2013 - ACM Senior Member
Nigel Collier mainly investigates Artificial intelligence, Natural language processing, Information retrieval, The Internet and Data science. His biological study spans a wide range of topics, including Machine learning and Pattern recognition. His work on Semantic similarity as part of general Natural language processing study is frequently connected to Term, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Information retrieval research is multidisciplinary, incorporating perspectives in Named-entity recognition and Entity linking. He has researched The Internet in several fields, including Public health surveillance and Internet privacy. His research investigates the connection with Data science and areas like Infectious disease which intersect with concerns in Global health and Outbreak.
His primary areas of study are Artificial intelligence, Natural language processing, Information retrieval, Data science and World Wide Web. His studies deal with areas such as Machine learning and Named-entity recognition as well as Artificial intelligence. His Natural language processing research incorporates elements of Semantics, Recall, Word and Similarity.
Nigel Collier combines subjects such as Annotation and Text mining with his study of Information retrieval. The Data science study combines topics in areas such as Field, Outbreak, Biomedical text mining, Public health and Risk assessment. His study looks at the relationship between Public health and topics such as Infectious disease, which overlap with Global health.
Nigel Collier mainly focuses on Artificial intelligence, Natural language processing, Information retrieval, Word and Machine learning. His studies in Sentence, Language model, Machine translation, Representation and Taxonomy are all subfields of Artificial intelligence research. His Natural language processing research integrates issues from Similarity and Word embedding.
His research integrates issues of Lexical item, SNOMED CT, Named-entity recognition and Stance detection in his study of Information retrieval. His study in Named-entity recognition is interdisciplinary in nature, drawing from both Feature engineering, Visualization and Infectious disease. When carried out as part of a general Machine learning research project, his work on Feature and Deep learning is frequently linked to work in Curriculum, therefore connecting diverse disciplines of study.
His main research concerns Artificial intelligence, Natural language processing, Named-entity recognition, Word and Information retrieval. His work in the fields of Artificial intelligence, such as Taxonomy, intersects with other areas such as Geocoding. His Natural language processing study combines topics from a wide range of disciplines, such as Journalism and Fake news.
His Named-entity recognition research incorporates themes from Artificial neural network, Terminology, Orthographic projection and Semi-supervised learning. The study incorporates disciplines such as Sentence, Semantic similarity and Convolutional neural network in addition to Word. The various areas that Nigel Collier examines in his Information retrieval study include Feature engineering and Stance detection.
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Sentiment Analysis using Support Vector Machines with Diverse Information Sources
Tony Mullen;Nigel Collier.
empirical methods in natural language processing (2004)
Introduction to the bio-entity recognition task at JNLPBA
Jin-Dong Kim;Tomoko Ohta;Yoshimasa Tsuruoka;Yuka Tateisi.
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications (2004)
Change-point detection in time-series data by relative density-ratio estimation
Song Liu;Makoto Yamada;Nigel Collier;Masashi Sugiyama.
Neural Networks (2013)
Extracting the names of genes and gene products with a hidden Markov model
Nigel Collier;Chikashi Nobata;Jun-ichi Tsujii.
international conference on computational linguistics (2000)
BioCaster: detecting public health rumors with a Web-based text mining system
Nigel Collier;Son Doan;Ai Kawazoe;Reiko Matsuda Goodwin;Reiko Matsuda Goodwin.
On-line Trend Analysis with Topic Models: #twitter Trends Detection Topic Model Online
Jey Han Lau;Nigel Collier;Timothy Baldwin.
international conference on computational linguistics (2012)
Global mapping of infectious disease
Simon I. Hay;Katherine E. Battle;David M. Pigott;David L. Smith;David L. Smith.
Philosophical Transactions of the Royal Society B (2013)
Use of support vector machines in extended named entity recognition
Koichi Takeuchi;Nigel Collier.
international conference on computational linguistics (2002)
An Analysis of Twitter Messages in the 2011 Tohoku Earthquake
Son Doan;Bao-Khanh Ho Vo;Nigel Collier.
electronic healthcare (2011)
Bio-medical entity extraction using support vector machines
Koichi Takeuchi;Nigel Collier.
Artificial Intelligence in Medicine (2005)
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