What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Natural language processing
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 most cited work include:
- Sentiment Analysis using Support Vector Machines with Diverse Information Sources (547 citations)
- Introduction to the bio-entity recognition task at JNLPBA (419 citations)
- Change-point detection in time-series data by relative density-ratio estimation (272 citations)
What are the main themes of his work throughout his whole career to date?
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.
He most often published in these fields:
- Artificial intelligence (48.72%)
- Natural language processing (40.00%)
- Information retrieval (27.69%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (48.72%)
- Natural language processing (40.00%)
- Information retrieval (27.69%)
In recent papers he was focusing on the following fields of study:
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.
Between 2015 and 2021, his most popular works were:
- SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity (106 citations)
- Normalising Medical Concepts in Social Media Texts by Learning Semantic Representation (74 citations)
- Bidirectional LSTM for Named Entity Recognition in Twitter Messages (69 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Natural language processing
- Machine learning
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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