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 426–447 of 447 papers

TitleStatusHype
Fast and Robust Part-of-Speech Tagging Using Dynamic Model Selection—0
Subgroup Detector: A System for Detecting Subgroups in Online Discussions—0
Building Trainable Taggers in a Web-based, UIMA-Supported NLP Workbench—0
A Cost Sensitive Part-of-Speech Tagging: Differentiating Serious Errors from Minor Errors—0
La structuration prosodique et les relations syntaxe/ prosodie dans le discours politique (Prosodic Structuring and the Syntax-Prosody Relationship in Political Speech) [in French]—0
Apprentissage automatique d'un chunker pour le fran (Machine Learning of a chunker for French) [in French]—0
Un segmenteur-\'etiqueteur et un chunker pour le fran (A Segmenter-POS Labeller and a Chunker for French) [in French]—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
Deep Unsupervised Feature Learning for Natural Language Processing—0
Detecting Japanese Compound Functional Expressions using Canonical/Derivational Relation—0
A Concise Query Language with Search and Transform Operations for Corpora with Multiple Levels of Annotation—0
Improving the Recall of a Discourse Parser by Constraint-based Postprocessing—0
Open-Source Boundary-Annotated Corpus for Arabic Speech and Language Processing—0
YADAC: Yet another Dialectal Arabic Corpus—0
Predicting Phrase Breaks in Classical and Modern Standard Arabic Text—0
ROMBAC: The Romanian Balanced Annotated Corpus—0
Linguistically-Adapted Structural Query Annotation for Digital Libraries in the Social Sciences—0
PHACTS about activation-based word similarity effects—0
Multi-View Learning of Word Embeddings via CCA—0
Named Entity Recognition in Tweets: An Experimental StudyCode0
Natural Language Processing (almost) from ScratchCode0
Forgetting Exceptions is Harmful in Language Learning—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