Chunking
67 papers with code • 5 benchmarks • 5 datasets
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 |
Benchmarks
These leaderboards are used to track progress in Chunking
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Libraries
Use these libraries to find Chunking models and implementations
Penn Treebank
CoNLL 2003
Latest papers
Query-Based Keyphrase Extraction from Long Documents
Transformer-based architectures in natural language processing force input size limits that can be problematic when long documents need to be processed.
Building Odia Shallow Parser
Shallow parsing is an essential task for many NLP applications like machine translation, summarization, sentiment analysis, aspect identification and many more.
CUSIDE: Chunking, Simulating Future Context and Decoding for Streaming ASR
thu-spmi/cat
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The simulation module is jointly trained with the ASR model using a self-supervised loss; the ASR model is optimized with the usual ASR loss, e. g., CTC-CRF as used in our experiments.
NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
netket/netket
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We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics.
tsflex: flexible time series processing & feature extraction
$\texttt{tsflex}$ is flexible as it supports (1) multivariate time series, (2) multiple window-stride configurations, and (3) integrates with processing and feature functions from other packages, while (4) making no assumptions about the data sampling regularity, series alignment, and data type.
BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications
We propose a system that combines SAD and a BERT model to perform speaker change detection and speaker role detection (SRD) by chunking ASR transcripts, i. e., SD with a defined number of speakers together with SRD.
Paradigm Shift in Natural Language Processing
In this paper, we review such phenomenon of paradigm shifts in recent years, highlighting several paradigms that have the potential to solve different NLP tasks.
Evaluating Relaxations of Logic for Neural Networks: A Comprehensive Study
Symbolic knowledge can provide crucial inductive bias for training neural models, especially in low data regimes.
Large-scale image segmentation based on distributed clustering algorithms
Many approaches to 3D image segmentation are based on hierarchical clustering of supervoxels into image regions.
Weighted Training for Cross-Task Learning
In this paper, we introduce Target-Aware Weighted Training (TAWT), a weighted training algorithm for cross-task learning based on minimizing a representation-based task distance between the source and target tasks.
