Drug name entity recognition focuses on identifying concepts appearing in the text that correspond to a chemical substance used in pharmacology for treatment, cure, prevention or diagnosis of diseases. This paper describes a system based on ontologies for identifying the chemical substances in biomedical text. The system achieves an F-1 measure of 0.529

Pronunciation-translated names (P-Names) bring more ambiguities to Chinese word segmentation and generic named entity recognition. As there are few annotated resources that can be used to develop a good P-Name extraction system, this paper presents a bootstrapping algorithm, called PN-Finder, to tackle this problem. Starting from a small set of P-Name characters and context cue-words, the algor...

This paper explores the possible role of named entities in an automatic indexing process, based on text in subtitles. This is done by analyzing entity types, name density and name frequencies in subtitles and metadata records from different TV programs. The name density in metadata records is much higher than the name density in subtitles, and named entities with high frequencies in the subtitl...

Gene and protein names follow few, if any, true naming conventions and are subject to great variation in different occurrences of the same name. This gives rise to two important problems in natural language processing. First, can one locate the names of genes or proteins in free text, and second, can one determine when two names denote the same gene or protein? The first of these problems is a ...

Entity Recognition (ER) is a key component of relation extraction systems and many other natural-language processing applications. Unfortunately, most ER are restricted to produce labels from small set entity classes, e.g., person, organization, location or miscellaneous. In order intelligently understand text extract wide range information, it useful more precisely determine the semantic class...

Named Entity Recognition (NER) is the process of automatically recognizing entity names such as person, organization, and date in a document. In this study, we focus on bank documents written Turkish propose CRF model to extract named entities. The main contribution study twofold: (i) domain-specific features law, regulation, reference which frequently appear documents; (ii) contribute NER rese...

Named entity (NE) recognition is an important task for many natural language applications, such as Internet search engines, document indexing, information extraction and machine translation. Moreover, in oriental languages (such as Chinese, Japanese and Korean), NE recognition is even more important because it significantly affects the performance of word segmentation, the most fundamental task...

Chemical Named Entity Recognition (NER) is the basic step for consequent information extraction tasks such as named entity resolution, drug-drug interaction discovery, extraction of the names of the molecules and their properties. Improvement in the performance of such systems may affects the quality of the subsequent tasks. Chemical text from which data for named entity recognition is extracte...

We describe and compare methods developed for the BioCreative IV chemical compound and drug name recognition (CHEMDNER) task. The presented conditional random fields (CRF)-based named entity recogniser employs a statistical model trained on domain-specific features, in addition to those typically used in biomedical NERs. In order to increase recall, two heuristics-based post-processing steps we...

BACKGROUND The chemical compound and drug name recognition plays an important role in chemical text mining, and it is the basis for automatic relation extraction and event identification in chemical information processing. So a high-performance named entity recognition system for chemical compound and drug names is necessary. METHODS We developed a CHEMDNER system based on mixed conditional r...