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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 251–275 of 447 papers

TitleStatusHype
Cross-View Training for Semi-Supervised Learning—0
Analyzing the Impact of Spelling Errors on POS-Tagging and Chunking in Learner English—0
Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?Code0
A Sequential Neural Encoder with Latent Structured Description for Modeling Sentences—0
Robust Multilingual Part-of-Speech Tagging via Adversarial TrainingCode0
Segment-Level Neural Conditional Random Fields for Named Entity Recognition—0
Review highlights: opinion mining on reviews: a hybrid model for rule selection in aspect extractionCode1
Linear-Time Sequence Classification using Restricted Boltzmann Machines—0
A Unified Framework for Structured Prediction: From Theory to Practice—0
Annotation of Clinical Narratives in Bulgarian language—0
Adapting the TTL Romanian POS Tagger to the Biomedical Domain—0
The Component Unit. Introducing a Novel Unit of Syntactic Analysis—0
Chinese Descriptive and Resultative V-de Constructions. A Dependency-based Analysis—0
Hierarchical Word Structure-based Parsing: A Feasibility Study on UD-style Dependency Parsing in Japanese—0
Realization of long sentences using chunking—0
Boundary-based MWE segmentation with text partitioning—0
Exploiting Morphological Regularities in Distributional Word Representations—0
Learning the Structure of Variable-Order CRFs: a finite-state perspective—0
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings—0
NNVLP: A Neural Network-Based Vietnamese Language Processing ToolkitCode0
PKU\_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge—0
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling TasksCode1
An Algebra for Feature Extraction—0
Improving Scalability of Inductive Logic Programming via Pruning and Best-Effort Optimisation—0
Un \'etiqueteur en ligne du Fran (An online tagger for French)—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ACEExact Span F197.3—Unverified
2BERT-CRF (Replicated in AdaSeq)Exact Span F197.18—Unverified
3ELMo + MAT + Multi-TaskExact Span F197.04—Unverified
4CVT+Multi-Task+LargeExact Span F196.98—Unverified
5ELMo + Multi-TaskExact Span F196.83—Unverified
6FlairExact Span F196.72—Unverified
7SeqVATExact Span F195.45—Unverified
8Adversarial TrainingExact Span F195.25—Unverified
9BiLSTM-CRFExact Span F195.18—Unverified
#ModelMetricClaimedVerifiedStatus
1ACEF1 score97.3—Unverified
2Flair embeddingsF1 score96.72—Unverified
3JMTF1 score95.77—Unverified
4Low supervisionF1 score95.57—Unverified
5IntNet + BiLSTM-CRFF1 score95.29—Unverified
6Suzuki and IsozakiF1 score95.15—Unverified
7NCRF++F1 score95.06—Unverified
8BI-LSTM-CRF (Senna) (ours)F1 score94.46—Unverified
#ModelMetricClaimedVerifiedStatus
1ACEF195—Unverified
2Wang et al., 2020F194.4—Unverified
3AINF194.04—Unverified
#ModelMetricClaimedVerifiedStatus
1Wang et al., 2020F192—Unverified
2AINF191.71—Unverified
#ModelMetricClaimedVerifiedStatus
1Def2VecAUC93.07—Unverified