Pith. sign in

REVIEW 3 major objections 5 minor 51 references

Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace Selection

T0 review · 3 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper claims that a weakly supervised constrained k-means with subspace selection beats prior few-shot NER baselines across all tested settings, using 1–5 labeled sentences per entity type plus unlabeled target text.

desk verdict A genuinely new constrained-k-means core for few-shot NER, but the headline results lean on an unprincipled choice of the O-ratio hyperparameter and some unfair baseline comparisons; worth a serious referee, not acceptance as-is. read the letter →

arxiv 2412.00426 v2 pith:2O4C6BZI submitted 2024-11-30 cs.CL

Ayoub Hammal , Benno Uthayasooriyar , Caio Corro This is my paper · ORCID
classification cs.CL
keywords few-shotnamedentityrecognitionweaklysupervisedclusteringconstrainedk-meanssubspaceselectionratioconstraintsdomainadaptationdeterministicoptimizationunlabeleddata
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Few-shot named-entity recognition normally relies on a handful of labeled sentences and often stops there. This paper argues that large amounts of unlabeled target text can be put to work by reframing fine-tuning as a constrained clustering problem: cluster word representations into entity-type prototypes while enforcing how many words may fall outside mentions, and simultaneously learn a projection that separates the clusters. The authors claim this weakly supervised procedure outperforms nearest-neighbor and contrastive baselines in both tag-set extension and domain-transfer settings on English benchmarks. If the claim holds, few-shot NER becomes cheaper and more reproducible, since the optimization is deterministic and has no learning-rate or temperature hyperparameters to tune.

What carries the argument

The central object is a constrained k-means objective over contextual word representations, with assignment matrix A, prototype centroids C, and projection U. The E-step is reformulated as a bipartite graph contraction: for hard assignments, enforcing the O-ratio reduces to picking the n×rO smallest entries of a penalized distance vector, solved in O(nk+n log n); for soft assignments, the same constraint is enforced by iterative Bregman projections in O(nk) per iteration. The M-step is unchanged. The U update solves a generalized eigenvalue problem between within-class and total scatter matrices under the constraint $UᵀS^{{(t)}}$U = I; the projection dimension is set to k−1, the rank of the between-class scatter. This machinery is what lets small labeled sets and cheap unlabeled text jointly determine the tag prototypes.

What would settle it

Run the 5-shot WNUT17 setting with only the development set as unlabeled data and an outside-word ratio of 0.95: if the average F1 does not beat the strongest published baseline of 32.5, the paper's headline comparison fails on that setting. Separately, check the rank of the total scatter matrix on that corpus; a rank below the embedding dimension would show the projection step is not well defined as derived.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that joint constrained k-means with subspace selection is a practical few-shot NER method: with 1 to 5 labeled sentences per target entity type plus unlabeled target text, the algorithm's hard and soft variants reach higher F1 than prototypical-network, nearest-neighbor, structured-decoding, and contrastive baselines across all experimental settings in the paper. The method treats tag prototypes as cluster centroids, restricts assignments with a few labeled words, imposes a ratio constraint so that roughly a known fraction of words are assigned to the outside (O) group, and learns a linear projection in which those clusters are well separated. The gains are largest in tag-set extension, and on WNUT17 5-shot domain transfer the best configuration improves reported F1 from 32.5 to 39.0.

Load-bearing premise

The derivation assumes the unlabeled target text has far more words than the 768-dimensional embedding dimension; the method can become numerically ill-posed on small target corpora where this fails.

Editorial extensions

If this is right

  • The method requires no learning-rate, temperature, or other hyperparameter tuning during fine-tuning, because the optimization is deterministic and parameter-free.
  • Unlabeled target text can substitute for annotated support: on WNUT17 5-shot, using a realistic outside-word ratio raises F1 from 26.5 (no ratio) to 39.0.
  • The same weakly supervised procedure covers both tag-set extension and input-domain transfer, so a single algorithm can serve two distinct few-shot adaptation scenarios.
  • When the outside-word ratio is uncertain, the soft-assignment variant is the safer choice, since it is less sensitive to a misspecified ratio than hard assignment.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Editorial inference: the constraint-and-projection machinery should transfer to other token-level classification tasks, such as slot filling or event detection, whenever unlabeled in-domain text and an approximate outside-class ratio are available.
  • Editorial inference: because the U update requires an invertible total scatter matrix, small target corpora could be handled by replacing the inverse with a pseudo-inverse or adding shrinkage; the paper does not explore this, but it follows directly from the derivation.
  • Editorial inference: the algorithm is transductive in spirit, optimizing on the target unlabeled set at training time, so it is naturally suited to settings where test-time text may be used; the authors list transductive learning only as future work.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The manuscript proposes a weakly-supervised few-shot NER method that combines constrained k-means with a learned linear subspace projection. Support labels are injected as assignment constraints, an expected O-ratio constraint is enforced in the E-step, and the projection is updated through a generalized eigenvalue problem with a total-scatter normalization. The optimization is deterministic and parameter-free in the sense of having no learning-rate or temperature parameters. The paper evaluates on OntoNotes5 tag-set extension and on domain transfer to CoNLL2003 and WNUT17, reporting large F1 gains over prototypical, nearest-neighbor, structured-prediction, and contrastive baselines in 1-shot and 5-shot settings.

