# Detecting Potential Topics In News Using BERT, CRF and Wikipedia

arXiv: https://arxiv.org/abs/2002.11402 · HTML: https://mlsanity.com/paper/2002.11402/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2020.

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

- [Multilinguals at SemEval-2022 Task 11: Complex NER in Semantically Ambiguous Settings for Low Resource Languages](https://mlsanity.com/paper/2207.06882/) (2022)

## Abstract (arXiv)

For a news content distribution platform like Dailyhunt, Named Entity Recognition is a pivotal task for building better user recommendation and notification algorithms. Apart from identifying names, locations, organisations from the news for 13+ Indian languages and use them in algorithms, we also need to identify n-grams which do not necessarily fit in the definition of Named-Entity, yet they are important. For example, "me too movement", "beef ban", "alwar mob lynching". In this exercise, given an English language text, we are trying to detect case-less n-grams which convey important information and can be used as topics and/or hashtags for a news. Model is built using Wikipedia titles data, private English news corpus and BERT-Multilingual pre-trained model, Bi-GRU and CRF architecture. It shows promising results when compared with industry best Flair, Spacy and Stanford-caseless-NER in terms of F1 and especially Recall.