# Modeling Noisiness to Recognize Named Entities using Multitask Neural Networks on Social Media

## TL;DR

This paper introduces two multitask neural network models that effectively recognize named entities in noisy social media text by leveraging character-level features and transfer learning, outperforming previous methods.

## Contribution

The paper proposes novel multitask BLSTM-CRF models that improve named entity recognition in noisy social media data, addressing domain-specific challenges.

## Key findings

- Outperform state-of-the-art F1 scores by 2.45% and 3.69%.
- Effectively handle noisy social media text.
- Utilize character-level phonetics, phonology, and POS tags.

## Abstract

Recognizing named entities in a document is a key task in many NLP applications. Although current state-of-the-art approaches to this task reach a high performance on clean text (e.g. newswire genres), those algorithms dramatically degrade when they are moved to noisy environments such as social media domains. We present two systems that address the challenges of processing social media data using character-level phonetics and phonology, word embeddings, and Part-of-Speech tags as features. The first model is a multitask end-to-end Bidirectional Long Short-Term Memory (BLSTM)-Conditional Random Field (CRF) network whose output layer contains two CRF classifiers. The second model uses a multitask BLSTM network as feature extractor that transfers the learning to a CRF classifier for the final prediction. Our systems outperform the current F1 scores of the state of the art on the Workshop on Noisy User-generated Text 2017 dataset by 2.45% and 3.69%, establishing a more suitable approach for social media environments.