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Nigel Collier
Researcher at University of Cambridge
Publications - 235
Citations - 8050
Nigel Collier is an academic researcher from University of Cambridge. The author has contributed to research in topics: Computer science & Ontology (information science). The author has an hindex of 42, co-authored 210 publications receiving 6825 citations. Previous affiliations of Nigel Collier include Tencent & Toshiba.
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Proceedings Article
Sentiment Analysis using Support Vector Machines with Diverse Information Sources
Tony Mullen,Nigel Collier +1 more
TL;DR: An approach to sentiment analysis which uses support vector machines (SVMs) to bring together diverse sources of potentially pertinent information, including several favorability measures for phrases and adjectives and, where available, knowledge of the topic of the text is introduced.
Proceedings ArticleDOI
Introduction to the bio-entity recognition task at JNLPBA
TL;DR: The JNLPBA shared task of bio-entity recognition using an extended version of the GENIA version 3 named entity corpus of MEDLINE abstracts is described and a general discussion of the approaches taken by participating systems is presented.
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Change-point detection in time-series data by relative density-ratio estimation
TL;DR: In this paper, the relative Pearson divergence is used as a divergence measure, and it is accurately and efficiently estimated by a method of direct density-ratio estimation, which can detect abrupt property changes lying behind time-series data.
Proceedings ArticleDOI
Extracting the names of genes and gene products with a hidden Markov model
TL;DR: A study into the use of a linear interpolating hidden Markov model (HMM) for the task of extracting technical terminology from MEDLINE abstracts and texts in the molecular-biology domain, the first stage in a system that will extract event information for automatically updating biology databases.
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BioCaster: detecting public health rumors with a Web-based text mining system
Nigel Collier,Son Doan,Ai Kawazoe,Reiko Matsuda Goodwin,Reiko Matsuda Goodwin,Mike Conway,Yoshio Tateno,Quoc Hung Ngo,Dinh Dien,Asanee Kawtrakul,Koichi Takeuchi,Mika Shigematsu,Kiyosu Taniguchi +12 more
TL;DR: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web and consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition.