# Neural Models for Sequence Chunking

arXiv: https://arxiv.org/abs/1701.04027 · HTML: https://mlsanity.com/paper/1701.04027/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2017.

## Built on by (most cited first, 10 of 10)

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- [Few-shot Learning for Slot Tagging with Attentive Relational Network](https://mlsanity.com/paper/2103.02333/) (2021)
- [Provenance: A Light-weight Fact-checker for Retrieval Augmented LLM Generation Output](https://mlsanity.com/paper/2411.01022/) (2024)
- [Span-Oriented Information Extraction -- A Unifying Perspective on Information Extraction](https://mlsanity.com/paper/2403.15453/) (2024)
- [An empirical assessment of deep learning approaches to task-oriented dialog management](https://mlsanity.com/paper/2108.03478/) (2021)
- [Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling](https://mlsanity.com/paper/2307.07946/) (2023)
- [Low-Resource Named Entity Recognition Based on Multi-hop Dependency Trigger](https://mlsanity.com/paper/2109.07118/) (2021)

## Abstract (arXiv)

Many natural language understanding (NLU) tasks, such as shallow parsing (i.e., text chunking) and semantic slot filling, require the assignment of representative labels to the meaningful chunks in a sentence. Most of the current deep neural network (DNN) based methods consider these tasks as a sequence labeling problem, in which a word, rather than a chunk, is treated as the basic unit for labeling. These chunks are then inferred by the standard IOB (Inside-Outside-Beginning) labels. In this paper, we propose an alternative approach by investigating the use of DNN for sequence chunking, and propose three neural models so that each chunk can be treated as a complete unit for labeling. Experimental results show that the proposed neural sequence chunking models can achieve start-of-the-art performance on both the text chunking and slot filling tasks.