# Multilingual Name Entity Recognition and Intent Classification Employing Deep Learning Architectures

arXiv: https://arxiv.org/abs/2211.02415 · HTML: https://mlsanity.com/paper/2211.02415/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 53 references that are in the map.
- First posted on arXiv 4 Nov 2022.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017)
- [Text and Code Embeddings by Contrastive Pre-Training](https://mlsanity.com/paper/2201.10005/) (2022)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Semi-supervised Sequence Learning](https://mlsanity.com/paper/1511.01432/) (2015)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling](https://mlsanity.com/paper/1609.01454/) (2016)
- [TENER: Adapting Transformer Encoder for Named Entity Recognition](https://mlsanity.com/paper/1911.04474/) (2019)
- [A Co-Interactive Transformer for Joint Slot Filling and Intent Detection](https://mlsanity.com/paper/2010.03880/) (2020)
- [FLERT: Document-Level Features for Named Entity Recognition](https://mlsanity.com/paper/2011.06993/) (2020)
- [GREEK-BERT: The Greeks visiting Sesame Street](https://mlsanity.com/paper/2008.12014/) (2020)
- [Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling](https://mlsanity.com/paper/1601.01530/) (2016)
- [A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts](https://mlsanity.com/paper/2001.05326/) (2020)
- [MT-BioNER: Multi-task Learning for Biomedical Named Entity Recognition using Deep Bidirectional Transformers](https://mlsanity.com/paper/2001.08904/) (2020)
- [Enriched Pre-trained Transformers for Joint Slot Filling and Intent Detection](https://mlsanity.com/paper/2004.14848/) (2020)

## Built on by (most cited first, 1 of 1)

- [A Systematic Survey of Natural Language Processing for the Greek Language](https://mlsanity.com/paper/2407.09861/) (2024)

## Abstract (arXiv)

Named Entity Recognition and Intent Classification are among the most important subfields of the field of Natural Language Processing. Recent research has lead to the development of faster, more sophisticated and efficient models to tackle the problems posed by those two tasks. In this work we explore the effectiveness of two separate families of Deep Learning networks for those tasks: Bidirectional Long Short-Term networks and Transformer-based networks. The models were trained and tested on the ATIS benchmark dataset for both English and Greek languages. The purpose of this paper is to present a comparative study of the two groups of networks for both languages and showcase the results of our experiments. The models, being the current state-of-the-art, yielded impressive results and achieved high performance.