Why pay more? A simple and efficient named entity recognition system for tweets

Abstract

The current paper investigates the problem of multimodal named entity recognition from Twitter data. Named entity recognition (NER) is an important task in natural language processing and has been carefully studied in recent decades. NER from tweets is particularly challenging because of 1) tweets are limited in length, 2) contains noisy text, and 3) contains hashtags. Moreover often tweets are associated with images and hyperlinks. Existing works on tweet-NER mostly concentrate on multimodal deep learning based models neglecting the use of hand-crafted features and usage of hyperlinks. The current paper investigates the incorporation of hand-crafted features extracted from different modalities like images, hyperlinks while extracting named entities from tweet-text. A large set of hand-crafted features are extracted from different modalities (images, hyperlinks) and those are added with the features extracted by a hybrid deep-neural model, bi-directional LSTM and CNN, followed by a conditional random field to perform this task. Several variants of these models in association with different hand-crafted feature sets are designed. Extensive experimentations on a multimodal Twitter data (containing text, images and urls) illustrate that character level hand-crafted features significantly improve the performance of the systems. In a part of the paper, results of the proposed models are also shown on a standard NER dataset, CoNLL 2003 dataset.