Named Entity Recognition (NER) models capable of Continual Learning (CL) are realistically valuable in areas where entity types continuously increase (e.g., personal assistants). Meanwhile the learning paradigm NER advances to new patterns such as span-based methods. However, its potential CL has not been fully explored. In this paper, we propose SpanKL, a simple yet effective Span-based model ...

In Natural Language Processing, named entity recognition (NER) is a task of (NLP) that tries to automatically identify and annotate Named Entities in text, such as people, places, organisations. We employ deep learning-based architecture this work solve the problem recognising entities Hindi text phrase. literature, approaches based on bidirectional long short-term memory (BiLSTM) have been uti...

Named entity recognition (NER) is an information extraction technique that aims to locate and classify named entities (e.g., organizations, locations, ...) within a document into predefined categories. Correctly identifying these phrases plays significant role in simplifying access. However, it remains difficult task because (NEs) have multiple forms they are context dependent. While the can be...

In this paper, we define the task of named entity recognition, look at existing systems for named entity recognition, and discuss the design, implementation, and evaluation of a system that performs named entity recognition on Filipino texts. We also compare the results of the system with an existing named entity recognizer designed for English texts using a Filipino corpus.

With the increasing popularity of microblogging services, new research challenges arise in the area of text processing. In this paper, we hypothesize that already existing services for Named Entity Recognition (NER), or a combination thereof, perform well on microposts, despite the fact that these NER services have been developed for processing long-form text documents that are well-structured ...

In this work we present a method for Named Entity Recognition (NER). Our method does not rely on complex linguistic resources, and apart from a hand coded system, we do not use any languagedependent tools. The only information we use is automatically extracted from the documents, without human intervention. Moreover, the method performs well even without the use of the hand coded system. The ex...