With a recent quick development of a molecular biology domain it becomes indispensable to promote different resources as databases and ontologies that represent the formal knowledge of the domain. As these resources have to be permanently updated, due to a constant appearance of new data, the Information Extraction (IE) methods become very useful. Named Entity Recognition (NER), that is considered to be the easiest task of IE, still remains very challenging in molecular biology domain because of the special phenomena of biomedical entities. In this paper we present our Hidden Markov Model (HMM)-based biomedical NER system that takes into account only parts-of-speech as an additional feature, which are used both to tackle the problem of nonuniform distribution among biomedical entity classes and to provide the system with an additional information about entity boundaries. Our system, in spite of its poor knowledge, has proved to obtain better results than some of the state-of-the-art systems that employ a greater number of features.

/pdf/biomedical-named-entity-recognition-a-poor-knowledge-hmm-1q6ah8pdmv.pdf

Biomedical named entity recognition: a poor knowledge HMM-based approach

Biomedical named entity recognition (BioNER) is one subtask of named entity recognition (NER) research. Although there are a number of named entity recognition systems, they can not obtain good performances extended to biomedical subfield. BioNER becomes a challenging work. We employ a skip-chain conditional random fields (CRFs) model for BioNER. This model completely considers to the long-range dependencies about biomedical information. These distant dependencies are powerful to identify some frequent appearing named entities and to classify them, especially for both classes protein and cell type. When we test the GENIA corpus, our approach obtains significant improvement over other methods, which achieves precision, recall and F-score of 72.8%, 73.6% and 73.2%, respectively.

Biomedical Named Entity Recognition Based on Skip-Chain CRFS

The purpose of extracting of Bio-Medical Entities is to recognize the particular entities, whether word or phrases, from the unstructured data contained in the text. This work proposes different approaches and methods, i.e. Machine Learning Hybrid Classification, Rule Based Non-tested Generalized Exemplars and Partial Decision Tree (PART) Learners for Bio-Medical Named Entity Recognition. The Prime objective is to consider, preferably, simple characteristics, such as, affixes and context. In addition, orthographic, Parts of Speech (POS) tags and N-grams are given secondary importance as for as their comparison with affixes and context is concerned. Further, for the very purpose of Bio-medical Diseased Named Recognition, proposal of Rule Based Classifiers along with the Statistical Machine Learning is given. Also, this paper proposes the blend of both preceding methods that jointly construct Hybrid Classification algorithm. Precision, Recall and F-measure – standard metrics- has been put into practice for the evaluation. The results prove that the technique used has far better performance results than the method used before - state-of-art Disease NER (Named Entity Recognition).

/pdf/bio-ner-biomedical-named-entity-recognition-using-rule-based-28stji7hq6.pdf

Bio-NER: Biomedical Named Entity Recognition using Rule-Based and Statistical Learners

Tamil Siddha medicine, an ancient medicinal system has yielded us a wide range of untapped information about traditional medicines. In this paper, we explore into the various Natural Language Processing techniques that can be implemented to this syntactically rich corpus. As domain information mostly concentrates on the central concepts, we start our work by identifying the Named Entities and categorizing them. An integrated NER classifier is built which comprises of SVM and Decision Tree classifier with an accuracy as high as 95%. These entities play different roles in different context. Hence their roles are labelled along with the predicates surrounding them. These roles and predicates give rise to a rule based sentence tagging system, trained by an MEM model, to tag different contents in this otherwise unstructured text. These two important techniques are then exploited to develop our Information Retrieval System that combines the methods category tagging done by Named Entity Recognition and content tagging done by Semantic Role Labelling. The system takes full advantage of the rich features of the language and hence can be expanded to other domains.

/pdf/content-based-information-retrieval-by-named-entity-3cqe6o0cfc.pdf

Content-based Information Retrieval by Named Entity Recognition and Verb Semantic Role Labelling

Valuable Information about tamil traditional medicines are available in various forms like books, magazines and websites These instructions are however very large and unstructured Our system focuses on constructing a NER identification module using SVM classifier to identify named entities and to classify them into their corresponding categories The two main categories considered are name of disorders and name of ingredients used The system uses features such as unigrams/bigrams, case markers, substring clues and tf-idf score to classify the entities into their classes These named entities are stored in the NE Dictionary based on their categories

https://ieeexplore.ieee.org/iel7/7045158/7054731/07055016.pdf

Named entity recognition for tamil biomedical documents

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 tags to produce a state of the art solution. The experimental results on the GENIA corpus achieved an F-score of 68.2% for semantic classification of 23 categories and achieved F-score of 78.1% on identification.

Biomedical named entity recognition system

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Biomedical named entity recognition system