Significance. If the reported gains are robust, the paper contributes a practical weakly-supervised few-shot NER method: it exploits cheap unlabeled target data, is deterministic and reproducible, has public code, and offers polynomial-time E-steps for ratio constraints. The domain-transfer experiment in Table 2 is a controlled comparison on shared support sets and is the strongest evidence in the paper. However, the headline claim of consistent superiority over baselines is currently supported only by rows that use hand-picked O-ratio values, and the tag-set-extension comparison mixes support sets, so the significance of the empirical contribution is conditional on fixing these issues.

major comments (3)
  1. [§6.2 and Tables 1-2] The central claim that the method 'performs considerably better than other baseline approaches across all experimental settings' rests on rows selected with hand-picked values of rO. The best domain-transfer rows in Table 2 use rO=0.85/0.95, and the tag-set-extension rows in Table 1 use rO=0.95/0.96/0.93, but the paper gives no protocol for choosing rO from the support or from unlabeled data alone. The few-shot setting assumes no development set, as the paper itself argues for the temperature parameter in §5. Under the alternative rO=0.80/0.90 in Table 2, the 5-shot hard-clustering average is 50.6 F1, below CONTaiNER+Viterbi's 54.2. Reporting the best of two manually chosen rO values is therefore equivalent to tuning on the test set, and the practical superiority claim is not established. The §1 statement that the optimization is 'parameter-free' is also misleading because rO and the number of O clusters (fixed to 10 in §6) are user-set hyperparameters.
  2. [§6.1 and Table 1] The tag-set-extension comparison is not controlled. The baselines marked with a dagger are taken from Das et al. (2022) and were evaluated on different support sets than the proposed method, as the table caption itself states. The authors report their own NNShot and StructShot reproductions on their support sets, but not the CONTaiNER baseline, so the large apparent gains in Table 1 (for example, 5-shot average 61.3 versus 56.2 for CONTaiNER) cannot be attributed to the algorithm rather than to the support-set draw. The domain-transfer experiment in Table 2 uses shared support sets and is controlled, but it alone cannot support the claim of superiority across all experimental settings.
  3. [§4.2 and Appendix E] The derivation of the subspace projection requires the total scatter matrix S_t to be invertible, and the paper asserts in §4.2 that n >> d 'is the case in practice' without verification. This assumption is satisfied for the main domain-transfer protocol, where the unlabeled CoNLL2003 and WNUT17 train+dev sets are much larger than the 768-dimensional BERT embedding, so the stress-test concern about WNUT17 does not land for the headline tables. However, the support-only and dev-set-only rows in Table 3 are not obviously in this regime, and the generalized eigenvalue problem in Eq. (14) and the equivalent form in Eq. (15) are undefined when S_t is singular. The paper should state the rank condition explicitly at the point where p is set to k-1, and should either verify n >> d for each experimental configuration or provide a regularized fallback such as a pseudo-inverse.
minor comments (5)
  1. [Appendix B] Definition 1 misspells 'Bregman divergence' as 'Bregram divergence'.
  2. [§4.1] The scatter-matrix definitions contain the typo 'n[n]' in the summation index; the intended expression is 'i in [n]'.
  3. [§3.2] The hard-ratio E-step treats n x rO as an integer cardinality, but the paper does not specify how to round when the product is not integral.
  4. [Appendix B] The phrase 'by k-periodicity' reuses k, which already denotes the number of prototypes and also the number of sets in the projection sequence; this makes the periodicity statement confusing and should be reworded.
  5. [§1] The sentence 'our method ... overcome limitations of previous work' has a subject-verb agreement error and should read 'overcomes'.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity in the derivation chain; the rO selection caveat is an evaluation-validity concern, not a definitional reduction.

full rationale

The paper's derivation is self-contained. The method is defined as an explicit optimization problem in Sections 3 and 4: constrained k-means with a ratio constraint on the O tag and a jointly learned subspace projection. The E-step reductions, the M-step centroid update, and the projection update are all derived from first-order optimality conditions and standard linear algebra; in particular, Equation (14) follows from stationarity of the Lagrangian, Equation (15) uses only the stated full-rank assumption on S_t, and the choice p = k-1 follows from the rank of S_b. Appendix C.1 proves the scatter-matrix identity S_t = S_w + S_b using the centroid optimality condition (Equation 9), and Appendix A shows the prototype-based tag weights are equivalent to a linear model, so using Equation (2) is not a hidden reuse of the predicted quantity. The central empirical claim is benchmark F1, not a quantity constructed from the method's own inputs. The paper explicitly acknowledges the ratio-constraint uncertainty: 'As we cannot assume to know the true ratio of O tags, we evaluate our approach with both under- and over-estimations' (Section 6). Reporting several rO rows and highlighting the best one is a test-set-selection and external-validity concern, not a circular derivation, because rO is not fitted from a subset and then renamed as a prediction. There are no load-bearing self-citations and no fitted parameter is presented as a first-principles result. Therefore, under the stated circularity criteria, the derivation is not circular.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The method rests primarily on two modeling choices: the ratio constraint rO (a user-supplied free parameter) and the subspace normalization U^T S_t U = I. The full-rank assumption on S_t is an unstated fragility. No new physical or conceptual entities are introduced beyond the projection matrix and prototypes, which are model parameters.

free parameters (4)
  • rO (expected ratio of O tags) = 0.95, 0.96, 0.93 for tag sets A, B, C; 0.85, 0.95 for CoNLL, WNUT17
    Set close to the true proportion of O words in the target dev/train data; performance varies noticeably with this choice, as shown by the NA and under/over estimates in Tables 1-2.
  • Number of O clusters = 10
    Fixed by hand; the paper does not explore sensitivity to this hyperparameter.
  • Number of alternating iterations = 10
    Fixed at 10; no stopping rule or convergence check is discussed.
  • Bregman iteration count for soft E-step = not specified
    The soft assignment algorithm in Appendix B requires an iteration count or convergence threshold, which is not reported.
assumptions (5)
  • ad hoc to paper Total scatter normalization U^T S_t U = I prevents the trivial U = 0 collapse.
    The constraint in Eq. (13) is a modeling choice introduced to avoid the degenerate optimum; it is not derived from the task and restricts the search space.
  • domain assumption The total scatter matrix S_t is full rank, requiring n >> d.
    Section 4.2 and Appendix E invert S_t to compute the projection; this may fail for small target datasets where n is comparable to the embedding dimension.
  • domain assumption Words of the same entity type form compact, separable clusters in some linear subspace of BERT embeddings.
    The whole clustering approach depends on the clustering assumption being a good proxy for the NER tagging objective.
  • standard math Iterative Bregman projections converge to the intersection of the affine sets S(1), S(2), S(3).
    Appendix B relies on this convergence for the soft E-step with ratio constraint.
  • domain assumption The hard E-step constraint sum_i a_i = n*rO is feasible for binary a, i.e., n*rO is an integer.
    The optimization in Section 3.2 requires an integer number of O assignments; the paper does not address rounding when n*rO is not integral.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace Selection." pith.science (2026). https://pith.science/paper/2O4C6BZI

@misc{pith2026241200426,
  author       = {Pith},
  title        = {Pith review of: Few-Shot Domain Adaptation for Named-Entity Recognition via Joint Constrained k-Means and Subspace Selection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2O4C6BZI}},
  note         = {Machine review of arXiv:2412.00426}
}
read the original abstract

Named-entity recognition (NER) is a task that typically requires large annotated datasets, which limits its applicability across domains with varying entity definitions. This paper addresses few-shot NER, aiming to transfer knowledge to new domains with minimal supervision. Unlike previous approaches that rely solely on limited annotated data, we propose a weakly supervised algorithm that combines small labeled datasets with large amounts of unlabeled data. Our method extends the k-means algorithm with label supervision, cluster size constraints and domain-specific discriminative subspace selection. This unified framework achieves state-of-the-art results in few-shot NER on several English datasets.

Figures

Figures reproduced from arXiv: 2412.00426 by the authors.

Figure 2
Figure 2. Illustration of the benefit of subspace selection. (top) Data in its original 2D space. We assume the constrained clustering results in two clusters: one containing the two black crosses and the other containing the two red circles. Let the green star be a test point. In￾tuitively, it should be classified in the black crosses cluster, however, it is closer to the other cluster centroid! (bottom) Data after projectio… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

51 extracted references · 28 canonical work pages

  1. [1]

    Daniel Aloise, Amit Deshpande, Pierre Hansen, and Preyas Popat. 2009. https://doi.org/10.1007/s10994-009-5103-0 Np-hardness of euclidean sum-of-squares clustering . Machine Learning, 75(2):245–248

    doi:10.1007/s10994-009-5103-0 2009

  2. [2]

    Tayeb Belghiti, and Pham Dinh Tao

    Le Thi Hoai An, M. Tayeb Belghiti, and Pham Dinh Tao. 2006. https://doi.org/10.1007/s10898-006-9066-4 A new efficient algorithm based on dc programming and dca for clustering . Journal of Global Optimization, 37(4):593–608

    work page doi:10.1007/s10898-006-9066-4 2006

  3. [3]

    Amir Beck. 2017. First-order methods in optimization. SIAM

    work page 2017

  4. [4]

    Jean-David Benamou, Guillaume Carlier, Marco Cuturi, Luca Nenna, and Gabriel Peyr\' e . 2015. https://doi.org/10.1137/141000439 Iterative bregman projections for regularized transportation problems . SIAM Journal on Scientific Computing, 37(2):A1111--A1138

    doi:10.1137/141000439 2015

  5. [5]

    James C. Bezdek. 1981. Pattern Recognition with Fuzzy Objective Function Algorithms. Kluwer Academic Publishers, USA

    work page 1981

  6. [6]

    Martins, and Vlad Niculae

    Mathieu Blondel, André F.T. Martins, and Vlad Niculae. 2020. http://jmlr.org/papers/v21/19-021.html Learning with fenchel-young losses . Journal of Machine Learning Research, 21(35):1--69

    2020

  7. [7]

    L \'e on Bottou. 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010, pages 177--186, Heidelberg. Physica-Verlag HD

    work page 2010

  8. [8]

    Paul S Bradley, Kristin P Bennett, and Ayhan Demiriz. 2000. Constrained k-means clustering. Microsoft Research, Redmond, 20(0):0

    work page 2000

Show all 51 references
  1. [9]

    Paul S Bradley and Usama M Fayyad. 1998. Refining initial points for k-means clustering. In ICML, volume 98, pages 91--99. Citeseer

    1998

  2. [10]

    Lev M Bregman. 1967. The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming. USSR computational mathematics and mathematical physics, 7(3):200--217

    1967

  3. [11]

    Yair Censor and Stavros Andrea Zenios. 1997. Parallel optimization: Theory, algorithms, and applications. Oxford University Press, USA

    1997

  4. [12]

    Yanru Chen, Yanan Zheng, and Zhilin Yang. 2023. https://doi.org/10.18653/v1/2023.findings-acl.451 Prompt-based metric learning for few-shot NER . In Findings of the Association for Computational Linguistics: ACL 2023, pages 7199--7212, Toronto, Canada. Association for Computat...

    2023 doi

  5. [13]

    Chinchor and P

    N. Chinchor and P. Robinson. 1998. https://aclanthology.org/M98-1028 Appendix E : MUC -7 named entity task definition (version 3.5) . In Seventh Message Understanding Conference ( MUC -7): Proceedings of a Conference Held in Fairfax, Virginia, A pril 29 - May 1, 1998

    1998

  6. [14]

    Pierre Colombo, Victor Pellegrain, Malik Boudiaf, Myriam Tami, Victor Storchan, Ismail Ayed, and Pablo Piantanida. 2023. https://doi.org/10.18653/v1/2023.emnlp-main.257 Transductive learning for textual few-shot classification in API -based embedding models . In Proceedings of...

    2023 doi

  7. [15]

    Sarkar Snigdha Sarathi Das, Arzoo Katiyar, Rebecca Passonneau, and Rui Zhang. 2022. https://doi.org/10.18653/v1/2022.acl-long.439 CONT ai NER : Few-shot named entity recognition via contrastive learning . In Proceedings of the 60th Annual Meeting of the Association for Computa...

    2022 doi

  8. [16]

    Sanjoy Dasgupta. 2008. The hardness of k-means clustering. Technical report, Department of Computer Science and Engineering, University of California, San Diego

    2008

  9. [17]

    Fernando De la Torre and Takeo Kanade. 2006. https://doi.org/10.1145/1143844.1143875 Discriminative cluster analysis . In Proceedings of the 23rd International Conference on Machine Learning, ICML '06, page 241–248, New York, NY, USA. Association for Computing Machinery

    2006

  10. [18]

    Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham. 2017. https://doi.org/10.18653/v1/W17-4418 Results of the WNUT 2017 shared task on novel and emerging entity recognition . In Proceedings of the 3rd Workshop on Noisy User-generated Text, pages 140--147, Cope...

    2017 doi

  11. [19]

    Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. https://doi.org/10.18653/v1/N19-1423 BERT : Pre-training of deep bidirectional transformers for language understanding . In Proceedings of the 2019 Conference of the North A merican Chapter of the Associat...

    2019 doi

  12. [20]

    Chris Ding and Tao Li. 2007. https://doi.org/10.1145/1273496.1273562 Adaptive dimension reduction using discriminant analysis and k-means clustering . In Proceedings of the 24th International Conference on Machine Learning, ICML '07, page 521–528, New York, NY, USA. Associatio...

    2007

  13. [21]

    Richard O Duda, Peter E Hart, and David G Stork. 2000. Pattern Classification, 2 edition. A Wiley-Interscience publication. John Wiley & Sons, Nashville, TN

    2000

  14. [22]

    J. C. Dunn. 1973. https://doi.org/10.1080/01969727308546046 A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters . Journal of Cybernetics, 3(3):32--57

    1973 doi

  15. [23]

    Thomas Effland and Michael Collins. 2021. https://doi.org/10.1162/tacl_a_00429 Partially supervised named entity recognition via the expected entity ratio loss . Transactions of the Association for Computational Linguistics, 9:1320--1335

    2021 doi

  16. [24]

    Alexander Fritzler, Varvara Logacheva, and Maksim Kretov. 2019. https://doi.org/10.1145/3297280.3297378 Few-shot classification in named entity recognition task . In Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing, SAC '19, page 993–1000, New York, NY, USA. A...

    2019

  17. [25]

    Kuzman Ganchev, João Graça, Jennifer Gillenwater, and Ben Taskar. 2010. http://jmlr.org/papers/v11/ganchev10a.html Posterior regularization for structured latent variable models . Journal of Machine Learning Research, 11(67):2001--2049

    2010

  18. [26]

    Benyamin Ghojogh, Fakhri Karray, and Mark Crowley. 2023. https://arxiv.org/abs/1903.11240 Eigenvalue and generalized eigenvalue problems: Tutorial . Preprint, arXiv:1903.11240

    2023 arXiv

  19. [27]

    Gene H Golub and Charles F Van Loan. 2013. Matrix computations. JHU press

    2013

  20. [28]

    Ralph Grishman and Beth Sundheim. 1996. https://aclanthology.org/C96-1079 M essage U nderstanding C onference- 6: A brief history . In COLING 1996 Volume 1: The 16th International Conference on Computational Linguistics

    1996

  21. [29]

    Trevor Hastie, Robert Tibshirani, and Jerome Friedman. 2009. https://doi.org/10.1007/978-0-387-84858-7 The Elements of Statistical Learning . Springer New York

    2009 doi

  22. [30]

    Trevor Hastie, Robert Tibshirani, and Martin Wainwright. 2015. Statistical learning with sparsity. Monographs on statistics and applied probability, 143(143):8

    2015

  23. [31]

    Yutai Hou, Wanxiang Che, Yongkui Lai, Zhihan Zhou, Yijia Liu, Han Liu, and Ting Liu. 2020. https://doi.org/10.18653/v1/2020.acl-main.128 Few-shot slot tagging with collapsed dependency transfer and label-enhanced task-adaptive projection network . In Proceedings of the 58th An...

    2020 doi

  24. [32]

    Rong Jin and Zoubin Ghahramani. 2002. https://proceedings.neurips.cc/paper_files/paper/2002/file/653ac11ca60b3e021a8c609c7198acfc-Paper.pdf Learning with multiple labels . In Advances in Neural Information Processing Systems, volume 15. MIT Press

    2002

  25. [33]

    John Lafferty, Andrew McCallum, Fernando Pereira, et al. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, volume 1, page 3

    2001

  26. [34]

    Andrew McCallum and Wei Li. 2003. https://aclanthology.org/W03-0430 Early results for named entity recognition with conditional random fields, feature induction and web-enhanced lexicons . In Proceedings of the Seventh Conference on Natural Language Learning at HLT - NAACL 200...

