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 201–225 of 447 papers

TitleStatusHype
SeqVAT: Virtual Adversarial Training for Semi-Supervised Sequence Labeling—0
Continual General Chunking Problem and SyncMapCode0
Neural Simultaneous Speech Translation Using Alignment-Based Chunking—0
Recurrent Chunking Mechanisms for Long-Text Machine Reading ComprehensionCode1
New Ideas for Brain Modelling 6—0
The Structured Weighted Violations MIRACode0
Learning Architectures from an Extended Search Space for Language Modeling—0
Linguistic Resources for Bhojpuri, Magahi and Maithili: Statistics about them, their Similarity Estimates, and Baselines for Three Applications—0
Capturing Global Informativeness in Open Domain Keyphrase ExtractionCode1
CmnRec: Sequential Recommendations with Chunk-accelerated Memory NetworkCode0
Scalable Multilingual Frontend for TTS—0
Caption Generation of Robot Behaviors based on Unsupervised Learning of Action Segments—0
Improving cross-lingual model transfer by chunking—0
A Data Efficient End-To-End Spoken Language Understanding Architecture—0
At a Glance: The Impact of Gaze Aggregation Views on Syntactic Tagging—0
Gated Task Interaction Framework for Multi-task Sequence TaggingCode0
Language-Agnostic Syllabification with Neural Sequence LabelingCode0
NarrativeTime: Dense Temporal Annotation on a Timeline—0
Position-Aware Self-Attention based Neural Sequence Labeling—0
Artificially Evolved Chunks for Morphosyntactic Analysis—0
Chunker diff\'erents types de discours oraux : d\'efis pour l'apprentissage automatique (Chunking different spoken speech types : challenges for machine learning)—0
Bayes Test of Precision, Recall, and F1 Measure for Comparison of Two Natural Language Processing Models—0
Augmenting Neural Networks with First-order LogicCode0
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence LabelingCode0
SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling—0
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All datasetsCoNLL-2000Penn TreebankCoNLL 2003 (German)CoNLL 2003 (English)CoNLL 2003

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