To extract medical clinical related entity mention from patient clinical records is an essential step in clinical research. Recently, many researchers employ neural architecture to tackle the similar task of clinical concept extraction or drug name recognition from English clinical records, and have got prominent progress. However, most previous systems on Chinese Clinical Named Entity Recognit...

Building a system to extract Arabic named entities is a complex task due to the ambiguity and structure of Arabic text. Previous approaches that have tackled the problem of Arabic named entity recognition relied heavily on Arabic parsers and taggers combined with a huge set of gazetteers and sometimes large training sets to solve the ambiguity problem. But while these approaches are applicable ...

The Named Entity Recognition Task is one of the most common steps used in natural language applications. Linked Data datasets have been presented as promising background knowledge for Named Entity Recognition algorithms due to the amount of data available and the high variety of knowledge domains they cover. However, the discovery of names in Linked Data datasets is still a costly task if we co...

In this paper, we present a biomedical name recognition system, called PowerBioNE. In order to deal with the special phenomena in the biomedical domain, various evidential features are proposed and integrated through a mutual information independence model (MIIM). In addition, a support vector machine (SVM) plus sigmoid is proposed to resolve the data sparseness problem in the MIIM. In this way...

This paper proposes a hybrid Chinese named entity recognition model based on multiple features. It differentiates from most of the previous approaches mainly as follows. Firstly, the proposed Hybrid Model integrates coarse particle feature (POS Model) with fine particle feature (Word Model), so that it can overcome the disadvantages of each other. Secondly, in order to reduce the searching spac...

The purpose of this research is to analyze the patterns of the product names used in Thai economic news and to find clues that could be used to identify the product names’ boundaries and their categories. It is found that the patterns of Thai product names are quite varied. Thirty two patterns are found in this study. While some clues like collocation and the context of names can be used for id...

We present a named entity recognition (NER) system for tagging fiction: LitNER. Relative to more traditional approaches, LitNER has two important properties: (1) it makes no use of handtagged data or gazetteers, instead it bootstraps a model from term clusters; and (2) it leverages multiple instances of the same name in a text. Our experiments show it to substantially outperform off-the-shelf s...

Methods for name matching, an important component to support downstream tasks such as entity linking and entity clustering, have focused on alphabetic languages, primarily English. In contrast, logogram languages such as Chinese remain untested. We evaluate methods for name matching in Chinese, including both string matching and learning approaches. Our approach, based on new representations fo...

This article presents a machine learning-based solution for automatic chemical and drug name recognition on scientific documents, which was applied in the BioCreative IV CHEMDNER task, namely in the chemical entity mention recognition (CEM) and the chemical document indexing (CDI) sub-tasks. The proposed approach applies conditional random fields with a rich feature set, including linguistic, o...