Fine Grained Named Entity Recognition via Seq2seq Framework
نویسندگان
چکیده
منابع مشابه
Fine-grained Arabic named entity recognition
Named Entity Recognition (NER) is a Natural Language Processing (NLP) task, which aims to extract useful information from unstructured textual data by detecting and classifying Named Entity (NE) phrases into predefined semantic classes. This thesis addresses the problem of fine-grained NER for Arabic, which poses unique linguistic challenges to NER; such as the absence of capitalisation and sho...
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This paper describes the creation of a fine-grained named entity annotation scheme and corpus for Dutch, and experiments on automatic main type and subtype named entity recognition. We give an overview of existing named entity annotation schemes, and motivate our own, which describes six main types (persons, organizations, locations, products, events and miscellaneous named entities) and finer-...
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Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER systems are restricted to produce labels from to a small set of entity classes, e.g., person, organization, location or miscellaneous. In order to intelligently understand text and extract a wide range of information, it is useful to more prec...
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Fine-grained named entity classi cation or FG-NEC refers to the process of classifying a set of named entities from naturally occurring texts to the maximum granularity. It is essentially di erent from the traditional coarse-grained NEC (PER, LOC, ORG) in that it requires deep semantic analysis and the FG semantic classes are highly ambiguous. While research has been conducted in an application...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2980431