Most state-of-the-art named entity recognition (NER) systems rely on handcrafted features
and on the output of other NLP tasks such as part-of-speech (POS) tagging and text …

Modern approaches to Named Entity Recognition (NER) use neural networks (NN) to
automatically extract features from text and seamlessly integrate them with sequence …

ABSTRACT Named Entity Recognition (NER) is sub task of Information Extraction that
includes identification of named entities and classification of them into named entity classes …

Neural Networks are widely used for Named Entity Recognition due to their capability of
extracting features from texts automatically and integrating them with sequence taggers …

In the context of Natural Language Processing, the Named Entity Recognition (NER) task
focuses on extracting and classifying named entities from free text, such as news, which …

Résumé Named Entity Recognition involves automatically identifying and classifying entities
such as persons, places, and organizations, and it is a very important task in Information …

Abstract Entropy Guided Transformation Learning (ETL) is a new machine learning strategy
that combines the advantages of decision trees (DT) and Transformation Based Learning …

Abstract Entropy Guided Transformation Learning (ETL) is a new machine learning strategy
that combines the advantages of Decision Trees (DT) and Transformation Based Learning …

This chapter details the entropy guided transformation learning algorithm [8, 23]. ETL is an
effective way to overcome the transformation based learning bottleneck: the construction of …

Identifying similar text passages plays an important role in many applications in NLP, such
as paraphrase generation, automatic summarization, etc. This paper presents some …