Named-entity recognition

Named-entity recognition platforms and evaluation - GATE, OpenNLP, SpaCy, Transformers, Stanford NER, and NLTK are notable NER platforms - Precision, recall, and F1 score are commonly used measures for evaluating NER systems - State-of-the-art NER systems for English achieve near-human performance - The best system in MUC-7 scored 93.39% of F-measure - Human annotators scored 97.60% and 96.95% - NER systems have made significant advancements in accuracy and efficiency - Different types of errors and their importance need to be considered in evaluating NER systems Named entity types and hierarchies - BBN categories, Sekines extended hierarchy, and Freebase entity types are proposed for named entity types - BBN categories consist of 29 types and 64 subtypes - Sekines extended hierarchy includes 200 subtypes - Freebase entity types have been used for NER over social media text - Different hierarchies help organize and classify named entities in NER systems Approaches to Named Entity Recognition - NER systems use linguistic grammar-based techniques and statistical models like machine learning - Hand-crafted grammar-based systems have better precision but lower recall and require months of work - Statistical NER systems require a large amount of manually annotated training data - Semisupervised approaches have been suggested to reduce annotation effort - Conditional random fields are a typical choice for machine-learned NER Challenges, misconceptions, and research in Named Entity Recognition - Named-entity recognition is far from being solved despite high reported F1 numbers - Efforts are focused on reducing annotation labor through semi-supervised learning - Robust performance across domains is a key challenge - Scaling up to fine-grained entity types is another challenge - Crowdsourcing is a promising solution to obtain high-quality human judgments for NER - Researchers have compared NER performances from different statistical models and feature sets - Graph-based semi-supervised learning models have been proposed for language-specific NER tasks Applications and advances in Named Entity Recognition - NER has applications in question answering, information retrieval, and open-domain search queries - Chinese named entity recognition is relevant to language processing and intelligent information systems - NER has been applied to Twitter messages - Advances in NER include fine-grained recognition using conditional random fields and graph-based semi-supervised learning models