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 276–300 of 447 papers

TitleStatusHype
Semi-supervised sequence tagging with bidirectional language modelsCode0
Semi-supervised Multitask Learning for Sequence LabelingCode1
Verb-Particle Constructions in Questions—0
Identification of Languages in Algerian Arabic Multilingual Documents—0
Neural Models for Sequence ChunkingCode0
Decoding with Finite-State Transducers on GPUs—0
Deep Semi-Supervised Learning with Linguistically Motivated Sequence Labeling Task Hierarchies—0
Learning Succinct Models: Pipelined Compression with L1-Regularization, Hashing, Elias-Fano Indices, and Quantization—0
Using Linguistic Data for English and Spanish Verb-Noun Combination Identification—0
A Novel Fast Framework for Topic Labeling Based on Similarity-preserved Hashing—0
Cross-lingual transfer parser from Hindi to Bengali using delexicalization and chunking—0
Development of a Bengali parser by cross-lingual transfer from Hindi—0
A New Recurrent Neural CRF for Learning Non-linear Edge Features—0
Attending to Characters in Neural Sequence Labeling Models—0
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP TasksCode0
Generalization of metric classification algorithms for sequences classification and labelling—0
Keystroke dynamics as signal for shallow syntactic parsingCode0
Retrieval Term Prediction Using Deep Learning Methods—0
Boundary-based MWE segmentation with text partitioningCode0
Modelling the Combination of Generic and Target Domain Embeddings in a Convolutional Neural Network for Sentence Classification—0
Decomposing Bilexical Dependencies into Semantic and Syntactic Vectors—0
Phrase Representations for Multiword Expressions—0
Discourse Sense Classification from Scratch using Focused RNNsCode0
context2vec: Learning Generic Context Embedding with Bidirectional LSTM—0
Segment-Level Sequence Modeling using Gated Recursive Semi-Markov Conditional Random Fields—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