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 351–400 of 447 papers

TitleStatusHype
Niyama : Breaking the Silos of LLM Inference Serving—0
Noun Group and Verb Group Identification for Hindi—0
Noun Phrase Chunking for Marathi using Distant Supervision—0
Obituary: George A. Miller—0
One ACT Play: Single Demonstration Behavior Cloning with Action Chunking Transformers—0
On the Hidden Negative Transfer in Sequential Transfer Learning for Domain Adaptation from News to Tweets—0
On the Importance of Ezafe Construction in Persian Parsing—0
Open-Source Boundary-Annotated Corpus for Arabic Speech and Language Processing—0
Optimizing Retrieval-Augmented Generation: Analysis of Hyperparameter Impact on Performance and Efficiency—0
Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning—0
PaCMan : Parallel Corpus Management Workbench—0
Painless Semi-Supervised Morphological Segmentation using Conditional Random Fields—0
ParallelFlow: Parallelizing Linear Transformers via Flow Discretization—0
Passage Segmentation of Documents for Extractive Question Answering—0
Passive-Aggressive Sequence Labeling with Discriminative Post-Editing for Recognising Person Entities in Tweets—0
PathOCL: Path-Based Prompt Augmentation for OCL Generation with GPT-4—0
PCA-Featured Transformer for Jamming Detection in 5G UAV Networks—0
Performance Evaluation of Geospatial Images based on Zarr and Tiff—0
PHACTS about activation-based word similarity effects—0
Phrase Representations for Multiword Expressions—0
PKU\_ICL at SemEval-2017 Task 10: Keyphrase Extraction with Model Ensemble and External Knowledge—0
Predicting Phrase Breaks in Classical and Modern Standard Arabic Text—0
Prepare your video for streaming with Segue—0
Preserving Trees in Minimal Automata—0
Pretraining without Wordpieces: Learning Over a Vocabulary of Millions of Words—0
Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-RankingCode0
Financial Report Chunking for Effective Retrieval Augmented GenerationCode0
Def2Vec: Extensible Word Embeddings from Dictionary DefinitionsCode0
ChuLo: Chunk-Level Key Information Representation for Long Document ProcessingCode0
What's Wrong, Python? -- A Visual Differ and Graph Library for NLP in PythonCode0
A Feature-Rich Vietnamese Named-Entity Recognition ModelCode0
LLM-TA: An LLM-Enhanced Thematic Analysis Pipeline for Transcripts from Parents of Children with Congenital Heart DiseaseCode0
Robust Multilingual Part-of-Speech Tagging via Adversarial TrainingCode0
Experiential Explanations for Reinforcement LearningCode0
Continual General Chunking Problem and SyncMapCode0
Augmenting Neural Networks with First-order LogicCode0
CAG: Chunked Augmented Generation for Google Chrome's Built-in Gemini NanoCode0
Evaluation of Word Vector Representations by Subspace AlignmentCode0
Text Chunking for Document Classification for Urban System Management using Large Language ModelsCode0
Weighted Training for Cross-Task LearningCode0
The AI-KU System at the SPMRL 2013 Shared Task : Unsupervised Features for Dependency ParsingCode0
Evaluating Relaxations of Logic for Neural Networks: A Comprehensive StudyCode0
Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language ModelsCode0
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence LabelingCode0
BIRA: Improved Predictive Exchange Word ClusteringCode0
Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented GenerationCode0
Unsupervised Chunking as Syntactic Structure Induction with a Knowledge-Transfer ApproachCode0
Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?Code0
SEER: Self-Aligned Evidence Extraction for Retrieval-Augmented GenerationCode0
Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic TransformationsCode0
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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