    2003

  27. [35]

    Michael K Ng. 2000. A note on constrained k-means algorithms. Pattern Recognition, 33(3):515--519

    2000

  28. [36]

    Beresford N. Parlett. 1998. https://doi.org/10.1137/1.9781611971163 The Symmetric Eigenvalue Problem . Society for Industrial and Applied Mathematics

    1998 doi

  29. [37]

    Lance Ramshaw and Mitch Marcus. 1995. https://aclanthology.org/W95-0107 Text chunking using transformation-based learning . In Third Workshop on Very Large Corpora

    1995

  30. [38]

    Tjong Kim Sang and Fien De Meulder

    Erik F. Tjong Kim Sang and Fien De Meulder. 2003. https://aclanthology.org/W03-0419 Introduction to the C o NLL -2003 shared task: Language-independent named entity recognition . In Proceedings of the Seventh Conference on Natural Language Learning at HLT - NAACL 2003 , pages 142--147

    2003

  31. [39]

    Sunita Sarawagi and William W Cohen. 2004. https://proceedings.neurips.cc/paper_files/paper/2004/file/eb06b9db06012a7a4179b8f3cb5384d3-Paper.pdf Semi-markov conditional random fields for information extraction . In Advances in Neural Information Processing Systems, volume 17. ...

    2004

  32. [40]

    Jake Snell, Kevin Swersky, and Richard Zemel. 2017. https://proceedings.neurips.cc/paper_files/paper/2017/file/cb8da6767461f2812ae4290eac7cbc42-Paper.pdf Prototypical networks for few-shot learning . In Advances in Neural Information Processing Systems, volume 30. Curran Assoc...

    2017

  33. [41]

    Amber Stubbs and Özlem Uzuner. 2015. https://doi.org/10.1016/j.jbi.2015.07.020 Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/uthealth corpus . Journal of Biomedical Informatics, 58:S20--S29. Supplement: Proceedings of the 2014 i2b2/UTHealth S...

    2015 doi

  34. [42]

    Torr, and Timothy M

    Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H.S. Torr, and Timothy M. Hospedales. 2018. Learning to compare: Relation network for few-shot learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

    2018

  35. [43]

    Oriol Vinyals, Charles Blundell, Timothy Lillicrap, koray kavukcuoglu, and Daan Wierstra. 2016. https://proceedings.neurips.cc/paper_files/paper/2016/file/90e1357833654983612fb05e3ec9148c-Paper.pdf Matching networks for one shot learning . In Advances in Neural Information Pro...

    2016

  36. [44]

    Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013. Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia, PA

    2013

  37. [45]

    Yi Yang and Arzoo Katiyar. 2020. https://doi.org/10.18653/v1/2020.emnlp-main.516 Simple and effective few-shot named entity recognition with structured nearest neighbor learning . In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)...

    2020 doi

  38. [46]

    Jieping Ye, Zheng Zhao, and Huan Liu. 2007 a . https://doi.org/10.1109/CVPR.2007.383103 Adaptive distance metric learning for clustering . In 2007 IEEE Conference on Computer Vision and Pattern Recognition, pages 1--7

    2007

  39. [47]

    Jieping Ye, Zheng Zhao, and Mingrui Wu. 2007 b . https://proceedings.neurips.cc/paper_files/paper/2007/file/a5cdd4aa0048b187f7182f1b9ce7a6a7-Paper.pdf Discriminative k-means for clustering . In Advances in Neural Information Processing Systems, volume 20. Curran Associates, Inc

    2007

  40. [48]

    Sung Whan Yoon, Jun Seo, and Jaekyun Moon. 2019. https://proceedings.mlr.press/v97/yoon19a.html T ap N et: Neural network augmented with task-adaptive projection for few-shot learning . In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proce...

    2019

  41. [49]

    Urchade Zaratiana, Nadi Tomeh, Pierre Holat, and Thierry Charnois. 2024. https://doi.org/10.18653/v1/2024.naacl-long.300 GL i NER : Generalist model for named entity recognition using bidirectional transformer . In Proceedings of the 2024 Conference of the North American Chapt...

    2024 doi

  42. [50]

    online" 'onlinestring :=

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint eprinttype howpublished institution journal key month note number organization pages publisher school series title type volume year doi pubmed url lastchecked label extra.label sort.label short.list...

  43. [51]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.