The task of Named Entity Recognition (NER) is to identify in text predefined units of information such as person names, organizations and locations. In this work, we address the problem of NER in Estonian using supervised learning approach. We explore common issues related to building a NER system such as the usage of language-agnostic and languagespecific features, the representation of named ...

We study a comprehensive set of features used in supervised named entity recognition. We explore various combinations of features and compare their impact on recognition performance. We build a conditional random field based system that achieves 91.02% F1-measure on the CoNLL 2003 (Sang and Meulder, 2003) dataset and 81.4% F1-measure on the OntoNotes version 4 (Hovy et al., 2006) CNN dataset, w...

Entries in microblogging sites are very short. For example, a ‘tweet’ (a post or status update on the popular microblogging site Twitter) can contain at most 140 characters. To comply with this restriction, users frequently use abbreviations to express their thoughts, thus producing sentences that are often poorly structured or ungrammatical. As a result, it becomes a challenge to come up with ...

Named Entity Recognition is an information extraction technique that identifies name entities in a text. Three popular methods have been conventionally used namely: rule-based, machine-learning-based and hybrid of them to extract named entities from a text. Machine-learning-based methods have good performance in the Persian language if they are trained with good features. To get good performanc...

In recent years much of the work in named entity recognition has been focused on tackling entities in different languages or domains. However the task of English named entity recognition still remains to be solved. In this paper we explore more ways to improve the English named entity recognition system beyond just distributional semantics and the use of external gazetteer. These ways include p...

Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present ...

It is often claimed that Named Ent i ty recognition systems need extensive gazetteers--lists of names of people, organisations, locations, and other named entities. Indeed, the compilation of such gazetteers is sometimes mentioned as a bottleneck in the design of Named Ent i ty recognition systems. We report on a Named Entity recognition system which combines rule-based grammars with statistica...