Named Entity Recognition in Twitter using Images and Text

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 with 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.Comment: The 3rd International Workshop on Natural Language Processing for Informal Text (NLPIT 2017), 8 page

Robustness to Capitalization Errors in Named Entity Recognition

Robustness to capitalization errors is a highly desirable characteristic of named entity recognizers, yet we find standard models for the task are surprisingly brittle to such noise. Existing methods to improve robustness to the noise completely discard given orthographic information, mwhich significantly degrades their performance on well-formed text. We propose a simple alternative approach based on data augmentation, which allows the model to \emph{learn} to utilize or ignore orthographic information depending on its usefulness in the context. It achieves competitive robustness to capitalization errors while making negligible compromise to its performance on well-formed text and significantly improving generalization power on noisy user-generated text. Our experiments clearly and consistently validate our claim across different types of machine learning models, languages, and dataset sizes.Comment: Accepted to EMNLP 2019 Workshop : W-NUT 2019 5th Workshop on Noisy User Generated Tex

Few-shot classification in Named Entity Recognition Task

For many natural language processing (NLP) tasks the amount of annotated data is limited. This urges a need to apply semi-supervised learning techniques, such as transfer learning or meta-learning. In this work we tackle Named Entity Recognition (NER) task using Prototypical Network - a metric learning technique. It learns intermediate representations of words which cluster well into named entity classes. This property of the model allows classifying words with extremely limited number of training examples, and can potentially be used as a zero-shot learning method. By coupling this technique with transfer learning we achieve well-performing classifiers trained on only 20 instances of a target class.Comment: In proceedings of the 34th ACM/SIGAPP Symposium on Applied Computin

Named Entity Recognition by Neural Prediction

International audienceNamed entity recognition (NER) remains a very challenging problem essentially when the document, where we perform it, is handwritten and ancient. Traditional methods using regular expressions or those based on syntactic rules, work but are not generic because they require, for each dataset, additional work of adaptation. We propose here a recognition method by context exploitation and tag prediction. We use a pipeline model composed of two consecutive BLSTMs (Bidirectional Long-Short Term Memory). The first one is a BLSTM-CTC coupling to recognize the words in a text line using a sliding window and HOG features. The second BLSTM serves as a language model. It cleverly exploits the gates of the BLSTM memory cell by deploying some syntactic rules in order to store the content around the proper nouns. This operation allows it to predict the tag of the next word, depending on its context, which is followed gradually until the discovery of the named entity (NE). All the words of the context are used to help the prediction. We have tested this system on a private dataset of Philharmonie de Paris, for the extraction of proper nouns within sale music transactions as well as on the public IAM dataset. The results are satisfactory, compared to what exists in the literature