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 426–447 of 447 papers

TitleStatusHype
Deep multi-task learning with low level tasks supervised at lower layers—0
Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies—0
Deep Unsupervised Feature Learning for Natural Language Processing—0
Dependency Based Embeddings for Sentence Classification Tasks—0
Detecting Japanese Compound Functional Expressions using Canonical/Derivational Relation—0
Development of a Bengali parser by cross-lingual transfer from Hindi—0
Discovering Chunks in Neural Embeddings for Interpretability—0
Discriminative Lexical Semantic Segmentation with Gaps: Running the MWE Gamut—0
Dissecting Span Identification Tasks with Performance Prediction—0
DM\_NLP at SemEval-2018 Task 8: neural sequence labeling with linguistic features—0
Document Chunking and Learning Objective Generation for Instruction Design—0
Domain Adaptation with Filtering for Named Entity Extraction of Japanese Anime-Related Words—0
DTSim at SemEval-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional Semantics—0
DTSim at SemEval-2016 Task 2: Interpreting Similarity of Texts Based on Automated Chunking, Chunk Alignment and Semantic Relation Prediction—0
Duluth: Word Sense Discrimination in the Service of Lexicography—0
Dynamic Chunking for End-to-End Hierarchical Sequence Modeling—0
Effect of Non-linear Deep Architecture in Sequence Labeling—0
Efficient Higher-Order CRFs for Morphological Tagging—0
Enhance Robustness of Sequence Labelling with Masked Adversarial Training—0
Position-Aware Self-Attention based Neural Sequence Labeling—0
Enhancing Talent Employment Insights Through Feature Extraction with LLM Finetuning—0
Enrichir et raisonner sur des espaces s\'emantiques pour l'attribution de mots-cl\'es (Enriching and reasoning on semantic spaces for keyword extraction) [in French]—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