# Span-based Named Entity Recognition by Generating and Compressing Information

arXiv: https://arxiv.org/abs/2302.05392 · HTML: https://mlsanity.com/paper/2302.05392/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 10 Feb 2023.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Deep Variational Information Bottleneck](https://mlsanity.com/paper/1612.00410/) (2016)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](https://mlsanity.com/paper/1902.09476/) (2019)
- [SpanNER: Named Entity Re-/Recognition as Span Prediction](https://mlsanity.com/paper/2106.00641/) (2021)
- [Variational Information Bottleneck for Effective Low-Resource Fine-Tuning](https://mlsanity.com/paper/2106.05469/) (2021)
- [BottleSum: Unsupervised and Self-supervised Sentence Summarization using the Information Bottleneck Principle](https://mlsanity.com/paper/1909.07405/) (2019)
- [DialogVED: A Pre-trained Latent Variable Encoder-Decoder Model for Dialog Response Generation](https://mlsanity.com/paper/2204.13031/) (2022)
- [A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition](https://mlsanity.com/paper/2106.14373/) (2021)
- [Specializing Word Embeddings (for Parsing) by Information Bottleneck](https://mlsanity.com/paper/1910.00163/) (2019)
- [Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition](https://mlsanity.com/paper/2004.14514/) (2020)
- [Multi-Grained Named Entity Recognition](https://mlsanity.com/paper/1906.08449/) (2019)
- [Information bottleneck through variational glasses](https://mlsanity.com/paper/1912.00830/) (2019)
- [MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective](https://mlsanity.com/paper/2204.04391/) (2022)
- [Improving the Adversarial Robustness of NLP Models by Information Bottleneck](https://mlsanity.com/paper/2206.05511/) (2022)
- [Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering](https://mlsanity.com/paper/2004.03561/) (2020)
- [Variational Sequential Labelers for Semi-Supervised Learning](https://mlsanity.com/paper/1906.09535/) (2019)
- [Distantly Supervised Relation Extraction with Sentence Reconstruction and Knowledge Base Priors](https://mlsanity.com/paper/2104.08225/) (2021)
- [Principled Paraphrase Generation with Parallel Corpora](https://mlsanity.com/paper/2205.12213/) (2022)
- [Instance-Based Neural Dependency Parsing](https://mlsanity.com/paper/2109.13497/) (2021)
- [Interpretable Research Replication Prediction via Variational Contextual Consistency Sentence Masking](https://mlsanity.com/paper/2203.14474/) (2022)

## Built on by (most cited first, 3 of 3)

- [Neural Machine Translation of Clinical Text: An Empirical Investigation into Multilingual Pre-Trained Language Models and Transfer-Learning](https://mlsanity.com/paper/2312.07250/) (2023)
- [Enhancing Phrase Representation by Information Bottleneck Guided Text Diffusion Process for Keyphrase Extraction](https://mlsanity.com/paper/2308.08739/) (2023)
- [Named Entity Recognition Under Domain Shift via Metric Learning for Life Sciences](https://mlsanity.com/paper/2401.10472/) (2024)

## Abstract (arXiv)

The information bottleneck (IB) principle has been proven effective in various NLP applications. The existing work, however, only used either generative or information compression models to improve the performance of the target task. In this paper, we propose to combine the two types of IB models into one system to enhance Named Entity Recognition (NER). For one type of IB model, we incorporate two unsupervised generative components, span reconstruction and synonym generation, into a span-based NER system. The span reconstruction ensures that the contextualised span representation keeps the span information, while the synonym generation makes synonyms have similar representations even in different contexts. For the other type of IB model, we add a supervised IB layer that performs information compression into the system to preserve useful features for NER in the resulting span representations. Experiments on five different corpora indicate that jointly training both generative and information compression models can enhance the performance of the baseline span-based NER system. Our source code is publicly available at https://github.com/nguyennth/joint-ib-models.