A current problem in NLP is massaging and processing low-resource languages which lack
useful training attributes such as supervised data, number of native speakers or experts, etc …

We describe and evaluate a character-level tagger for language-independent Named Entity
Recognition (NER). Instead of words, a sentence is represented as a sequence of …

Natural language processing (NLP) technologies and applications in legal text processing
are gaining momentum. Being one of the most prominent tasks in NLP, named-entity …

We study the effectiveness of Feature Density (FD) using different linguistically-backed
feature preprocessing methods in order to estimate dataset complexity, which in turn is used …

Sažetak Language model pre-training architectures have demonstrated to be useful to learn
language representations. bidirectional encoder representations from transformers (BERT) …

Named entity recognition aims to detect pre-determined entity types in unstructured text.
There is a limited number of studies on this task for low-resource languages such as Turkish …

In this article, we propose a word embedding--based named entity recognition (NER)
approach. NER is commonly approached as a sequence labeling task with the application of …

The chapter on linguistics and technology is written by Professor Niladri Sekhar Dash of the
Indian Statistical Institute in India. In the chapter 'Corpus Linguistics and Language …

Named entity recognition (NER) is an extensively studied task that extracts and classifies
named entities in a text. NER is crucial not only in downstream language processing …

Named Entity Recognition (NER) is an important task in Natural Language Processing, Data
Mining and Information Extraction areas since 1990's. While NER is a succesfully solved …