We propose a machine learning approach, using a Maximum Entropy (ME) model to construct a Named Entity Recognition (NER) classifier to retrieve biomedical names from texts. In experiments, we utilize a blend of various linguistic features incorporated into the ME model to assign class labels and location within an entity sequence, and a postprocessing strategy for corrections to sequences of ta...

We apply a memory-based learner to the CoNLL-2002 shared task: language-independent named entity recognition. We use three additional techniques for improving the base performance of the learner: cascading, feature selection and system combination. The overall system is trained with two types of features: words and substrings of words which are relevant for this particular task. It is tested on...

This paper is about Named Entity Recognition (NER) for Telugu. Not much work has been done in NER for Indian languages in general and Telugu in particular. Adequate annotated corpora are not yet available in Telugu. We recognize that named entities are usually nouns. In this paper we therefore start with our experiments in building a CRF (Conditional Random Fields) based Noun Tagger. Trained on...

Unstructured data still makes up an important portion of the Web. One key task towards transforming this unstructured data into structured data is named entity recognition. We demo FOX, the Federated knOwledge eXtraction framework, a highly accurate open-source framework that implements RESTful web services for named entity recognition. Our framework achieves a higher Fmeasure than state-of-the...

The role of Internet in personal, economic and political advancement is growing in a fast pace. By the turn of century, data on web reaches to petabytes or exabytes or may even scale up-to unimaginable quantities. Extraction of precise and structured information from such large amounts of unstructured or semi-structured data is the major concern of web known as Information Extraction. Named ent...

Named entity recognition (NER) is used in many domains beyond the newswire text that comprises current gold-standard corpora. Recent work has used Wikipedia’s link structure to automatically generate near gold-standard annotations. Until now, these resources have only been evaluated on newswire corpora or themselves. We present the first NER evaluation on a Wikipedia gold standard (WG) corpus. ...

This paper is about Named Entity Recognition (NER) for Telugu. Not much work has been done in NER for Indian languages in general and Telugu in particular. Adequate annotated corpora are not yet available in Telugu. We recognize that named entities are usually nouns. In this paper we therefore start with our experiments in building a CRF (Conditional Random Fields) based Noun Tagger. Trained on...

Named Entity Recognition is a process through which a program extracts proper nouns in texts and associates them with a proper tag. NER has made significant progress in European languages, but in Indian languages due to the lack of effort as well as proper resources, it remains a challenging task. Recognizing ambiguities and assigning the correct tags to the names is the main goal of NER. Thus ...

Named Entity Recognition is an important task but is still relatively new for Vietnamese. It is partly due to the lack of a large annotated corpus. In this paper, we present a systematic approach in building a named entity annotated corpus while at the same time building rules to recognize Vietnamese named entities. The resulting open source system achieves an F-measure of 83%, which is better ...