# BTPK-based interpretable method for NER tasks based on Talmudic Public Announcement Logic

arXiv: https://arxiv.org/abs/2201.09523 · HTML: https://mlsanity.com/paper/2201.09523/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 12 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition](https://mlsanity.com/paper/2004.07493/) (2020)
- [Interpretable Multi-dataset Evaluation for Named Entity Recognition](https://mlsanity.com/paper/2011.06854/) (2020)
- [Interpretability Analysis for Named Entity Recognition to Understand System Predictions and How They Can Improve](https://mlsanity.com/paper/2004.04564/) (2020)
- [Learning with Interpretable Structure from Gated RNN](https://mlsanity.com/paper/1810.10708/) (2018)
- [Increasing the Interpretability of Recurrent Neural Networks Using Hidden Markov Models](https://mlsanity.com/paper/1606.05320/) (2016)
- [Interpretable Recurrent Neural Networks Using Sequential Sparse Recovery](https://mlsanity.com/paper/1611.07252/) (2016)

## Abstract (arXiv)

As one of the basic tasks in natural language processing (NLP), named entity recognition (NER) is an important basic tool for downstream tasks of NLP, such as information extraction, syntactic analysis, machine translation and so on. The internal operation logic of current name entity recognition model is black-box to the user, so the user has no basis to determine which name entity makes more sense. Therefore, a user-friendly explainable recognition process would be very useful for many people. In this paper, we propose a novel interpretable method, BTPK (Binary Talmudic Public Announcement Logic model), to help users understand the internal recognition logic of the name entity recognition tasks based on Talmudic Public Announcement Logic. BTPK model can also capture the semantic information in the input sentences, that is, the context dependency of the sentence. We observed the public announcement of BTPK presents the inner decision logic of BRNNs, and the explanations obtained from a BTPK model show us how BRNNs essentially handle NER tasks.