov CRF alignment model which unifies word and phrase alignments through variable-length spans. |
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Towards Improving Neural Named Entity Recognition with Gazetteers (P19-1)
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| Challenge: | Currently, neural models for named entity recognition are based on data-driven models, with a strong emphasis on getting rid of the efforts for collecting external resources or designing hand-crafted features. |
| Approach: | They propose to use external gazetteers to efficiently access annotated data to generalize beyond the annotation of entities. |
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Constrained Decoding for Computationally Efficient Named Entity Recognition Taggers (2020.findings-emnlp)
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| Challenge: | Named entity recognition models use a conditional random field as the final layer . current work eschews prior knowledge of how the span encoding scheme works . |
| Approach: | They propose to constrain the output to suppress illegal transitions to train a tagger with a cross-entropy loss twice as fast as a CRF. |
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Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors (2020.emnlp-main)
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| Challenge: | Recent advances in NLP focus on simple approaches to model the output label space . graphical models are often limited to (heuristic) greedy search and its variants . |
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A Neural CRF-based Hierarchical Approach for Linear Text Segmentation (2023.findings-eacl)
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Inderjeet Nair, Aparna Garimella, Balaji Vasan Srinivasan, Natwar Modani, Niyati Chhaya, Srikrishna Karanam, Sumit Shekhar
| Challenge: | Existing methods to segment unformatted text and transcripts explicitly train to predict segment boundaries, but they fail to provide a large annotated dataset. |
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A Unified MRC Framework for Named Entity Recognition (2020.acl-main)
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| Challenge: | Named entity recognition is divided into nested NER and flat NER depending on whether entities are nesting. |
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| Outcome: | The proposed framework achieves vast amount of performance boost over current models on nested and flat NER datasets. |
Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging (N19-1)
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| Challenge: | Character-level models of tokens are effective at dealing with within-token noise and out-of-vocabulary words. |
| Approach: | They propose to eliminate the need for tokenizers by using a character-level semi-Markov conditional random field that uses neural networks for its character and segment representations. |
| Outcome: | The proposed model outperforms state-of-the-art part-of speech taggers on a noisy English dataset. |
Simple Yet Powerful: An Overlooked Architecture for Nested Named Entity Recognition (2022.coling-1)
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| Challenge: | Named Entity Recognition (NER) is an important task in Natural Language Processing that aims to identify text spans belonging to predefined categories. |
| Approach: | They propose to revisit the Multiple LSTM-CRF (MLC) model, a simple, overlooked, yet powerful approach based on training independent sequence labeling models for each entity type. |
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Bregman Conditional Random Fields: Sequence Labeling with Parallelizable Inference Algorithms (2025.acl-long)
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| Challenge: | Existing methods for sequence labeling are hidden Markov models and conditional random fields (CRF). |
| Approach: | They propose a new discriminative model for sequence labeling called Bregman conditional random fields (BCRF) they propose to use Fenchel-Young losses to learn from partial labels. |
| Outcome: | The proposed model performs better in highly constrained settings than the existing model, which is slower and faster. |