# A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding

arXiv: https://arxiv.org/abs/1511.00215 · HTML: https://mlsanity.com/paper/1511.00215/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2015.

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

- [Are Word Embedding Methods Stable and Should We Care About It?](https://mlsanity.com/paper/2104.08433/) (2021)
- [GWPT: A Green Word-Embedding-based POS Tagger](https://mlsanity.com/paper/2401.07475/) (2024)
- [Automatic Posology Structuration : What role for LLMs?](https://mlsanity.com/paper/2506.19525/) (2025)

## Abstract (arXiv)

Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for modeling and predicting sequential data, e.g. speech utterances or handwritten documents. In this study, we propose to use BLSTM-RNN for a unified tagging solution that can be applied to various tagging tasks including part-of-speech tagging, chunking and named entity recognition. Instead of exploiting specific features carefully optimized for each task, our solution only uses one set of task-independent features and internal representations learnt from unlabeled text for all tasks.Requiring no task specific knowledge or sophisticated feature engineering, our approach gets nearly state-of-the-art performance in all these three tagging tasks.