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...

The cumulative number of publications, in particular in the life sciences, requires efficient methods for the automated extraction of information and semantic information retrieval. The recognition and identification of information-carrying units in text – concept denominations and named entities – relevant to a certain domain is a fundamental step. The focus of this thesis lies on the recognit...

Previous studies have shown that various biomedical subdomains have lexical, syntactic, semantic and discourse structure variations. It is essential to recognise such differences to understand that biomedical natural language processing tools, such as named entity recognisers, that work well on some subdomains may not work as well on others. In this paper, we investigate the pairwise similarity...

Named entity recognition is important in sophisticated information service system such as Question Answering and Text Mining since most of the answer type and text mining unit depend on the named entity type. Therefore we focus on named entity recognition model in Korean. Korean named entity recognition is difficult since each word of named entity has not specific features such as the capitaliz...

We describe the annotation of chemical named entities in scientific text. A set of annotation guidelines defines 5 types of named entities, and provides instructions for the resolution of special cases. A corpus of fulltext chemistry papers was annotated, with an inter-annotator agreement score of 93%. An investigation of named entity recognition using LingPipe suggests that scores of 63% are p...

The talk deals with different approaches used for Named Entity recognition and how they are used in developing a robust Named Entity Recognizer. The talk includes the development of tagset for NER and manual annotation of text.

Named Entity Recognition is always important when dealing with major Natural Language Processing tasks such as information extraction, question-answering, machine translation, document summarization etc so in this paper we put forward a survey of Named Entities in Indian Languages with particular reference to Assamese. There are various rule-based and machine learning approaches available for N...

Biomedical named entity recognition (BNER) has been actively studied over the years, and several BNER systems have become publicly available. In this study, we investigate the utility of a simple voting method called at-least-n voting to improve gene name recognition, which takes advantage of the availability of BNER systems in the domain. We found this voting scheme is effective in combining B...

We present two methods for learning the structure of personal names from unlabeled data. The first simply uses a few implicit constraints governing this structure to gain a toehold on the problem — e.g., descriptors come before first names, which come before middle names, etc. The second model also uses possible coreference information. We found that coreference constraints on names improve the...