Named Entity Recognition on Noisy Data using Images and Text (1-page abstract)

TL;DR

This paper introduces a novel multi-level architecture for Named Entity Recognition on noisy Twitter data that leverages both images and text features, avoiding reliance on hand-crafted features or domain-specific rules.

Contribution

The proposed approach uniquely combines image and text features for NER, demonstrating competitive results without using linguistic resources or encoded rules.

Findings

Achieved 0.59 F-measure on Ritter dataset

Outperformed traditional methods on noisy Twitter data

Showed potential for improved NER in complex contexts

Abstract

Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities. Despite recent achievements, we still face limitations in correctly detecting and classifying entities, prominently in short and noisy text, such as Twitter. An important negative aspect in most of NER approaches is the high dependency on hand-crafted features and domain-specific knowledge, necessary to achieve state-of-the-art results. Thus, devising models to deal with such linguistically complex contexts is still challenging. In this paper, we propose a novel multi-level architecture that does not rely on any specific linguistic resource or encoded rule. Unlike traditional approaches, we use features extracted from images and text to classify named entities. Experimental tests against state-of-the-art NER for Twitter on the Ritter dataset present competitive results (0.59 F-measure), indicating that this approach may lead towards better NER models.