Name entity recognition (NER) is an important subtask in natural language processing. Various NER systems have been developed in the last decade. They may target for different domains, employ different methodologies, work on different languages, detect different types of entities, and support different inputs and output formats. These conditions make it difficult for a user to select the right ...

Single character named entity (SCNE) is a name entity (NE) composed of one Chinese character, such as “ ” (zhong1, China) and “ ” e2,Russia . SCNE is very common in written Chinese text. However, due to the lack of in-depth research, SCNE is a major source of errors in named entity recognition (NER). This paper formulates the SCNE recognition within the sourcechannel model framework. Our experi...

Named Entity Recognition and Intent Classification are among the most important subfields of field Natural Language Processing. Recent research has lead to development faster, more sophisticated efficient models tackle problems posed by those two tasks. In this work we explore effectiveness separate families Deep Learning networks for tasks: Bidirectional Long Short-Term Transformer-based netwo...

Named entity recognition (NER) in biological sources, also called medical named (MNER), attempts to identify and categorize terminology electronic records. Deep neural networks have recently demonstrated substantial effectiveness MNER. However, Chinese MNER has issues that cannot use lexical information involve nested entities. To address these problems, we propose a model which can handle both...

The chemical name extraction has a great importance in the biomedical field. Named Entity Recognition is the subtask of information extraction that is used to identify named entities in the given data. There are various dictionary-based, rule-based and machine learning approaches available for Named Entity Recognition. Rule based techniques include hand written rules. In this paper an extensive...

Named Entity Recognition is an information extraction technique that identifies name entities in a text. Three popular methods have been conventionally used namely: rule-based, machine-learning-based and hybrid of them to extract named entities from a text. Machine-learning-based methods have good performance in the Persian language if they are trained with good features. To get good performanc...

Place names embedded in online natural language text present a useful source of geographic information. Despite this, many methods for the extraction place from use pre-trained models that were not explicitly designed this task. Our paper builds five custom-built Named Entity Recognition (NER) and evaluates them against three popular pre-built name extraction. The are evaluated using set manual...