Chunking
66 papers with code • 6 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
Opening the black box of language acquisition
However, it is unclear whether or how these models represent grammatical information from the learned languages.
Def2Vec: Extensible Word Embeddings from Dictionary Definitions
Def2Vec introduces a novel paradigm for word embeddings, leveraging dictionary definitions to learn semantic representations.
Fast and Accurate Factual Inconsistency Detection Over Long Documents
We introduce SCALE (Source Chunking Approach for Large-scale inconsistency Evaluation), a task-agnostic model for detecting factual inconsistencies using a novel chunking strategy.
Unsupervised Chunking with Hierarchical RNN
Our approach involves a two-stage training process: pretraining with an unsupervised parser and finetuning on downstream NLP tasks.
Geo-Encoder: A Chunk-Argument Bi-Encoder Framework for Chinese Geographic Re-Ranking
Chinese geographic re-ranking task aims to find the most relevant addresses among retrieved candidates, which is crucial for location-related services such as navigation maps.
Sparse Modular Activation for Efficient Sequence Modeling
renll/seqboat
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NeurIPS 2023
To validate the effectiveness of SMA on sequence modeling, we design a novel neural architecture, SeqBoat, which employs SMA to sparsely activate a Gated Attention Unit (GAU) based on the state representations learned from an SSM.
Recurrent Attention Networks for Long-text Modeling
4ai/ran
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Revisiting the self-attention mechanism and the recurrent structure, this paper proposes a novel long-document encoding model, Recurrent Attention Network (RAN), to enable the recurrent operation of self-attention.
Open Information Extraction via Chunks
Accordingly, we propose a simple BERT-based model for sentence chunking, and propose Chunk-OIE for tuple extraction on top of SaC.
Experiential Explanations for Reinforcement Learning
amal994/experiential-explanations-rl
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However, RL agents discard the qualitative features of their training, making it difficult to recover user-understandable information for "why" an action is chosen.
ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths
Sequential data naturally have different lengths in many domains, with some very long sequences.
