Abstract In this paper, we propose a novel way of improving named entity recognition (NER) in the Korean language using its language-specific features. While field NER has been studied extensively recent years, mechanism efficiently recognizing entities (NEs) hardly explored. This is because distinct linguistic properties that present challenges for modeling. Therefore, an annotation scheme cor...

Named entity recognition (NER) for identifying proper nouns in unstructured text is one of the most important and fundamental tasks natural language processing. However, despite widespread use NER models, they still require a large-scale labeled data set, which incurs heavy burden due to manual annotation. Domain adaptation promising solutions this problem, where rich from relevant source domai...

Abstract Named entity recognition (NER) is a fundamental task for natural language processing, which aims to detect mentions of real-world entities from text and classifying them into predefined types. Recently, research on overlapped discontinuous named has received increasing attention. However, we note that few studies have considered both entities. In this paper, proposed novel sequence-to-...

Chinese NER is a challenging task. As pictographs, characters contain latent glyph information, which often overlooked. In this paper, we propose the FGN, Fusion Glyph Network for NER. Except adding method may also add extra interactive information with fusion mechanism. The major innovations of FGN include: (1) novel CNN structure called CGS-CNN proposed to capture both and between glyphs from...

Named entity recognition (NER) is a subfield of natural language processing (NLP). It able to identify proper nouns, such as person names, locations, and organizations, has been widely used in various tasks. NER can be practical extracting information from social media data. However, the unstructured noisy nature (such grammatical errors typos) causes new challenges for NER, especially low-reso...

Due to the rapid development of internet technology, social media and popular research article databases have generated many open text information. This large amount textual information leads 'Big Data'. Textual can be recorded repeatedly about an event or topic on different websites. Text summarization (TS) is emerging field that helps produce summary from a single multiple documents. The redu...

Named entities (NE) are phrases that contain the names of persons, organizations, locations, times, quantities, email, phone number, etc., in a document. Entity Recognition (NER) is fundamental task useful many applications, especially information extraction and question answering. Shared tasks on NER provides several reference datasets languages. In 2016 2018 editions VLSP workshop series, hav...