SOTAVerified

Chunking

Chunking, also known as shallow parsing, identifies continuous spans of tokens that form syntactic units such as noun phrases or verb phrases.

Example:

| Vinken | , | 61 | years | old | | --- | ---| --- | --- | --- | | B-NLP| I-NP | I-NP | I-NP | I-NP |

Papers

Showing 126150 of 447 papers

TitleStatusHype
Benchmarking and Building Long-Context Retrieval Models with LoCo and M2-BERT0
An Experimental Investigation of Part-Of-Speech Taggers for Vietnamese0
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models0
Crowd Prefers the Middle Path: A New IAA Metric for Crowdsourcing Reveals Turker Biases in Query Segmentation0
Cross-View Training for Semi-Supervised Learning0
Bayesian Induction of Bracketing Inversion Transduction Grammars0
An Experimental Comparison of Active Learning Strategies for Partially Labeled Sequences0
Adapting the TTL Romanian POS Tagger to the Biomedical Domain0
Cross-lingual transfer parser from Hindi to Bengali using delexicalization and chunking0
CronusVLA: Transferring Latent Motion Across Time for Multi-Frame Prediction in Manipulation0
Linguistic Resources for Bhojpuri, Magahi and Maithili: Statistics about them, their Similarity Estimates, and Baselines for Three Applications0
Counting What Counts: Decompounding for Keyphrase Extraction0
Coreference Resolution for the Basque Language with BART0
A Web Service for Pre-segmenting Very Long Transcribed Speech Recordings0
A New Recurrent Neural CRF for Learning Non-linear Edge Features0
context2vec: Learning Generic Context Embedding with Bidirectional LSTM0
Automatic Morphological Enrichment of a Morphologically Underspecified Treebank0
A New HOPE: Domain-agnostic Automatic Evaluation of Text Chunking0
ACT-JEPA: Joint-Embedding Predictive Architecture Improves Policy Representation Learning0
AccelGen: Heterogeneous SLO-Guaranteed High-Throughput LLM Inference Serving for Diverse Applications0
Concept-Guided Interpretability via Neural Chunking0
Compound Embedding Features for Semi-supervised Learning0
Combining Top-down and Bottom-up Search for Unsupervised Induction of Transduction Grammars0
A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding0
Analyzing the Impact of Spelling Errors on POS-Tagging and Chunking in Learner English0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACEExact Span F197.3Unverified
2BERT-CRF (Replicated in AdaSeq)Exact Span F197.18Unverified
3ELMo + MAT + Multi-TaskExact Span F197.04Unverified
4CVT+Multi-Task+LargeExact Span F196.98Unverified
5ELMo + Multi-TaskExact Span F196.83Unverified
6FlairExact Span F196.72Unverified
7SeqVATExact Span F195.45Unverified
8Adversarial TrainingExact Span F195.25Unverified
9BiLSTM-CRFExact Span F195.18Unverified
#ModelMetricClaimedVerifiedStatus
1ACEF1 score97.3Unverified
2Flair embeddingsF1 score96.72Unverified
3JMTF1 score95.77Unverified
4Low supervisionF1 score95.57Unverified
5IntNet + BiLSTM-CRFF1 score95.29Unverified
6Suzuki and IsozakiF1 score95.15Unverified
7NCRF++F1 score95.06Unverified
8BI-LSTM-CRF (Senna) (ours)F1 score94.46Unverified
#ModelMetricClaimedVerifiedStatus
1ACEF195Unverified
2Wang et al., 2020F194.4Unverified
3AINF194.04Unverified
#ModelMetricClaimedVerifiedStatus
1Wang et al., 2020F192Unverified
2AINF191.71Unverified
#ModelMetricClaimedVerifiedStatus
1Def2VecAUC93.07Unverified